5.5.9 Compressive Sensing, Compressive Imaging, Compressed Sensing, Compression, Reconstruction

Donoho, D.L.,
Compressed sensing,
IT(52), No. 4, 2006, pp. 1289-1306. BibRef 0600

He, L., Chen, H., Carin, L.,
Tree-Structured Compressive Sensing With Variational Bayesian Analysis,
SPLetters(17), No. 1, January 2010, pp. 233-236.
IEEE DOI 1001
BibRef

Romberg, J.K.[Justin K.],
Compressive Sensing By Random Convolution,
SIIMS(2), No. 4, 2009, pp. 1098-1128.
DOI Link 1002
compressive sensing; random matrices; L_1 regularization BibRef

Muise, R.[Robert],
Compressive Imaging: An Application,
SIIMS(2), No. 4, 2009, pp. 1255-1276.
DOI Link 1002
compressive imaging; persistent surveillance; image multiplexing Field of view imaging, field of regard imaging. Limit the area to process to improve times. BibRef

Han, B.[Bing], Wu, F.[Feng], Wu, D.P.[Da-Peng],
Image representation by compressive sensing for visual sensor networks,
JVCIR(21), No. 4, May 2010, pp. 325-333.
Elsevier DOI 1006
BibRef
Earlier:
Image representation by compressed sensing,
ICIP08(1344-1347).
IEEE DOI 0810
Image representation; Compressive sensing; Random sampling; Projection onto convex sets; Convex optimization; Image decomposition; Interpolation; Image reconstruction BibRef

Baraniuk, R.G., Candes, E., Elad, M., Ma, Y.,
Applications of Sparse Representation and Compressive Sensing,
PIEEE(98), No. 6, June 2010, pp. 906-909.
IEEE DOI 1006
BibRef

Baraniuk, R.G.[Richard G.], Cevher, V., Wakin, M.B.[Michael B.],
Low-Dimensional Models for Dimensionality Reduction and Signal Recovery: A Geometric Perspective,
PIEEE(98), No. 6, June 2010, pp. 959-971.
IEEE DOI 1006
BibRef

Duarte, M.F.[Marco F.], Davenport, M.A.[Mark A.], Wakin, M.B.[Michael B.], Laska, J.N.[Jason N.], Takhar, D.[Dharmpal], Kelly, K.F.[Kevin F.], Baraniuk, R.G.[Richard G.],
Multiscale Random Projections for Compressive Classification,
ICIP07(VI: 161-164).
IEEE DOI 0709
BibRef

Wakin, M.B., Laska, J.N., Duarte, M.F., Baron, D., Sarvotham, S., Takhar, D., Kelly, K.F., Baraniuk, R.G.,
An Architecture for Compressive Imaging,
ICIP06(1273-1276).
IEEE DOI 0610
BibRef

Duarte, M.F., Baraniuk, R.G.,
Kronecker Compressive Sensing,
IP(21), No. 2, February 2012, pp. 494-504.
IEEE DOI 1201
BibRef

Elad, M., Figueiredo, M.A.T., Ma, Y.,
On the Role of Sparse and Redundant Representations in Image Processing,
PIEEE(98), No. 6, June 2010, pp. 972-982.
IEEE DOI 1006
BibRef

Fadili, M.J., Starck, J.L., Bobin, J., Moudden, Y.,
Image Decomposition and Separation Using Sparse Representations: An Overview,
PIEEE(98), No. 6, June 2010, pp. 983-994.
IEEE DOI 1006
BibRef

Jacques, L.[Laurent], Hammond, D.K.[David Kenric], Fadili, M.J.[M. Jalal],
Weighted fidelity in non-uniformly quantized compressed sensing,
ICIP11(1921-1924).
IEEE DOI 1201
BibRef
Earlier:
De-Quantizing Compressed Sensing with non-Gaussian constraints,
ICIP09(1465-1468).
IEEE DOI 0911
BibRef

Bajwa, W.U., Haupt, J., Sayeed, A.M., Nowak, R.,
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels,
PIEEE(98), No. 6, June 2010, pp. 1058-1076.
IEEE DOI 1006
BibRef

Yang, A.Y., Gastpar, M., Bajcsy, R., Sastry, S.S.,
Distributed Sensor Perception via Sparse Representation,
PIEEE(98), No. 6, June 2010, pp. 1077-1088.
IEEE DOI 1006
BibRef

Robucci, R., Gray, J.D., Chiu, L.K., Romberg, J.K., Hasler, P.,
Compressive Sensing on a CMOS Separable-Transform Image Sensor,
PIEEE(98), No. 6, June 2010, pp. 1089-1101.
IEEE DOI 1006
BibRef

Yu, L., Barbot, J.P., Zheng, G., Sun, H.,
Compressive Sensing With Chaotic Sequence,
SPLetters(17), No. 8, August 2010, pp. 731-734.
IEEE DOI 1007
BibRef

Fannjiang, A.C.[Albert C.], Strohmer, T.[Thomas], Yan, P.C.[Peng-Chong],
Compressed Remote Sensing Of Sparse Objects,
SIIMS(3), No. 3, 2010, pp. 595-618.
DOI Link compressed sensing; incoherence; threshold aperture; Rayleigh resolution; random sensor array BibRef 1000

Edwards, J.,
Focus on Compressive Sensing,
SPMag(28), No. 2, 2011, pp. 11-13.
IEEE DOI 1103
Special Reports. Brief survey. BibRef

Ashok, A.[Amit], Neifeld, M.A.[Mark A.],
Compressive imaging: hybrid measurement basis design,
JOSA-A(28), No. 6, June 2011, pp. 1041-1050.
DOI Link 1101
BibRef

Ni, K.Y.[Kang-Yu], Datta, S.[Somantika], Mahanti, P.[Prasun], Roudenko, S.[Svetlana], Cochran, D.[Douglas],
Efficient Deterministic Compressed Sensing for Images with Chirps and Reed-Muller Codes,
SIIMS(4), No. 3, 2011, pp. 931-953.
WWW Link. 1110
BibRef

Li, B.[Bo], Shen, Y.[Yi], Li, J.[Jia],
Dictionaries Construction Using Alternating Projection Method in Compressive Sensing,
SPLetters(18), No. 11, November 2011, pp. 663-666.
IEEE DOI 1112
BibRef

Chen, W., Rodrigues, M.R.D., Wassell, I.J.,
On the Use of Unit-Norm Tight Frames to Improve the Average MSE Performance in Compressive Sensing Applications,
SPLetters(19), No. 1, January 2012, pp. 8-11.
IEEE DOI 1112
BibRef

Chen, W.[Wei], Wassell, I.J., Rodrigues, M.R.D.,
Dictionary Design for Distributed Compressive Sensing,
SPLetters(22), No. 1, January 2015, pp. 95-99.
IEEE DOI 1410
compressed sensing BibRef

Abou Saleh, A., Chan, W.Y., Alajaji, F.,
Compressed Sensing With Nonlinear Analog Mapping in a Noisy Environment,
SPLetters(19), No. 1, January 2012, pp. 39-42.
IEEE DOI 1112
BibRef

Yang, Z., Zhang, C., Xie, L.,
On Phase Transition of Compressed Sensing in the Complex Domain,
SPLetters(19), No. 1, January 2012, pp. 47-50.
IEEE DOI 1112
BibRef

Wu, X., Dong, W., Zhang, X., Shi, G.,
Model-Assisted Adaptive Recovery of Compressed Sensing with Imaging Applications,
IP(21), No. 2, February 2012, pp. 451-458.
IEEE DOI 1201
BibRef

Hong, S.[Seokbeom], Park, H.[Hosung], Shin, B.[Beomkyu], No, J.S.[Jong-Seon], Chung, H.[Habong],
A New Performance Measure Using k -Set Correlation for Compressed Sensing Matrices,
SPLetters(19), No. 3, March 2012, pp. 143-146.
IEEE DOI 1202
BibRef

Fannjiang, A.[Albert], Liao, W.J.[Wen-Jing],
Coherence Pattern-Guided Compressive Sensing with Unresolved Grids,
SIIMS(5), No. 1 2012, pp. 179.
DOI Link 1203
BibRef

Fu, C.J.[Chang-Jun], Ji, X.Y.[Xiang-Yang], Dai, Q.H.[Qiong-Hai],
Adaptive Compressed Sensing Recovery Utilizing the Property of Signal's Autocorrelations,
IP(21), No. 5, May 2012, pp. 2369-2378.
IEEE DOI 1204
BibRef

Deng, C.[Chao], Zhang, Y.L.[Yuan-Long], Mao, Y.F.[Yi-Feng], Fan, J.T.[Jing-Tao], Suo, J.L.[Jin-Li], Zhang, Z.L.[Zhi-Li], Dai, Q.H.[Qiong-Hai],
Sinusoidal Sampling Enhanced Compressive Camera for High Speed Imaging,
PAMI(43), No. 4, April 2021, pp. 1380-1393.
IEEE DOI 2103
Encoding, Image reconstruction, Cameras, Image coding, Frequency modulation, Frequency-domain analysis, high-speed video BibRef

Wang, L., Wu, X., Shi, G.,
Binned Progressive Quantization for Compressive Sensing,
IP(21), No. 6, June 2012, pp. 2980-2990.
IEEE DOI 1202
BibRef

Zou, J., Fu, Y., Xie, S.,
A Block Fixed Point Continuation Algorithm for Block-Sparse Reconstruction,
SPLetters(19), No. 6, June 2012, pp. 364-367.
IEEE DOI 1202
BibRef

Xiao, Y.H.[Yun-Hai], Song, H.N.[Hui-Na],
An Inexact Alternating Directions Algorithm for Constrained Total Variation Regularized Compressive Sensing Problems,
JMIV(44), No. 2, October 2012, pp. 114-127.
WWW Link. 1206
BibRef

Guo, W.H.[Wei-Hong], Yin, W.T.[Wo-Tao],
Edge Guided Reconstruction for Compressive Imaging,
SIIMS(5), No. 3 2012, pp. 809-834.
DOI Link 1208
BibRef

Sidiropoulos, N.D., Kyrillidis, A.,
Multi-Way Compressed Sensing for Sparse Low-Rank Tensors,
SPLetters(19), No. 11, November 2012, pp. 757-760.
IEEE DOI 1210
BibRef

Bourquard, A., Unser, M.,
Binary Compressed Imaging,
IP(22), No. 3, March 2013, pp. 1042-1055.
IEEE DOI 1302
BibRef

Zhang, X.[Xue], Wang, A.H.[An-Hong], Zeng, B.[Bing], Liu, L.[Lei], Liu, Z.[Zhuo],
Adaptive Block-Wise Compressive Image Sensing Based on Visual Perception,
IEICE(E96-D), No. 2, February 2013, pp. 383-386.
WWW Link. 1301
BibRef

Lee, H.K.[Hyung-Keuk], Oh, H.[Heeseok], Lee, S.H.[Sang-Hoon], Bovik, A.C.,
Visually Weighted Compressive Sensing: Measurement and Reconstruction,
IP(22), No. 4, April 2013, pp. 1444-1455.
IEEE DOI 1303
BibRef
Earlier: A1, A2, A3, Only:
A new block compressive sensing to control the number of measurements,
ICIP11(2713-2716).
IEEE DOI 1201
BibRef

Zhang, X.Y.[Xiao-Ya], Li, S.[Song],
Compressed Sensing via Dual Frame Based L_1-Analysis With Weibull Matrices,
SPLetters(20), No. 3, March 2013, pp. 265-268.
IEEE DOI 1303
BibRef

Liu, Y., Li, M., Pados, D.A.,
Motion-Aware Decoding of Compressed-Sensed Video,
CirSysVideo(23), No. 3, March 2013, pp. 438-444.
IEEE DOI 1303
BibRef

Liu, Y., Pados, D.A.,
Compressed-Sensed-Domain L1-PCA Video Surveillance,
MultMed(18), No. 3, March 2016, pp. 351-363.
IEEE DOI 1603
Computational complexity BibRef

Yu, X., Baek, S.J.,
Sufficient Conditions on Stable Recovery of Sparse Signals With Partial Support Information,
SPLetters(20), No. 5, May 2013, pp. 539-542.
IEEE DOI 1304
BibRef

Patel, V.M.[Vishal M.], Chellappa, R.[Rama],
Sparse Representations and Compressive Sensing for Imaging and Vision,
Springer2013. ISBN 978-1-4614-6380-1.

Yang, Z.L.[Zhi-Li], Jacob, M.,
Nonlocal Regularization of Inverse Problems: A Unified Variational Framework,
IP(22), No. 8, 2013, pp. 3192-3203.
IEEE DOI 1307
concave programming; compressive sensing; current schemes; robust distance metrics; Noise reduction; nonconvex; nonlocal means BibRef

Sun, B.[Biao], Chen, Q.[Qian], Xu, X.[Xinxin], He, Y.[Yun], Jiang, J.J.[Jian-Jun],
Permuted and Filtered Spectrum Compressive Sensing,
SPLetters(20), No. 7, 2013, pp. 685-688.
IEEE DOI OFDM modulation; Fourier coefficient; OFDM 1307
BibRef

Du, X.P.[Xin-Peng], Cheng, L.Z.[Li-Zhi], Liu, L.F.[Lu-Feng],
A Swarm Intelligence Algorithm for Joint Sparse Recovery,
SPLetters(20), No. 6, 2013, pp. 611-614.
IEEE DOI 1307
Gaussian processes; compressed sensing theory BibRef

Wu, X.F.[Xiao-Fu], Yang, Z.[Zhen],
Verification-Based Interval-Passing Algorithm for Compressed Sensing,
SPLetters(20), No. 10, 2013, pp. 933-936.
IEEE DOI 1309
iterative methods BibRef

Yu, N.Y., Zhao, N.,
Deterministic Construction of Real-Valued Ternary Sensing Matrices Using Optical Orthogonal Codes,
SPLetters(20), No. 11, 2013, pp. 1106-1109.
IEEE DOI 1310
Coherence BibRef

Shen, Y., Fang, J., Li, H.,
Exact Reconstruction Analysis of Log-Sum Minimization for Compressed Sensing,
SPLetters(20), No. 12, 2013, pp. 1223-1226.
IEEE DOI 1311
Compressed sensing BibRef

Krahmer, F., Ward, R.,
Stable and Robust Sampling Strategies for Compressive Imaging,
IP(23), No. 2, February 2014, pp. 612-622.
IEEE DOI 1402
Fourier transforms BibRef

Huang, T.Y.[Tian-Yao], Liu, Y.M.[Yi-Min], Meng, H.D.[Hua-Dong], Wang, X.[Xiqin],
Adaptive Compressed Sensing via Minimizing Cramer-Rao Bound,
SPLetters(21), No. 3, March 2014, pp. 270-274.
IEEE DOI 1403
adaptive signal processing BibRef

Foucart, S., Koslicki, D.,
Sparse Recovery by Means of Nonnegative Least Squares,
SPLetters(21), No. 4, April 2014, pp. 498-502.
IEEE DOI 1403
Compressed sensing BibRef

Foucart, S., Lecué, G.,
An IHT Algorithm for Sparse Recovery From Subexponential Measurements,
SPLetters(24), No. 9, September 2017, pp. 1280-1283.
IEEE DOI 1708
compressed sensing, minimisation, probability, IHT algorithm, classical restricted isometry property, independent subexponential random variables, L1-minimization, matrix, probability, subexponential measurements, uniform sparse recovery, Compressive sensing, restricted isometry property, sparse recovery, subexponential random variable BibRef

Mohades, M.M., Mohades, A., Tadaion, A.,
A Reed-Solomon Code Based Measurement Matrix with Small Coherence,
SPLetters(21), No. 7, July 2014, pp. 839-843.
IEEE DOI 1405
Coherence BibRef

Dong, W.S.[Wei-Sheng], Shi, G.M.[Guang-Ming], Li, X.[Xin], Ma, Y.[Yi], Huang, F.[Feng],
Compressive Sensing via Nonlocal Low-Rank Regularization,
IP(23), No. 8, August 2014, pp. 3618-3632.
IEEE DOI 1408
biomedical MRI BibRef

Dong, W.S.[Wei-Sheng], Shi, G.M.[Guang-Ming], Li, X.[Xin], Peng, K., Wu, J., Guo, Z.,
Color-Guided Depth Recovery via Joint Local Structural and Nonlocal Low-Rank Regularization,
MultMed(19), No. 2, February 2017, pp. 293-301.
IEEE DOI 1702
computational geometry BibRef

Dong, W.S.[Wei-Sheng], Shi, G.M.[Guang-Ming], Hu, X.C.[Xiao-Cheng], Ma, Y.[Yi],
Nonlocal Sparse and Low-Rank Regularization for Optical Flow Estimation,
IP(23), No. 10, October 2014, pp. 4527-4538.
IEEE DOI 1410
image sequences BibRef

Feng, J.M.[Joe-Mei], Krahmer, F.,
An RIP-Based Approach to Sigma-Delta Quantization for Compressed Sensing,
SPLetters(21), No. 11, November 2014, pp. 1351-1355.
IEEE DOI 1408
compressed sensing BibRef

Li, Y.[Yun], Sankaranarayanan, A.C.[Aswin C.], Xu, L.[Lina], Baraniuk, R.[Richard], Kelly, K.F.[Kevin F.],
Realization of hybrid compressive imaging strategies,
JOSA-A(31), No. 8, August 2014, pp. 1716-1720.
DOI Link 1408
Inverse problems; Computational imaging BibRef

Friedland, S., Li, Q.[Qun], Schonfeld, D.,
Compressive Sensing of Sparse Tensors,
IP(23), No. 10, October 2014, pp. 4438-4447.
IEEE DOI 1410
compressed sensing Compare with Kronecker compressive sensing and multiway compressive sensing. KC is better compression, this is faster. BibRef

Lu, Z.Q.[Zhen-Qi], Ying, R.D.[Ren-Dong], Jiang, S.X.[Sum-Xin], Liu, P.L.[Pei-Lin], Yu, W.X.[Wen-Xian],
Distributed Compressed Sensing off the Grid,
SPLetters(22), No. 1, January 2015, pp. 105-109.
IEEE DOI 1410
compressed sensing BibRef

Rousseau, S., Helbert, D., Carre, P., Blanc-Talon, J.,
Compressive Pattern Matching on Multispectral Data,
GeoRS(52), No. 12, December 2014, pp. 7581-7592.
IEEE DOI 1410
compressed sensing BibRef

Ma, J.J.[Jun-Jie], Yuan, X.J.[Xiao-Jun], Ping, L.[Li],
Turbo Compressed Sensing with Partial DFT Sensing Matrix,
SPLetters(22), No. 2, February 2015, pp. 158-161.
IEEE DOI 1410
compressed sensing BibRef

Ma, J.J.[Jun-Jie], Yuan, X.J.[Xiao-Jun], Ping, L.[Li],
On the Performance of Turbo Signal Recovery with Partial DFT Sensing Matrices,
SPLetters(22), No. 10, October 2015, pp. 1580-1584.
IEEE DOI 1506
compressed sensing BibRef

Cloninger, A., Czaja, W., Bai, R., Basser, P.,
Solving 2D Fredholm Integral from Incomplete Measurements Using Compressive Sensing,
SIIMS(7), No. 3, 2014, pp. 1775-1798.
DOI Link 1410
BibRef

Liu, H.X.[Hai-Xiao], Song, B.[Bin], Tian, F.[Fang], Qin, H.[Hao],
Joint Sampling Rate and Bit-Depth Optimization in Compressive Video Sampling,
MultMed(16), No. 6, October 2014, pp. 1549-1562.
IEEE DOI 1410
compressed sensing BibRef

Wu, Q.S.[Qi-Song], Zhang, Y.D., Amin, M.G., Himed, B.,
Multi-Task Bayesian Compressive Sensing Exploiting Intra-Task Dependency,
SPLetters(22), No. 4, April 2015, pp. 430-434.
IEEE DOI 1411
Bayes methods BibRef

Zhou, Z., Liu, K., Fang, J.,
Bayesian Compressive Sensing Using Normal Product Priors,
SPLetters(22), No. 5, May 2015, pp. 583-587.
IEEE DOI 1411
Approximation methods BibRef

Poli, L.[Lorenzo], Oliveri, G.[Giacomo], Ding, P.P.[Ping Ping], Moriyama, T.[Toshifumi], Massa, A.[Andrea],
Multifrequency Bayesian compressive sensing methods for microwave imaging,
JOSA-A(31), No. 11, November 2014, pp. 2415-2428.
DOI Link 1411
Inverse problems BibRef

Wei, K.,
Fast Iterative Hard Thresholding for Compressed Sensing,
SPLetters(22), No. 5, May 2015, pp. 593-597.
IEEE DOI 1411
Approximation algorithms BibRef

Zhang, J.C.[Jing-Chao], Fu, N.[Ning], Peng, X.Y.[Xi-Yuan],
Compressive Circulant Matrix Based Analog to Information Conversion,
SPLetters(21), No. 4, April 2014, pp. 428-431.
IEEE DOI 1403
compressed sensing BibRef

Nichols, J.M., Oh, A.K., Willett, R.M.,
Reducing Basis Mismatch in Harmonic Signal Recovery via Alternating Convex Search,
SPLetters(21), No. 8, August 2014, pp. 1007-1011.
IEEE DOI 1406
Compressed sensing BibRef

Chen, Z., Molina, R., Katsaggelos, A.K.,
Automated Recovery of Compressedly Observed Sparse Signals From Smooth Background,
SPLetters(21), No. 8, August 2014, pp. 1012-1016.
IEEE DOI 1406
Algorithm design and analysis BibRef

Goertz, N., Guo, C., Jung, A., Davies, M.E., Doblinger, G.,
Iterative Recovery of Dense Signals from Incomplete Measurements,
SPLetters(21), No. 9, September 2014, pp. 1059-1063.
IEEE DOI 1406
Compressed sensing BibRef

Li, F.[Fuwei], Fang, J.[Jun], Li, H.B.[Hong-Bin], Huang, L.[Lei],
Robust One-Bit Bayesian Compressed Sensing with Sign-Flip Errors,
SPLetters(22), No. 7, July 2015, pp. 857-861.
IEEE DOI 1412
Bayes methods BibRef

Lu, W.Z.[Wei-Zhi], Li, W.Y.[Wei-Yu], Kpalma, K., Ronsin, J.,
Compressed Sensing Performance of Random Bernoulli Matrices with High Compression Ratio,
SPLetters(22), No. 8, August 2015, pp. 1074-1078.
IEEE DOI 1502
compressed sensing BibRef

Martin, G., Bioucas-Dias, J.M., Plaza, A.,
HYCA: A New Technique for Hyperspectral Compressive Sensing,
GeoRS(53), No. 5, May 2015, pp. 2819-2831.
IEEE DOI 1502
compressed sensing BibRef

Xu, S.C.[Song-Cen], de Lamare, R.C., Poor, H.V.,
Distributed Compressed Estimation Based on Compressive Sensing,
SPLetters(22), No. 9, September 2015, pp. 1311-1315.
IEEE DOI 1503
compressed sensing BibRef

Miller, T.G., Xu, S., de Lamare, R.C., Poor, H.V.,
Distributed Spectrum Estimation Based on Alternating Mixed Discrete-Continuous Adaptation,
SPLetters(23), No. 4, April 2016, pp. 551-555.
IEEE DOI 1604
Cost function BibRef

Jang, W.Y.[Woo-Yong], Ku, Z.[Zahyun], Urbas, A., Derov, J., Noyola, M.J.,
Plasmonic Superpixel Sensor for Compressive Spectral Sensing,
GeoRS(53), No. 6, June 2015, pp. 3471-3480.
IEEE DOI 1503
feature extraction BibRef

Schaeffer, H.[Hayden], Yang, Y.[Yi], Osher, S.J.[Stanley J.],
Space-Time Regularization for Video Decompression,
SIIMS(8), No. 1, 2015, pp. 373-402.
DOI Link 1503
From compressive sensing method. BibRef

Han, H.[Hong], Gan, L.[Lu], Liu, S.J.[San-Jun], Guo, Y.Y.[Yu-Yan],
A Novel Measurement Matrix Based on Regression Model for Block Compressed Sensing,
JMIV(51), No. 1, January 2015, pp. 161-170.
Springer DOI 1503
BibRef

Guo, J.[Jie], Song, B.[Bin], Tian, F.[Fang], Liu, H.X.[Hai-Xiao], Qin, H.[Hao],
Perception of Image Characteristics with Compressive Measurements,
IEICE(E97-D), No. 12, December 2014, pp. 3234-3235.
WWW Link. 1503
BibRef

Liu, F., Lin, L., Jiao, L., Li, L., Yang, S., Hou, B., Ma, H., Yang, L., Xu, J.,
Nonconvex Compressed Sensing by Nature-Inspired Optimization Algorithms,
Cyber(45), No. 5, May 2015, pp. 1028-1039.
IEEE DOI 1505
Algorithm design and analysis BibRef

Liu, J.X.[Ji-Xin], Li, X.F.[Xiao-Fei], Han, G.[Guang], Sun, N.[Ning], Du, K.[Kun], Sun, Q.S.[Quan-Sen],
Colour compressed sensing imaging via sparse difference and fractal minimisation recovery,
IET-IPR(9), No. 5, 2015, pp. 369-380.
DOI Link 1506
compressed sensing BibRef

Nagahara, M.,
Discrete Signal Reconstruction by Sum of Absolute Values,
SPLetters(22), No. 10, October 2015, pp. 1575-1579.
IEEE DOI 1506
compressed sensing BibRef

Saleh, A.A.[A. Abou], Alajaji, F., Chan, W.Y.[Wai-Yip],
Compressed Sensing with Non-Gaussian Noise and Partial Support Information,
SPLetters(22), No. 10, October 2015, pp. 1703-1707.
IEEE DOI 1506
Gaussian noise BibRef

Bi, D.J.[Dong-Jie], Xie, Y.L.[Yong-Le], Li, X.F.[Xi-Feng], Zheng, Y.R.,
A Sparsity Basis Selection Method for Compressed Sensing,
SPLetters(22), No. 10, October 2015, pp. 1738-1742.
IEEE DOI 1506
compressed sensing BibRef

Cambareri, V., Mangia, M., Pareschi, F., Rovatti, R., Setti, G.,
A Case Study in Low-Complexity ECG Signal Encoding: How Compressing is Compressed Sensing?,
SPLetters(22), No. 10, October 2015, pp. 1743-1747.
IEEE DOI 1506
compressed sensing BibRef

Zhu, S.Y.[Shu-Yuan], Zeng, B.[Bing], Gabbouj, M.[Moncef],
Adaptive sampling for compressed sensing based image compression,
JVCIR(30), No. 1, 2015, pp. 94-105.
Elsevier DOI 1507
Sparsity BibRef

Zhu, S.Y.[Shu-Yuan], Zeng, B.[Bing], Fang, L.[Lu], Gabbouj, M.[Moncef],
Downward spatially-scalable image reconstruction based on compressed sensing,
ICIP14(1352-1356)
IEEE DOI 1502
Compressed sensing BibRef

Qiao, H.[Heng], Pal, P.,
Generalized Nested Sampling for Compressing Low Rank Toeplitz Matrices,
SPLetters(22), No. 11, November 2015, pp. 1844-1848.
IEEE DOI 1509
Toeplitz matrices BibRef

Biswas, S., Achanta, H.K., Jacob, M., Dasgupta, S., Mudumbai, R.,
Recovery of Low Rank and Jointly Sparse Matrices with Two Sampling Matrices,
SPLetters(22), No. 11, November 2015, pp. 1945-1949.
IEEE DOI 1509
compressed sensing BibRef

Zhang, J.[Jun], Han, G.J.[Guo-Jun], Fang, Y.[Yi],
Deterministic Construction of Compressed Sensing Matrices from Protograph LDPC Codes,
SPLetters(22), No. 11, November 2015, pp. 1960-1964.
IEEE DOI 1509
binary codes BibRef

Wang, L.[Luhua], Zhang, J.[Jun],
BPDQ_p-Net: A Deep Unfolding Method for Quantized Compressed Sensing,
SPLetters(31), 2024, pp. 531-535.
IEEE DOI 2402
Quantization (signal), Distortion, Signal reconstruction, Signal processing algorithms, Compressed sensing, Transforms, quantized measurements BibRef

Jiang, H.[Hong], Huang, G.[Gang], Wilford, P.A.[Paul A.],
Noise analysis for lensless compressive imaging,
SP:IC(36), No. 1, 2015, pp. 70-82.
Elsevier DOI 1509
Lensless compressive imaging BibRef

Yuan, X., Huang, G.[Gang], Jiang, H.[Hong], Wilford, P.A.[Paul A.],
Block-wise lensless compressive camera,
ICIP17(31-35)
IEEE DOI 1803
Apertures, Cameras, Compressed sensing, Image coding, Image reconstruction, Real-time systems, Compressive sensing, sparse representation BibRef

Huang, G.[Gang], Jiang, H.[Hong], Matthews, K.[Kim], Wilford, P.A.[Paul A.],
Lensless imaging by compressive sensing,
ICIP13(2101-2105)
IEEE DOI 1402
Compressive sensing;imaging;lensless;sensor BibRef

Wang, X.[Xing], Liang, J.[Jie],
Approximate message passing-based compressed sensing reconstruction with generalized elastic net prior,
SP:IC(37), No. 1, 2015, pp. 19-33.
Elsevier DOI 1509
BibRef
And:
Multi-resolution compressed sensing reconstruction via approximate message passing,
ICIP15(4352-4356)
IEEE DOI 1512
Compressed sensing BibRef

Zhang, Y.L.[Yi-Long], Li, Y.H.[Yue-Hua], He, G.H.[Guan-Hua], Zhang, S.[Sheng],
A Compressive Regularization Imaging Algorithm for Millimeter-Wave SAIR,
IEICE(E98-D), No. 8, August 2015, pp. 1609-1612.
WWW Link. 1509
BibRef

Zhang, Y.L.[Yi-Long], Li, Y.[Yuehua], Safavi-Naeini, S.[Safieddin],
A Spectrum-Based Saliency Detection Algorithm for Millimeter-Wave InSAR Imaging with Sparse Sensing,
IEICE(E100-D), No. 1, February 2017, pp. 388-391.
WWW Link. 1702
BibRef

Warnell, G., Bhattacharya, S., Chellappa, R., Basar, T.,
Adaptive-Rate Compressive Sensing Using Side Information,
IP(24), No. 11, November 2015, pp. 3846-3857.
IEEE DOI 1509
adaptive signal processing BibRef

Li, S.J.[Shuang-Jiang], Qi, H.R.[Hai-Rong],
A Douglas-Rachford Splitting Approach to Compressed Sensing Image Recovery Using Low-Rank Regularization,
IP(24), No. 11, November 2015, pp. 4240-4249.
IEEE DOI 1509
compressed sensing BibRef

Sankaranarayanan, A.C.[Aswin C.], Xu, L.[Lina], Studer, C.[Christoph], Li, Y.[Yun], Kelly, K.F.[Kevin F.], Baraniuk, R.G.[Richard G.],
Video Compressive Sensing for Spatial Multiplexing Cameras Using Motion-Flow Models,
SIIMS(8), No. 3, 2015, pp. 1489-1518.
DOI Link 1511
BibRef
Earlier: A1, A3, A6, Only:
CS-MUVI: Video compressive sensing for spatial-multiplexing cameras,
ICCP12(1-10).
IEEE DOI 1208
BibRef

Holloway, J., Sankaranarayanan, A.C., Veeraraghavan, A., Tambe, S.,
Flutter Shutter Video Camera for compressive sensing of videos,
ICCP12(1-9).
IEEE DOI 1208
BibRef

Yuan, H., Song, H., Sun, X., Guo, K., Ju, Z.,
Compressive sensing measurement matrix construction based on improved size compatible array LDPC code,
IET-IPR(9), No. 11, 2015, pp. 993-1001.
DOI Link 1511
compressed sensing BibRef

Sudhakar, P.[Prasad], Jacques, L.[Laurent], Dubois, X.[Xavier], Antoine, P.[Philippe], Joannes, L.[Luc],
Compressive Imaging and Characterization of Sparse Light Deflection Maps,
SIIMS(8), No. 3, 2015, pp. 1824-1856.
DOI Link 1511
BibRef

Nicodème, M.[Marc], Turcu, F.[Flavius], Dossal, C.[Charles],
Optimal Dual Certificates for Noise Robustness Bounds in Compressive Sensing,
JMIV(53), No. 3, November 2015, pp. 251-263.
WWW Link. 1511
BibRef

Li, G.[Gang], Li, X.[Xiao], Li, S.[Sheng], Bai, H.[Huang], Jiang, Q.[Qianru], He, X.X.[Xiong-Xiong],
Designing robust sensing matrix for image compression,
IP(24), No. 12, December 2015, pp. 5389-5400.
IEEE DOI 1512
compressed sensing BibRef

Chang, K.[Kan], Li, B.X.[Bao-Xin],
Joint modeling and reconstruction of a compressively-sensed set of correlated images,
JVCIR(33), No. 1, 2015, pp. 286-300.
Elsevier DOI 1512
Compressive sensing BibRef

Romero, D., Ariananda, D., Tian, Z., Leus, G.,
Compressive Covariance Sensing: Structure-based compressive sensing beyond sparsity,
SPMag(33), No. 1, January 2016, pp. 78-93.
IEEE DOI 1601
BibRef

Lin, L.P.[Le-Ping], Liu, F.[Fang], Jiao, L.C.[Li-Cheng],
Geometric structure guided collaborative compressed sensing,
SP:IC(40), No. 1, 2016, pp. 16-25.
Elsevier DOI 1601
Geometric structure BibRef

Wang, Y.G.[Ying-Gui], Liu, Z.[Zheng], Yang, L.[Le], Jiang, W.L.[Wen-Li],
Generalized compressive detection of stochastic signals using Neyman-Pearson theorem,
SIViP(9), No. 1 Supp, December 2015, pp. 111-120.
Springer DOI 1601
BibRef

Sadeghigol, Z.[Zahra], Kahaei, M.H.[Mohammad Hossein], Haddadi, F.[Farzan],
Model based variational Bayesian compressive sensing using heavy tailed sparse prior,
SP:IC(41), No. 1, 2016, pp. 158-167.
Elsevier DOI 1602
Bayesian compressive sensing. based on generalized double Pareto. BibRef

Chang, K., Ding, P.L.K.[P. L. Kevin], Li, B.,
Compressive Sensing Reconstruction of Correlated Images Using Joint Regularization,
SPLetters(23), No. 4, April 2016, pp. 449-453.
IEEE DOI 1604
Compressed sensing BibRef

Sugimura, D.[Daisuke], Tomabechi, M.[Masaru], Hosaka, T.[Tadaaki], Hamamoto, T.[Takayuki],
Compressive multi-spectral imaging using self-correlations of images based on hierarchical joint sparsity models,
MVA(27), No. 4, May 2016, pp. 499-510.
Springer DOI 1605
BibRef

Liu, X.M.[Xian-Ming], Zhai, D.M.[De-Ming], Zhou, J.T.[Jian-Tao], Zhang, X.F.[Xin-Feng], Zhao, D.B.[De-Bin], Gao, W.[Wen],
Compressive Sampling-Based Image Coding for Resource-Deficient Visual Communication,
IP(25), No. 6, June 2016, pp. 2844-2855.
IEEE DOI 1605
Codecs BibRef

Eslahi, N.[Nasser], Aghagolzadeh, A.[Ali],
Compressive Sensing Image Restoration Using Adaptive Curvelet Thresholding and Nonlocal Sparse Regularization,
IP(25), No. 7, July 2016, pp. 3126-3140.
IEEE DOI 1606
compressed sensing BibRef

Eslahi, N., Foi, A.,
Anisotropic Spatiotemporal Regularization in Compressive Video Recovery by Adaptively Modeling the Residual Errors as Correlated Noise,
IVMSP18(1-5)
IEEE DOI 1809
Noise measurement, Spatiotemporal phenomena, Adaptation models, AWGN, Correlation, Transforms BibRef

Sasmal, P., Naidu, R.R., Sastry, C.S., Jampana, P.,
Composition of Binary Compressed Sensing Matrices,
SPLetters(23), No. 8, August 2016, pp. 1096-1100.
IEEE DOI 1608
compressed sensing BibRef

Naidu, R.R., Murthy, C.R.,
Construction of Binary Sensing Matrices Using Extremal Set Theory,
SPLetters(24), No. 2, February 2017, pp. 211-215.
IEEE DOI 1702
Gaussian processes BibRef

Guo, J., Song, B., Du, X.,
Significance Evaluation of Video Data Over Media Cloud Based on Compressed Sensing,
MultMed(18), No. 7, July 2016, pp. 1297-1304.
IEEE DOI 1608
cloud computing BibRef

Liu, S.C.[Sheng-Cai], Zhang, J.S.[Jiang-She], Liu, J.M.[Jun-Min], Yin, Q.Y.[Qing-Yan],
L1/2,1 group sparse regularization for compressive sensing,
SIViP(10), No. 5, May 2016, pp. 861-868.
WWW Link. 1608
BibRef

Ye, J.C.[Jong Chul],
Low-rank Fourier interpolation for compressed sensing imaging,
SPIE(Newsroom), July 27, 2016
DOI Link 1608
A novel annihilating filter-based low-rank Hankel matrix approach can be combined with classical analytic reconstruction techniques for application to several compressed sensing imaging problems. BibRef

Dziwoki, G.,
Averaged Properties of the Residual Error in Sparse Signal Reconstruction,
SPLetters(23), No. 9, September 2016, pp. 1170-1173.
IEEE DOI 1609
Gaussian processes BibRef

Zhang, L.Y.[Leo Yu], Wong, K.W.[Kwok-Wo], Zhang, Y.S.[Yu-Shu], Zhou, J.T.[Jian-Tao],
Bi-level Protected Compressive Sampling,
MultMed(18), No. 9, September 2016, pp. 1720-1732.
IEEE DOI 1609
compressed sensing BibRef

Sankaranarayanan, A.C., Herman, M.A., Turaga, P.K., Kelly, K.F.,
Enhanced Compressive Imaging Using Model-Based Acquisition: Smarter sampling by incorporating domain knowledge,
SPMag(33), No. 5, September 2016, pp. 81-94.
IEEE DOI 1610
compressed sensing BibRef

Feng, L.[Lei], Sun, H.J.[Huai-Jiang], Sun, Q.S.[Quan-Sen], Xia, G.Y.[Gui-Yu],
Image compressive sensing via Truncated Schatten-p Norm regularization,
SP:IC(47), No. 1, 2016, pp. 28-41.
Elsevier DOI 1610
Compressive sensing BibRef

Choi, J.,
Secure Transmissions via Compressive Sensing in Multicarrier Systems,
SPLetters(23), No. 10, October 2016, pp. 1315-1319.
IEEE DOI 1610
compressed sensing BibRef

Ahn, J.H.,
Compressive Sensing and Recovery for Binary Images,
IP(25), No. 10, October 2016, pp. 4796-4802.
IEEE DOI 1610
Hadamard matrices BibRef

Testa, M.[Matteo], Magli, E.[Enrico],
Compressive Estimation and Imaging Based on Autoregressive Models,
IP(25), No. 11, November 2016, pp. 5077-5087.
IEEE DOI 1610
autoregressive processes BibRef

Salwa, L.[Lagdali], Mohammed, R.[Rziza],
Novel phase-based descriptor using bispectrum for texture classification,
PRL(100), No. 1, 2017, pp. 1-5.
Elsevier DOI 1712
Bispectrum BibRef

Bai, H.[Huang], Li, S.[Sheng], He, X.X.[Xiong-Xiong],
Sensing Matrix Optimization Based on Equiangular Tight Frames With Consideration of Sparse Representation Error,
MultMed(18), No. 10, October 2016, pp. 2040-2053.
IEEE DOI 1610
compressed sensing BibRef

Canh, T.N.[Thuong Nguyen], Dinh, K.Q.[Khanh Quoc], Jeon, B.W.[Byeung-Woo],
Compressive sensing reconstruction via decomposition,
SP:IC(49), No. 1, 2016, pp. 63-78.
Elsevier DOI 1609
BibRef
Earlier: A2, A1, A3:
Compressive sensing of video with weighted sensing and measurement allocation,
ICIP15(2065-2069)
IEEE DOI 1512
Compressive sensing. Compressive sensing of video BibRef

Chien, T.V.[Trinh Van], Dinh, K.Q.[Khanh Quoc], Jeon, B.W.[Byeung-Woo], Burger, M.[Martin],
Block compressive sensing of image and video with nonlocal Lagrangian multiplier and patch-based sparse representation,
SP:IC(54), No. 1, 2017, pp. 93-106.
Elsevier DOI 1704
Block compressive sensing BibRef

Feng, L.[Lei], Sun, H.J.[Huai-Jiang], Sun, Q.S.[Quan-Sen], Xia, G.Y.[Gui-Yu],
Blind compressive sensing using block sparsity and nonlocal low-rank priors,
JVCIR(42), No. 1, 2017, pp. 37-45.
Elsevier DOI 1701
Blind compressive sensing BibRef

Choi, J.,
Successive Hypothesis Testing Based Sparse Signal Recovery and Its Application to MUD in Random Access,
SPLetters(24), No. 2, February 2017, pp. 166-170.
IEEE DOI 1702
compressed sensing BibRef

Baraniuk, R.G., Goldstein, T., Sankaranarayanan, A.C., Studer, C., Veeraraghavan, A., Wakin, M.B.,
Compressive Video Sensing: Algorithms, architectures, and applications,
SPMag(34), No. 1, January 2017, pp. 52-66.
IEEE DOI 1702
Survey, Compressive Sensing. compressed sensing BibRef

Wang, Y.G.[Ying-Gui], Yang, L.[Le], Tang, Z.Y.[Ze-Ying], Gao, Y.[Yong],
Multitask classification and reconstruction using extended Turbo approximate message passing,
SIViP(11), No. 2, February 2017, pp. 219-226.
Springer DOI 1702
BibRef

Tu, H.[Hao], Bu, W.H.[Wei-Hua], Wang, W.J.[Wen-Jing], Gao, B.X.[Bing-Xi], Feng, H.[Hui], Wu, S.[Shuai],
Applicability of Hadamard relaxation method to MMW and THz Imaging with compressive sensing,
SIViP(11), No. 3, March 2017, pp. 399-406.
Springer DOI 1702
BibRef

Hu, G.Q.[Gui-Qiang], Xiao, D.[Di], Wang, Y.[Yong], Xiang, T.[Tao],
An image coding scheme using parallel compressive sensing for simultaneous compression-encryption applications,
JVCIR(44), No. 1, 2017, pp. 116-127.
Elsevier DOI 1703
Compressive sensing BibRef

Unde, A.S.[Amit Satish], Deepthi, P.P.,
Block compressive sensing: Individual and joint reconstruction of correlated images,
JVCIR(44), No. 1, 2017, pp. 187-197.
Elsevier DOI 1703
Compressive sensing BibRef

Unde, A.S.[Amit Satish], Deepthi, P.P.,
Rate-distortion analysis of structured sensing matrices for block compressive sensing of images,
SP:IC(65), 2018, pp. 115-127.
Elsevier DOI 1805
Block compressive sensing, Rate-distortion performance, SRM, BPBD, Uniform quantization, Entropy coding BibRef

Xu, J.[Jin], Zhang, Y.[Yan], Fu, Z.Z.[Zhi-Zhong], Zhou, N.[Ning],
Perceptual Distributed Compressive Video Sensing via Reweighted Sampling and Rate-Distortion Optimized Measurements Allocation,
IEICE(E100-D), No. 4, April 2017, pp. 918-922.
WWW Link. 1704
BibRef

Trigano, T., Cohen, J.,
Intensity Estimation of Spectroscopic Signals With an Improved Sparse Reconstruction Algorithm,
SPLetters(24), No. 5, May 2017, pp. 530-534.
IEEE DOI 1704
compressed sensing BibRef

Wen, Z.D.[Zai-Dao], Hou, B.[Biao], Jiao, L.C.[Li-Cheng],
Joint Sparse Recovery With Semisupervised MUSIC,
SPLetters(24), No. 5, May 2017, pp. 629-633.
IEEE DOI 1704
MUSIC: multiple signal classification. compressed sensing BibRef

Song, X., Peng, X., Xu, J., Shi, G., Wu, F.,
Distributed Compressive Sensing for Cloud-Based Wireless Image Transmission,
MultMed(19), No. 6, June 2017, pp. 1351-1364.
IEEE DOI 1705
BibRef
Earlier:
Compressive sensing based image transmission with side information at the decoder,
VCIP15(1-4)
IEEE DOI 1605
Bandwidth, Correlation, Decoding, Image coding, Scalability, Signal to noise ratio, Silicon, Distributed compressive sensing (CS), graceful degradation (GD), image transmission, side, information, (SI) BibRef

Song, X., Peng, X., Xu, J., Shi, G., Wu, F.,
Unequal Error Protection for Scalable Video Storage in the Cloud,
MultMed(20), No. 3, March 2018, pp. 699-710.
IEEE DOI 1802
Bandwidth, Cloud computing, Maintenance engineering, Redundancy, Static VAr compensators, Streaming media, simulcast BibRef

Gholami, M., Alinia, M.,
Explicit APM-LDPC Codes With Girths 6, 8, and 10,
SPLetters(24), No. 6, June 2017, pp. 741-745.
IEEE DOI 1705
matrix algebra, parity check codes, affine permutation matrices, explicit APM-LDPC codes, exponent matrices, low-density parity-check codes, novel explicit constructions, Computers, Decoding, Hardware, Linear codes, Mathematics, Parity check codes, Simulation, Explicit constructions, girth, low-density, parity-check, codes, from, affine, permutation, matrices, (APM-LDPC, codes) BibRef

Ravazzi, C., Coluccia, G.[Giulio], Magli, E.[Enrico],
Curl-Constrained Gradient Estimation for Image Recovery From Highly Incomplete Spectral Data,
IP(26), No. 6, June 2017, pp. 2656-2668.
IEEE DOI 1705
data compression, image coding, l1-minimization methods, compressed Fourier measurements, compressed sensing problem, curl-constrained gradient estimation, gradient field, gradient-based methods, highly incomplete spectral data, least squares estimation, spectral coefficients, Estimation, Fourier transforms, Magnetic resonance imaging, Minimization, TV, Compressed sensing, BibRef

Bioglio, V.[Valerio], Coluccia, G.[Giulio], Magli, E.[Enrico],
Sparse image recovery using compressed sensing over finite alphabets,
ICIP14(1287-1291)
IEEE DOI 1502
Compressed sensing BibRef

Dinh, K.Q.[Khanh Quoc], Shim, H.J.[Hiuk Jae], Jeon, B.W.[Byeung-Woo],
Small-block sensing and larger-block recovery in block-based compressive sensing of images,
SP:IC(55), No. 1, 2017, pp. 10-22.
Elsevier DOI 1705
Compressive sensing BibRef

Dinh, K.Q.[Khanh Quoc], Jeon, B.W.[Byeung-Woo],
Iterative Weighted Recovery for Block-Based Compressive Sensing of Image/Video at a Low Subrate,
CirSysVideo(27), No. 11, November 2017, pp. 2294-2308.
IEEE DOI 1712
Algorithm design and analysis, Compressed sensing, Discrete cosine transforms, Image coding, Sensors, Videos, prior information BibRef

Geng, T., Sun, G., Xu, Y., He, J.,
Truncated Nuclear Norm Minimization Based Group Sparse Representation for Image Restoration,
SIIMS(11), No. 3, 2018, pp. 1878-1897.
DOI Link 1810
BibRef

Geng, T., Sun, G., Xu, Y., Li, Z.,
Image compressive sensing using group sparse representation via truncated nuclear norm minimization,
WSSIP17(1-5)
IEEE DOI 1707
Compressed sensing, Convergence, Convex functions, Dictionaries, Image processing, Minimization, Sparse matrices, Compressive sensing, dictionary learning, group sparse representation, truncated, nuclear, norm, minimization BibRef

Yan, B.[Bai], Zhao, Q.[Qi], Wang, Z.H.[Zhi-Hai], Zhao, X.Y.[Xin-Yuan],
A hybrid evolutionary algorithm for multiobjective sparse reconstruction,
SIViP(11), No. 6, September 2017, pp. 993-1000.
Springer DOI 1708
BibRef

Wang, L.Z.[Li-Zhi], Xiong, Z.W.[Zhi-Wei], Shi, G.M.[Guang-Ming], Wu, F.[Feng], Zeng, W.J.[Wen-Jun],
Adaptive Nonlocal Sparse Representation for Dual-Camera Compressive Hyperspectral Imaging,
PAMI(39), No. 10, October 2017, pp. 2104-2111.
IEEE DOI 1709
Hyperspectral imaging, Compressive sensing, dual-camera, hyperspectral imaging, nonlocal similarity, sparse, representation BibRef

Elzanaty, A.[Ahmed], Giorgetti, A.[Andrea], Chiani, M.[Marco],
Weak RIC Analysis of Finite Gaussian Matrices for Joint Sparse Recovery,
SPLetters(24), No. 10, October 2017, pp. 1473-1477.
IEEE DOI 1710
RIC: restricted isometry constant. compressed sensing, joint sparse reconstruction algorithms, BibRef

Rateb, A.M., Syed-Yusof, S.K., Rashid, R.A.,
On the Impact of Prefiltering on Compressed Sensing in Presence of Invalid Measurements,
SPLetters(24), No. 12, December 2017, pp. 1886-1890.
IEEE DOI 1712
Compressed sensing, Current measurement, Gain measurement, Minimization, Noise measurement, Random variables, Sensors, sparse recovery BibRef

Testa, M.[Matteo], Magli, E.[Enrico],
Compressive Bayesian K-SVD,
SP:IC(60), No. 1, 2018, pp. 1-5.
Elsevier DOI 1712
Compressed sensing BibRef

Zha, Z.Y.[Zhi-Yuan], Liu, X.[Xin], Zhang, X.G.[Xing-Gan], Chen, Y.[Yang], Tang, L.[Lan], Bai, Y.C.[Ye-Chao], Wang, Q.[Qiong], Shang, Z.H.[Zhen-Hong],
Compressed sensing image reconstruction via adaptive sparse nonlocal regularization,
VC(34), No. 1, January 2018, pp. 117-137.
Springer DOI 1801
BibRef

Gu, X.Y.[Xiao-Yi], Tu, S.Y.[Shenyin-Ying], Shi, H.J.M.[Hao-Jun Michael], Case, M.[Mindy], Needell, D.[Deanna], Plan, Y.[Yaniv],
Optimizing Quantization for Lasso Recovery,
SPLetters(25), No. 1, January 2018, pp. 45-49.
IEEE DOI 1801
Quantized compressed sensing, assuming that Lasso is used for signal estimation. compressed sensing, optimisation, quantisation (signal), Lasso recovery, constrained Lloyd-Max-like framework, quantized compressed sensing (CS) BibRef

Lu, C.Y.[Can-Yi], Feng, J.S.[Jia-Shi], Yan, S.C.[Shui-Cheng], Lin, Z.C.[Zhou-Chen],
A Unified Alternating Direction Method of Multipliers by Majorization Minimization,
PAMI(40), No. 3, March 2018, pp. 527-541.
IEEE DOI 1802
Compressed sensing, Convergence, Jacobian matrices, Minimization, Radio frequency, Standards, mixed ADMM BibRef

Unde, A.S.[Amit Satish], Deepthi, P.P.,
Fast BCS-FOCUSS and DBCS-FOCUSS with augmented Lagrangian and minimum residual methods,
JVCIR(52), 2018, pp. 92-100.
Elsevier DOI 1804
Block compressive sensing FOCal Underdetermined System Solver. Block compressive sensing, BCS-FOCUSS, DBCS-FOCUSS, BCS-augmented Lagrangian method, Minimum residual method BibRef

Mashhadi, M.B.[Mahdi Boloursaz], Gazor, S.[Saeed], Rahnavard, N.[Nazanin], Marvasti, F.[Farokh],
Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by Predictive Level Comparisons,
SPLetters(25), No. 4, April 2018, pp. 496-500.
IEEE DOI 1804
compressed sensing, error correction, iterative methods, signal reconstruction, signal sampling, spectral analysis, sparse signal acquisition BibRef

Hsieh, S.H., Lu, C.S., Pei, S.C.,
Compressive Sensing Matrix Design for Fast Encoding and Decoding via Sparse FFT,
SPLetters(25), No. 4, April 2018, pp. 591-595.
IEEE DOI 1804
Decoding, Dictionaries, Encoding, Frequency-domain analysis, Hardware, Sensors, Sparse matrices, Compressive sensing (CS), sparsity BibRef

Chen, Z., Tian, W.W.[Wen-Wen], Qian, X., Gong, C.[Chen],
Efficient and Robust Image Coding and Transmission Based on Scrambled Block Compressive Sensing,
MultMed(20), No. 7, July 2018, pp. 1610-1621.
IEEE DOI 1806
Complexity theory, Encoding, Image coding, Image reconstruction, Quantization (signal), Sensors, Visualization, robust image compression BibRef

Hou, X.S.[Xing-Song], Tian, W.W.[Wen-Wen], Gong, C.[Chen],
Robust and efficient SAR image coding transmission based on compressive sensing,
ICIP14(2512-2516)
IEEE DOI 1502
Compressed sensing BibRef

Wang, H.[Huake], Li, Z.[Ziang], Hou, X.S.[Xing-Song],
Versatile Denoising-Based Approximate Message Passing for Compressive Sensing,
IP(32), 2023, pp. 2761-2775.
IEEE DOI 2305
Image reconstruction, Noise reduction, Noise level, Distortion, Compressed sensing, Task analysis, Smoothing methods, fine-grained noise level division BibRef

Zhu, Z., Li, G., Ding, J., Li, Q., He, X.,
On Collaborative Compressive Sensing Systems: The Framework, Design, and Algorithm,
SIIMS(11), No. 2, 2018, pp. 1717-1758.
DOI Link 1807
BibRef

He, F., Huang, X., Liu, Y., Yan, M.,
Fast Signal Recovery From Saturated Measurements by Linear Loss and Nonconvex Penalties,
SPLetters(25), No. 9, September 2018, pp. 1374-1378.
IEEE DOI 1809
compressed sensing, concave programming, losses, minimax techniques, linear loss, nonconvex penalties, saturation BibRef

Jiang, W.[Wei], Yang, J.J.[Jun-Jie],
Energy-constraint rate distortion optimization for compressive sensing-based image coding,
SIViP(12), No. 7, October 2018, pp. 1419-1427.
WWW Link. 1809
BibRef

Wimalajeewa, T., Varshney, P.K.,
Compressive Sensing Based Classification in the Presence of Intra-and Inter-Signal Correlation,
SPLetters(25), No. 9, September 2018, pp. 1398-1402.
IEEE DOI 1809
compressed sensing, signal classification, signal reconstruction, compression ratio, intra-signal correlation, correlated data BibRef

Garcia, H., Correa, C.V., Arguello, H.,
Multi-Resolution Compressive Spectral Imaging Reconstruction from Single Pixel Measurements,
IP(27), No. 12, December 2018, pp. 6174-6184.
IEEE DOI 1810
approximation theory, cameras, compressed sensing, image reconstruction, image resolution, spectral analysis, compressive spectral imaging BibRef

Garcia, H., Correa, C.V., Arguello, H.,
Optimized Sensing Matrix for Single Pixel Multi-Resolution Compressive Spectral Imaging,
IP(29), 2020, pp. 4243-4253.
IEEE DOI 2002
Sensing matrix design, single pixel camera, compressive spectral imaging, multi-resolution BibRef

Ramirez, J.M., Arguello, H.,
Spectral Image Classification from Multi-Sensor Compressive Measurements,
GeoRS(58), No. 1, January 2020, pp. 626-636.
IEEE DOI 2001
Feature extraction, Image coding, Apertures, Optical imaging, Optical sensors, Compressive spectral imaging (CSI), spectral image classification BibRef

Vargas, E., Espitia, Ó., Arguello, H., Tourneret, J.,
Spectral Image Fusion From Compressive Measurements,
IP(28), No. 5, May 2019, pp. 2271-2282.
IEEE DOI 1903
compressed sensing, image fusion, image reconstruction, image resolution, image sampling, inverse problems, remote sensing BibRef

Hinojosa, C., Ramirez, J.M., Arguello, H.,
Spectral-Spatial Classification from Multi-Sensor Compressive Measurements Using Superpixels,
ICIP19(3143-3147)
IEEE DOI 1910
compressive spectral imaging, multi-sensor measurements, spectral image classification, feature extraction, superpixel algorithms. BibRef

Gan, H.P.[Hong-Ping], Xiao, S.[Song], Zhao, Y.M.[Yi-Min], Xue, X.[Xiao],
Construction of efficient and structural chaotic sensing matrix for compressive sensing,
SP:IC(68), 2018, pp. 129-137.
Elsevier DOI 1810
Compressive sensing, Structural sensing matrix, Mutual coherence, Chebyshev chaotic sequence BibRef

Suwanwimolkul, S., Zhang, L., Gong, D., Zhang, Z., Chen, C., Ranasinghe, D.C., Qinfeng Shi, J.,
An Adaptive Markov Random Field for Structured Compressive Sensing,
IP(28), No. 3, March 2019, pp. 1556-1570.
IEEE DOI 1812
Adaptation models, Probabilistic logic, Compressed sensing, Estimation, Markov processes, Biomedical measurement, sparse representation BibRef

Li, W.[Wan], Liu, F.[Fang], Jiao, L.C.[Li-Cheng], Hu, F.[Fei], Yang, S.Y.[Shu-Yuan],
Video reconstruction based on Intrinsic Tensor Sparsity model,
SP:IC(72), 2019, pp. 113-125.
Elsevier DOI 1902
Compressive sensing, Gaussian mixture model, Joint sparsity, Intrinsic Tensor Sparsity, CACTI BibRef

Li, D.[Dan], Wu, Z.J.[Zhao-Jun], Wang, Q.A.[Qi-Ang],
Edge guided compressive sensing for image reconstruction based on two-stage L_0 minimization,
JVCIR(59), 2019, pp. 461-474.
Elsevier DOI 1903
Compressive sensing, Image reconstruction, minimization, Edge prior, Multiple sampling scheme BibRef

Li, F., Hong, S., Gu, Y., Wang, L.,
An Optimization-Oriented Algorithm for Sparse Signal Reconstruction,
SPLetters(26), No. 3, March 2019, pp. 515-519.
IEEE DOI 1903
compressed sensing, computational complexity, greedy algorithms, optimisation, search problems, signal reconstruction, optimization-oriented algorithm BibRef

Daei, S.[Sajad], Haddadi, F.[Farzan], Amini, A.[Arash],
Distribution-Aware Block-Sparse Recovery via Convex Optimization,
SPLetters(26), No. 4, April 2019, pp. 528-532.
IEEE DOI 1903
Bayes methods, compressed sensing, convex programming, probability, signal reconstruction, signal sampling, block-sparse signal, convex optimization BibRef

Wang, Z.L.[Ze-Long], Zhu, J.[Jubo],
Compressive spectral feature sensing,
IET-IPR(13), No. 4, March 2019, pp. 644-652.
DOI Link 1903
BibRef

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Recovery of signal from sparse input. BibRef

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IEEE DOI 1905
compressed sensing, computational complexity, greedy algorithms, iterative methods, signal reconstruction, iterations, Tucker decomposition BibRef

Abedi, M., Sun, B., Zheng, Z.,
A Sinusoidal-Hyperbolic Family of Transforms With Potential Applications in Compressive Sensing,
IP(28), No. 7, July 2019, pp. 3571-3583.
IEEE DOI 1906
Image coding, Shape, Vibrations, Transforms, Sensors, Redundancy, Compressed sensing, Basis, compressive sensing, eigendecomposition, vibration BibRef

Rey-Escudero, S.[Samuel], Garcia, F.J.I.[Fernando Jose Iglesias], Cabrera, C.[Cristóbal], Marques, A.G.[Antonio G.],
Sampling and Reconstruction of Diffused Sparse Graph Signals From Successive Local Aggregations,
SPLetters(26), No. 8, August 2019, pp. 1142-1146.
IEEE DOI 1908
compressed sensing, graph theory, signal reconstruction, signal sampling, diffused sparse graph signals, distributed source localization BibRef

Dolatabadi, H.M., Amini, A.,
Deterministic Design of Toeplitz Matrices With Small Coherence Based on Weyl Sums,
SPLetters(26), No. 10, October 2019, pp. 1501-1505.
IEEE DOI 1909
Coherence, Sparse matrices, Sensors, Compressed sensing, Tools, Channel estimation, Coherence, compressive sensing, Weyl sum BibRef

Keshavarzian, R.[Razieh], Aghagolzadeh, A.[Ali], Rezaii, T.Y.[Tohid Yousefi],
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Elsevier DOI 1909
Compressed sensing, Group sparse representation, Half-quadratic theory, Image recovery, Nonlocal sparsity BibRef

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Compressive domain spatial-temporal difference saliency-based realtime adaptive measurement method for video recovery,
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Compressive Color Pattern Detection Using Partial Orthogonal Circulant Sensing Matrix,
IP(29), No. 1, 2020, pp. 670-678.
IEEE DOI 1910
One key issue in compressive sensing is to design a sensing matrix that is random enough to have a good signal reconstruction quality. compressed sensing, edge detection, image colour analysis, pattern recognition, signal reconstruction, image color analysis BibRef

Porta, C.J.D.[C. J. Della], Bekit, A.A., Lampe, B.H., Chang, C.,
Hyperspectral Image Classification via Compressive Sensing,
GeoRS(57), No. 10, October 2019, pp. 8290-8303.
IEEE DOI 1910
compressed sensing, geophysical image processing, geophysical signal processing, hyperspectral imaging, universality BibRef

Wu, Z.L.[Zong-Liang], Yang, C.S.[Cheng-Shuai], Su, X.F.[Xiong-Fei], Yuan, X.[Xin],
Adaptive Deep PnP Algorithm for Video Snapshot Compressive Imaging,
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Springer DOI 2307
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Wang, P.[Ping], Wang, L.[Lishun], Yuan, X.[Xin],
Deep Optics for Video Snapshot Compressive Imaging,
ICCV23(10612-10622)
IEEE DOI Code:
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Yuan, X., Brady, D.J., Katsaggelos, A.K.,
Snapshot Compressive Imaging: Theory, Algorithms, and Applications,
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IEEE DOI 2103
Signal processing algorithms, Detectors, Tomography, Signal processing, Image reconstruction BibRef

Wang, Z.J.[Zheng-Jue], Zhang, H.[Hao], Cheng, Z.H.[Zi-Heng], Chen, B.[Bo], Yuan, X.[Xin],
MetaSCI: Scalable and Adaptive Reconstruction for Video Compressive Sensing,
CVPR21(2083-2092)
IEEE DOI 2111
Training, Deep learning, Modulation, Imaging, Graphics processing units, Detectors, Data models BibRef

Chen, C.[Can], Zhou, C.[Chao], Liu, J.[Jian], Zhang, D.Y.[Deng-Yin],
Multi-Hypothesis Prediction Scheme Based on the Joint Sparsity Model,
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Distributed compressive video sensing. BibRef

Marks, R.J.[Robert J.],
Sampling below the Nyquist density using spectral subtiles,
JOSA-A(36), No. 8, August 2019, pp. 1322-1332.
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Composite materials, Fourier transforms, Interpolation, Multiplexing, Reflection, Spectral imaging BibRef

Stankovic, L., Mandic, D.P., Dakovic, M., Kisil, I.,
Demystifying the Coherence Index in Compressive Sensing,
SPMag(37), No. 1, January 2020, pp. 152-162.
IEEE DOI 2001
[Lecture Notes] Sparse matrices, Indexes, Discrete Fourier transforms, Weight measurement, Signal processing BibRef

Wang, B., Geng, J.,
Efficient Deblending in the PFK Domain Based on Compressive Sensing,
GeoRS(58), No. 2, February 2020, pp. 995-1003.
IEEE DOI 2001
Transforms, Compressed sensing, Data processing, Receivers, Delay effects, Data acquisition, Curvelet transform, sparsity BibRef

Trevisi, M., Akbari, A., Trocan, M., Rodríguez-Vázquez, Á., Carmona-Galán, R.,
Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction,
CirSysVideo(30), No. 2, February 2020, pp. 387-399.
IEEE DOI 2002
Sparse matrices, Image coding, Generators, Flip-flops, Image reconstruction, Symmetric matrices, Matrix decomposition, ternary measurement matrix BibRef

Monsalve, J., Rueda-Chacon, H., Arguello, H.,
Sensing Matrix Design for Compressive Spectral Imaging via Binary Principal Component Analysis,
IP(29), 2020, pp. 4003-4012.
IEEE DOI 2002
Compressive spectral imaging, binary principal component analysis, sensing matrix design BibRef

Vlašic, T.[Tin], Ralašic, I.[Ivan], Tafro, A.[Azra], Seršic, D.[Damir],
Spline-like Chebyshev polynomial model for compressive imaging,
JVCIR(66), 2020, pp. 102731.
Elsevier DOI 2003
Polynomial representation of image, Chebyshev moments, Runge phenomenon, Sparse modeling, Compressive sensing, 2D-imaging BibRef

Chen, J.[Jian], Chen, Z.F.[Zhi-Feng], Su, K.X.[Kai-Xiong], Peng, Z.[Zheng], Ling, N.[Nam],
Video compressed sensing reconstruction based on structural group sparsity and successive approximation estimation model,
JVCIR(66), 2020, pp. 102734.
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Compressed sensing, Group sparsity, Interframe estimation, Reconstruction algorithms BibRef

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(L_1,L_2)-RIP and Projected Back-Projection Reconstruction for Phase-Only Measurements,
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IEEE DOI 2004
Compressed sensing, restricted isometry property, complex Gaussian, phase-only BibRef

Wu, C.[Cathy], Pozdnukhov, A.[Alexei], Bayen, A.M.[Alexandre M.],
Block Simplex Signal Recovery: Methods, Trade-Offs, and an Application to Routing,
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IEEE DOI 2004
Estimation, Bayes methods, Compressed sensing, Convex functions, Sensors, Scalability, Transportation, Compressed sensing, signal reconstruction BibRef

Zhang, H., Lei, H.,
The Failure Case of Phase Transition for Penalized Problems in Corrupted Sensing,
SPLetters(27), 2020, pp. 555-559.
IEEE DOI 2005
Sensors, Upper bound, Extraterrestrial measurements, Geophysical measurements, Geometry, Gaussian processes, Simulation, compressed sensing BibRef

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Compressive sensed video recovery via iterative thresholding with random transforms,
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Sparse Signal Reconstruction Using Blind Super-Resolution With Arbitrary Sampling,
SPLetters(27), 2020, pp. 615-619.
IEEE DOI 2005
Frequency modulation, Signal resolution, Deconvolution, Image resolution, Wave functions, Compressed sensing, joint spectral sparsity BibRef

Grosche, S., Regensky, A., Seiler, J.[Jürgen], Kaup, A.[André],
Boosting Compressed Sensing Using Local Measurements and Sliding Window Reconstruction,
IP(29), 2020, pp. 7931-7944.
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Image Reconstruction, local measurements, compressed sensing, non-regular sampling BibRef

Canh, T.N.[Thuong Nguyen], Jeon, B.W.[Byeung-Woo],
Restricted Structural Random Matrix for compressive sensing,
SP:IC(90), 2021, pp. 116017.
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Sampling matrix. Compressive sensing, Structural sparse matrix, Restricted isometry property, Security, Kronecker compressive sensing BibRef

Tai, C.L., Hsieh, S.H., Lu, C.S.,
Greedy Algorithms for Hybrid Compressed Sensing,
SPLetters(27), 2020, pp. 2059-2063.
IEEE DOI 2012
Error bound, greedy algorithms, hybrid compressed sensing (CS) BibRef

Qin, S.[Shun],
Simple algorithm for L1-norm regularisation-based compressed sensing and image restoration,
IET-IPR(14), No. 14, December 2020, pp. 3405-3413.
DOI Link 2012
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Zhang, Z.H.[Zhong-Hao], Liu, Y.P.[Yi-Peng], Liu, J.[Jiani], Wen, F.[Fei], Zhu, C.[Ce],
AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing,
IP(30), 2021, pp. 1487-1500.
IEEE DOI 2101
Image reconstruction, Noise reduction, Sparse matrices, Iterative algorithms, Visualization, Optimization, Neural networks, image reconstruction BibRef

Zha, Z.Y.[Zhi-Yuan], Yuan, X.[Xin], Zhou, J.T.Y.[Joey Tian-Yi], Zhou, J.T.[Jian-Tao], Wen, B.H.[Bi-Han], Zhu, C.[Ce],
The Power Of Triply Complementary Priors For Image Compressive Sensing,
ICIP20(983-987)
IEEE DOI 2011
Image restoration, Optimization, Inverse problems, Standards, Noise reduction, Compressed sensing, Sensors, Image CS, non-local self-similarity BibRef

Kaur, A., Mishra, D., Amogh, K.M., Sarkar, M.,
On-Array Compressive Acquisition in CMOS Image Sensors Using Accumulated Spatial Gradients,
CirSysVideo(31), No. 2, February 2021, pp. 523-532.
IEEE DOI 2102
Image coding, Image sensors, Silicon, Image reconstruction, Interpolation, Discrete cosine transforms, Hardware, deep convolution neural network BibRef

Mishra, D.[Dipti], Singh, S.K.[Satish Kumar], Singh, R.K.[Rajat Kumar], Mishra, D., Singh, S.K., Singh, R.K.,
Wavelet-Based Deep Auto Encoder-Decoder (WDAED)-Based Image Compression,
CirSysVideo(31), No. 4, April 2021, pp. 1452-1462.
IEEE DOI 2104
Image coding, Wavelet transforms, Decoding, Image resolution, Convolutional codes, Machine learning, Wavelet, deep, CNN, frequency, autoencoder BibRef

Sasmal, P.[Pradip], Jampana, P.[Phanindra], Sastry, C.S.[Challa S.],
Construction of Binary Matrices as a Union of Orthogonal Blocks via Generalized Euler Squares,
SPLetters(28), 2021, pp. 882-886.
IEEE DOI 2106
Germanium, Sparse matrices, Coherence, Indexes, Matching pursuit algorithms, Image coding, Compressed sensing, block coherence BibRef

Torkamani, R.[Razieh], Zayyani, H.[Hadi], Sadeghzadeh, R.A.[Ramazan Ali],
Model-based decentralized Bayesian algorithm for distributed compressed sensing,
SP:IC(95), 2021, pp. 116212.
Elsevier DOI 2106
Distributed compressive sensing, Joint sparsity, Wavelet-tree structure, Bessel K-form, Variational Bayesian inference BibRef

Das, S.[Samiran],
Hyperspectral image, video compression using sparse Tucker tensor decomposition,
IET-IPR(15), No. 4, 2021, pp. 964-973.
DOI Link 2106
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Chen, Z.[Zan], Guo, W.L.[Wen-Long], Feng, Y.J.[Yuan-Jing], Li, Y.Q.[Yong-Qiang], Zhao, C.C.[Chang-Chen], Ren, Y.[Yi], Shao, L.[Ling],
Deep-Learned Regularization and Proximal Operator for Image Compressive Sensing,
IP(30), 2021, pp. 7112-7126.
IEEE DOI 2108
Image reconstruction, Optimization, Neural networks, Loss measurement, Iterative algorithms, Approximation algorithms, proximal operator BibRef

Zhou, S.W.[Si-Wang], He, Y.[Yan], Liu, Y.H.[Yong-He], Li, C.Q.[Cheng-Qing], Zhang, J.M.[Jian-Ming],
Multi-Channel Deep Networks for Block-Based Image Compressive Sensing,
MultMed(23), 2021, pp. 2627-2640.
IEEE DOI 2109
Image reconstruction, Sensors, Correlation, Approximation algorithms, Smoothing methods, Neural networks, image recovery BibRef

Zhang, B.[Bo], Xiao, D.[Di], Xiang, Y.[Yong],
Robust Coding of Encrypted Images via 2D Compressed Sensing,
MultMed(23), 2021, pp. 2656-2671.
IEEE DOI 2109
Image coding, Encryption, Computational complexity, Robustness, 2D compressed sensing, image encryption BibRef

Lu, R.Y.[Rui-Ying], Chen, B.[Bo], Liu, G.L.[Guan-Liang], Cheng, Z.H.[Zi-Heng], Qiao, M.[Mu], Yuan, X.[Xin],
Dual-view Snapshot Compressive Imaging via Optical Flow Aided Recurrent Neural Network,
IJCV(129), No. 12, December 2021, pp. 3279-3298.
Springer DOI 2111
capture videos from two field-of-views. BibRef

Kazemi, V.[Vahdat], Shahzadi, A.[Ali], Bizaki, H.K.[Hossein Khaleghi],
New flexible deterministic compressive measurement matrix based on finite Galois field,
IET-IPR(16), No. 1, 2022, pp. 239-251.
DOI Link 2112
Construct sensing martix. BibRef

Li, J.H.[Jia-Hang], Zhu, Q.[Qi], Wu, Y.[Yuezhou], Gao, X.Y.[Xu-Yang],
Image Reconstruction Based on Deep Iterative Shrinkage Network,
ICIVC21(259-263)
IEEE DOI 2112
Upper bound, Thresholding (Imaging), Stability analysis, Velocity measurement, Image reconstruction, Optimization, deep neural network BibRef

Cai, L.[Lei], Fu, Y.[Yuli], Zhu, T.[Tao], Xiang, Y.J.[You-Jun], Zeng, H.Q.[Huan-Qiang],
Proximal-Gen for fast compressed sensing recovery,
JVCIR(82), 2022, pp. 103358.
Elsevier DOI 2201
Compressed sensing, Generative models, Generator range, Reconstruction efficiency BibRef

Cai, L.[Lei], Fu, Y.[Yuli], Xiang, Y.J.[You-Jun], Zhu, T., Li, X., Zeng, H.Q.[Huan-Qiang],
Fast compressed sensing recovery using generative models and sparse deviations modeling,
VCIP20(447-450)
IEEE DOI 2102
Optimization, Image reconstruction, Compressed sensing, Measurement uncertainty, Computational modeling, Sensors, projected gradient descent BibRef

Wan, R.[Rentao], Zhou, J.J.[Jin-Jia], Huang, B.[Bowen], Zeng, H.[Hui], Fan, Y.[Yibo],
APMC: Adjacent Pixels Based Measurement Coding System for Compressively Sensed Images,
MultMed(24), 2022, pp. 3558-3569.
IEEE DOI 2207
Image coding, Prediction algorithms, Encoding, Correlation, Image reconstruction, Current measurement, Compressed sensing, measurement-domain prediction BibRef

Lee, B.[Bokyeung], Ko, K.[Kyungdeuk], Hong, J.[Jonghwan], Ku, B.[Bonhwa], Ko, H.S.[Han-Seok],
Information Bottleneck Measurement for Compressed Sensing Image Reconstruction,
SPLetters(29), 2022, pp. 1943-1947.
IEEE DOI 2209
Sensors, Generators, Decoding, Training, Image reconstruction, Image coding, Loss measurement, Information bottleneck, deep learning BibRef

Guo, Y.[Yuan], Jiang, J.L.[Jin-Lin], Chen, W.[Wei],
Fast bilateral complementary network for deep learning compressed sensing image reconstruction,
IET-IPR(16), No. 13, 2022, pp. 3485-3498.
DOI Link 2210
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Shen, M.[Minghe], Gan, H.P.[Hong-Ping], Ning, C.[Chao], Hua, Y.[Yi], Zhang, T.[Tao],
TransCS: A Transformer-Based Hybrid Architecture for Image Compressed Sensing,
IP(31), 2022, pp. 6991-7005.
IEEE DOI 2212
Image reconstruction, Transformers, Sensors, Task analysis, Compressed sensing, Matching pursuit algorithms, Head, image reconstruction BibRef

Heshmati, A.[Alireza], Amini, S.[Sajjad], Ghaemmaghami, S.[Shahrokh], Marvasti, F.[Farokh],
Designing Low Coherent Measurement Matrix With Controlled Spectral Norm Via an Efficient Approximation of L_inf-Norm,
SPLetters(29), 2022, pp. 2243-2247.
IEEE DOI 2212
Linear programming, Sparse matrices, Matrix decomposition, Current measurement, Coherence, Sensors, Optimization, Low coherent, measurement matrix BibRef

Zhang, J.[Jian], Chen, B.[Bin], Xiong, R.Q.[Rui-Qin], Zhang, Y.B.[Yong-Bing],
Physics-Inspired Compressive Sensing: Beyond deep unrolling,
SPMag(40), No. 1, January 2023, pp. 58-72.
IEEE DOI 2301
Image coding, Computational modeling, Signal processing algorithms, Transforms, Market research, Task analysis BibRef

Xu, J.[Jin], Fu, Z.Z.[Zhi-Zhong],
Image compressive sensing via hybrid regularization combining centralized group sparse representation and deep denoiser prior,
JVCIR(90), 2023, pp. 103723.
Elsevier DOI 2301
Image compressive sensing, Hybrid regularization, Centralized group sparse representation, Deep denoiser prior BibRef

Gan, H.P.[Hong-Ping], Gao, Y.[Yang], Liu, C.[Chunyi], Chen, H.W.[Hai-Wei], Zhang, T.[Tao], Liu, F.[Feng],
AutoBCS: Block-Based Image Compressive Sensing With Data-Driven Acquisition and Noniterative Reconstruction,
Cyber(53), No. 4, April 2023, pp. 2558-2571.
IEEE DOI 2303
Image reconstruction, Sensors, Transforms, Iterative algorithms, Discrete wavelet transforms, Reconstruction algorithms, image compressive sensing (CS) BibRef

Yang, X.[Xin], Yang, C.L.[Chun-Ling],
MAP-Inspired Deep Unfolding Network for Distributed Compressive Video Sensing,
SPLetters(30), 2023, pp. 309-313.
IEEE DOI 2304
Image reconstruction, Correlation, Optical imaging, Adaptive optics, Optical signal processing, Iterative methods, multi-hypothesis BibRef

Song, J.C.[Jie-Chong], Chen, B.[Bin], Zhang, J.[Jian],
Dynamic Path-Controllable Deep Unfolding Network for Compressive Sensing,
IP(32), 2023, pp. 2202-2214.
IEEE DOI 2305
Heuristic algorithms, Optimization, Image reconstruction, Image coding, Compressed sensing, Image restoration, dynamic modulation BibRef

Song, J.C.[Jie-Chong], Chen, B.[Bin], Zhang, J.[Jian],
Deep Memory-Augmented Proximal Unrolling Network for Compressive Sensing,
IJCV(131), No. 6, June 2023, pp. 1477-1496.
Springer DOI 2305
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Yong, J.W.[Jia-Wei], Li, K.[Kexin], Feng, Z.J.[Zhe-Jun], Wu, Z.Y.[Zeng-Yan], Ye, S.B.[Shu-Bing], Song, B.M.[Bao-Ming], Wei, R.X.[Run-Xi], Cao, C.Q.[Chang-Qing],
Research on Photon-Integrated Interferometric Remote Sensing Image Reconstruction Based on Compressed Sensing,
RS(15), No. 9, 2023, pp. xx-yy.
DOI Link 2305
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Ye, D.J.[Dong-Jie], Ni, Z.K.[Zhang-Kai], Wang, H.[Hanli], Zhang, J.[Jian], Wang, S.Q.[Shi-Qi], Kwong, S.[Sam],
CSformer: Bridging Convolution and Transformer for Compressive Sensing,
IP(32), 2023, pp. 2827-2842.
IEEE DOI 2306
Transformers, Image reconstruction, Convolutional neural networks, Convolution, Deep learning, image reconstruction BibRef

Wang, L.S.[Li-Shun], Cao, M.[Miao], Zhong, Y.[Yong], Yuan, X.[Xin],
Spatial-Temporal Transformer for Video Snapshot Compressive Imaging,
PAMI(45), No. 7, July 2023, pp. 9072-9089.
IEEE DOI 2306
Transformers, Image reconstruction, Image color analysis, Cameras, Task analysis, Gray-scale, Correlation, Attention, transformer BibRef

Wang, Y.H.[Ying-Hua], He, Z.[Zihao], Zhang, G.M.[Guo-Ming], Wen, J.M.[Jin-Ming],
Improved Sufficient Conditions Based on RIC of Order 2s for IHT and HTP Algorithms,
SPLetters(30), 2023, pp. 668-672.
IEEE DOI 2307
iterative hard thresholding (IHT) and hard thresholding pursuit (HTP). Signal processing algorithms, Indexes, Thresholding (Imaging), Sparse matrices, Matching pursuit algorithms, restricted isometry property BibRef

Patel, S.[Saumya], Vaish, A.[Ankita],
An efficient optimization of measurement matrix for compressive sensing,
JVCIR(95), 2023, pp. 103904.
Elsevier DOI 2309
Measurement matrix, Optimization, Compressive sensing, Sparsity BibRef

Meng, Z.Y.[Zi-Yi], Yuan, X.[Xin], Jalali, S.[Shirin],
Deep Unfolding for Snapshot Compressive Imaging,
IJCV(131), No. 1, January 2023, pp. 2933-2958.
Springer DOI 2310
BibRef

Yamaç, M.[Mehmet], Akpinar, U.[Ugur], Sahin, E.[Erdem], Kiranyaz, S.[Serkan], Gabbouj, M.[Moncef],
Generalized Tensor Summation Compressive Sensing Network (GTSNET): An Easy to Learn Compressive Sensing Operation,
IP(32), 2023, pp. 5637-5651.
IEEE DOI Code:
WWW Link. 2310
BibRef

Qiu, W.W.[Wei-Wei], Xue, L.L.[Lin-Lin], Wang, Z.[Zhongpeng],
Recovery performance improvement of image compressive sensing using complex-valued Vandermonde matrix,
IET-IPR(17), No. 13, 2023, pp. 3856-3868.
DOI Link 2311
compressed sensing, image reconstruction, Vandermonde matrix BibRef

Zhang, K.Y.[Kui-Yuan], Hua, Z.Y.[Zhong-Yun], Li, Y.M.[Yuan-Man], Chen, Y.Y.[Yong-Yong], Zhou, Y.C.[Yi-Cong],
AMS-Net: Adaptive Multi-Scale Network for Image Compressive Sensing,
MultMed(25), 2023, pp. 5676-5689.
IEEE DOI 2311
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Chen, Y.[Yong], Lai, W.Z.[Wen-Zhen], He, W.[Wei], Zhao, X.L.[Xi-Le], Zeng, J.S.[Jin-Shan],
Hyperspectral Compressive Snapshot Reconstruction via Coupled Low-Rank Subspace Representation and Self-Supervised Deep Network,
IP(33), 2024, pp. 926-941.
IEEE DOI Code:
WWW Link. 2402
Image reconstruction, Optimization, Imaging, Learning systems, Iterative algorithms, Hyperspectral imaging, Training data, self-supervised deep network BibRef

Cui, W.X.[Wen-Xue], Fan, X.P.[Xiao-Peng], Zhang, J.[Jian], Zhao, D.B.[De-Bin],
Deep Unfolding Network for Image Compressed Sensing by Content-Adaptive Gradient Updating and Deformation-Invariant Non-Local Modeling,
MultMed(26), 2024, pp. 4012-4027.
IEEE DOI 2402
Image reconstruction, Compressed sensing, Adaptation models, Deformable models, Image coding, Adaptive systems, Limiting, proximal gradient descent (PGD) BibRef

Zhao, Y.P.[Yin-Ping], Zhang, J.C.[Jian-Cheng], Chen, Y.Y.[Yong-Yong], Wang, Z.[Zhen], Li, X.L.[Xue-Long],
RCUMP: Residual Completion Unrolling With Mixed Priors for Snapshot Compressive Imaging,
IP(33), 2024, pp. 2347-2360.
IEEE DOI 2404
Imaging, Image coding, Iterative methods, Optimization, Image reconstruction, Hyperspectral imaging, deep unrolling-based methods BibRef

Gu, Z.F.[Zhen-Fei], Zhou, C.[Chao], Lin, G.F.[Guo-Feng],
A temporal shift reconstruction network for compressive video sensing,
IET-CV(18), No. 4, 2024, pp. 448-457.
DOI Link 2406
Compressive video sensing. compressed sensing, image reconstruction, neural nets, video coding BibRef

Qiu, C.X.[Chen-Xi], Hu, X.M.[Xue-Mei],
AdaCS: Adaptive Compressive Sensing With Restricted Isometry Property-Based Error-Clamping,
PAMI(46), No. 7, July 2024, pp. 4702-4719.
IEEE DOI 2406
Image reconstruction, Imaging, Measurement uncertainty, Magnetic resonance imaging, Adaptive systems, Loss measurement, restricted isometry property BibRef

Zhao, H.H.[Hui-Huang], Zhang, L.[Lin], Zhang, Y.D.[Yu-Dong], Wang, Y.[Yaonan],
Imagery Overlap Block Compressive Sensing With Convex Optimization,
ITS(25), No. 7, July 2024, pp. 8076-8092.
IEEE DOI 2407
Image reconstruction, Compressed sensing, Image coding, Matching pursuit algorithms, TV, Reconstruction algorithms, Poisson function BibRef

Su, Y.M.[Yue-Ming], Lian, Q.S.[Qiu-Sheng], Zhang, D.[Dan], Shi, B.S.[Bao-Shun],
Transformer based Douglas-Rachford unrolling network for compressed sensing,
SP:IC(127), 2024, pp. 117153.
Elsevier DOI Code:
WWW Link. 2408
Compressed sensing, Transformer, Binary sampling, Douglas-Rachford algorithm, Deep learning BibRef

Cao, J.H.[Jia-Hui], Yang, Z.B.[Zhi-Bo], Chen, X.F.[Xue-Feng],
Compressed Line Spectral Estimation Using Covariance: A Sparse Reconstruction Perspective,
SPLetters(31), 2024, pp. 2540-2544.
IEEE DOI 2410
Covariance matrices, Sensors, Estimation, Coherence, Vectors, Sparse matrices, Matrices, Compressed sensing, periodic non-uniform sampling BibRef

Dong, Y.[Yubo], Gao, D.[Dahua], Qiu, T.[Tian], Li, Y.Y.[Yu-Yan], Yang, M.X.[Min-Xi], Shi, G.M.[Guang-Ming],
Residual Degradation Learning Unfolding Framework with Mixing Priors Across Spectral and Spatial for Compressive Spectral Imaging,
CVPR23(22262-22271)
IEEE DOI 2309
BibRef

Jin, M.Y.[Meng-Ying], Wei, Z.H.[Zhi-Hui], Xiao, L.[Liang],
Learning Texture Enhancement Prior with Deep Unfolding Network for Snapshot Compressive Imaging,
ACCV22(III:357-373).
Springer DOI 2307
BibRef

Zhang, H.R.[Han-Ru], Yang, C.L.[Chun-Ling],
Dual-Domain Update and Double-Group Optimization Network for Image Compressive Sensing,
ICIP22(1286-1290)
IEEE DOI 2211
Integrated circuits, Image coding, Noise reduction, Focusing, Visual effects, Sensors, Iterative methods, Inter-feature recurrent BibRef

Yang, C.S.[Cheng-Shuai], Zhang, S.Y.[Shi-Yu], Yuan, X.[Xin],
Ensemble Learning Priors Driven Deep Unfolding for Scalable Video Snapshot Compressive Imaging,
ECCV22(XXIII:600-618).
Springer DOI 2211
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Hu, X.W.[Xiao-Wan], Cai, Y.H.[Yuan-Hao], Lin, J.[Jing], Wang, H.Q.[Hao-Qian], Yuan, X.[Xin], Zhang, Y.[Yulun], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging,
CVPR22(17521-17530)
IEEE DOI 2210
Photography, Learning systems, Visualization, Computational photography, Time-frequency analysis, Smoothing methods, Pattern recognition BibRef

Fan, Z.E.[Zi-En], Lian, F.[Feng], Quan, J.N.[Jia-Ni],
Global Sensing and Measurements Reuse for Image Compressed Sensing,
CVPR22(8944-8953)
IEEE DOI 2210
GSM, Convolutional codes, Computational modeling, Benchmark testing, Feature extraction, Sensors, Efficient learning and inferences BibRef

Liu, J.L.[Jiu-Long], Liu, Z.Q.[Zhao-Qiang],
Non-Iterative Recovery from Nonlinear Observations using Generative Models,
CVPR22(233-243)
IEEE DOI 2210
Atmospheric measurements, Computational modeling, Reconstruction algorithms, Particle measurements, Statistical methods BibRef

Saideni, W.[Wael], Courreges, F.[Fabien], Helbert, D.[David], Cances, J.P.[Jean Pierre],
End-to-End Video Snapshot Compressive Imaging using Video Transformers,
IPTA22(1-6)
IEEE DOI 2206
Deep learning, Image coding, Memory management, Imaging, Streaming media, Reconstruction algorithms, Transformers, video compressive sensing BibRef

Anirudh, R.[Rushil], Lohit, S.[Suhas], Turaga, P.[Pavan],
Generative Patch Priors for Practical Compressive Image Recovery,
WACV21(2534-2544)
IEEE DOI 2106
Image coding, Computational modeling, Generators, Sensors, Compressed sensing BibRef

Yu, H.Y.[Hui-Yuan], Cheng, M.[Maggie], Lu, Y.D.[Ying-Dong],
A Randomized Algorithm for Sparse Recovery,
ICPR21(8312-8319)
IEEE DOI 2105
Approximation algorithms, Sparse matrices, Optimization, Compressed sensing, Convergence BibRef

Belyaev, E.,
Compressive Sensed Video Coding Having JPEG Compatibility,
ICIP20(1128-1132)
IEEE DOI 2011
Encoding, Streaming media, Codecs, Video coding, Transform coding, Sensors, Loss measurement, video coding, compressive sensing BibRef

Sogabe, Y., Sugimoto, S., Kurozumi, T., Kimata, H.,
ADMM-Inspired Reconstruction Network for Compressive Spectral Imaging,
ICIP20(2865-2869)
IEEE DOI 2011
Image reconstruction, Hyperspectral imaging, Convex functions, Image coding, Imaging, Convergence, Iterative methods, Compressed Sensing BibRef

Li, W., Li, S., Liu, R.,
Channel Shuffle Reconstruction Network for Image Compressive Sensing,
ICIP20(2880-2884)
IEEE DOI 2011
Indexes, Economic indicators, Image compressive sensing, Inverted residual, Channel shuffle, Multi-scale BibRef

Meng, Z.Y.[Zi-Yi], Ma, J.W.[Jia-Wei], Yuan, X.[Xin],
End-to-end Low Cost Compressive Spectral Imaging with Spatial-spectral Self-attention,
ECCV20(XXIII:187-204).
Springer DOI 2011
BibRef

Bobin, J., Candes, E.J.,
A fast and accurate first-order algorithm for compressed sensing,
ICIP09(1457-1460).
IEEE DOI 0911
BibRef

Das, R., Rajwade, A.,
Nonlinear Blind Compressed Sensing Under Signal-Dependent Noise,
ICIP19(2030-2034)
IEEE DOI 1910
Blind Compressed Sensing, Anscombe Transform, Multiplicative Update, Performance Bounds. BibRef

Gao, Z., Ding, L., Xiong, C., Gong, Z., Xiong, Q.,
Compressive Sensing Reconstruction Based on Standardized Group Sparse Representation,
ICIP19(2095-2099)
IEEE DOI 1910
Compressive sensing reconstruction, non local sparsity, group sparse representation, z-score standardization BibRef

Asif, M.S., Prater-Bennette, A.,
Multilinear Compressive Sensing With Tensor Ring Factorization,
ICIP19(2100-2104)
IEEE DOI 1910
BibRef

Hubbard-Featherstone, C.J., Garcia, M.A., Lee, W.Y.L.,
Adaptive block compressive sensing for image compression,
IVCNZ17(1-6)
IEEE DOI 1902
compressed sensing, image coding, image reconstruction, image sampling, minimisation, transform coding, wavelet transforms, discrete wavelet transform BibRef

Yoshida, M.[Michitaka], Torii, A.[Akihiko], Okutomi, M.[Masatoshi], Endo, K.[Kenta], Sugiyama, Y.[Yukinobu], Taniguchi, R.I.[Rin-Ichiro], Nagahara, H.[Hajime],
Joint Optimization for Compressive Video Sensing and Reconstruction Under Hardware Constraints,
ECCV18(X: 649-663).
Springer DOI 1810
BibRef

Güngör, A., Kar, O.F., Güven, H.E.,
A Matrix-Free Reconstruction Method for Compressive Focal Plane Array Imaging,
ICIP18(1827-1831)
IEEE DOI 1809
Image reconstruction, Convergence, Image resolution, Cameras, Sensor arrays, Complexity theory, Compressed Sensing, Single Pixel Camera BibRef

Akbari, A., Trocan, M.,
Robust Image Reconstruction for Block-Based Compressed Sensing Using a Binary Measurement Matrix,
ICIP18(1832-1836)
IEEE DOI 1809
Image reconstruction, Matrix decomposition, Transforms, Reconstruction algorithms, Sensors, Sparse matrices, Robustness, image CS reconstruction BibRef

Guimarães, J.P.F.[João P. F.], Fontes, A.I.R.[Aluisio I. R.], da Silva, F.B.[Felipe B.], de M. Martins, A.[Allan], von Borries, R.[Ricardo],
Complex Correntropy Induced Metric Applied to Compressive Sensing with Complex-Valued Data,
Southwest18(21-24)
IEEE DOI 1809
Measurement, Kernel, Compressed sensing, Image reconstruction, Robustness, Minimization, Approximation to l0, compressive sensing BibRef

Bernal, E.A., Li, Q.,
Tensorial compressive sensing of jointly sparse matrices with applications to color imaging,
ICIP17(2781-2785)
IEEE DOI 1803
Color, Compressed sensing, Image color analysis, Image reconstruction, Sparse matrices, Task analysis, tensorial compressive sensing BibRef

Li, Q.[Qun], Bernal, E.A.[Edgar A.],
An Algorithm for Parallel Reconstruction of Jointly Sparse Tensors with Applications to Hyperspectral Imaging,
PBVS17(218-225)
IEEE DOI 1709
Complexity theory, Hyperspectral imaging, Image reconstruction, Matrix decomposition, Niobium, Tensile, stress BibRef

Ren, M.J.[Meng-Jie], Chen, S.[Shenpei], Chen, D.[Dong],
Research on missile-borne image transmission technology based on compressive sensing,
ICIVC17(773-777)
IEEE DOI 1708
Bandwidth, Encoding, Image coding, Image reconstruction, Sensors, compressive sensing, missile-borne image, transmission, technology BibRef

Sato, S., Wakai, N., Nobori, K., Azuma, T., Miyata, T., Nakashizuka, M.,
Compressive color sensing using random complementary color filter array,
MVA17(43-46)
DOI Link 1708
Color, Compressed sensing, Image color analysis, Image edge detection, Image reconstruction, Imaging, Optical, filters BibRef

Wang, Z.E.[Zhong-Eng], Chen, S.F.[Shou-Fa],
Performance comparison of image block compressive sensing based on chaotic sensing matrix using different basis matrices,
ICIVC17(620-623)
IEEE DOI 1708
Compressed sensing, Discrete Fourier transforms, Discrete cosine transforms, Discrete wavelet transforms, Image reconstruction, Sparse matrices, compressive sensing, image processing, peak signal-to-noise ratio, sparse, basis, matrix BibRef

Jellali, Z.[Zakia], Atallah, L.N.[Leïla Najjar], Cherif, S.[Sofiane],
Data acquisition by 2D compression and ID reconstruction techniques for WSN spatially correlated data,
ISIVC16(224-229)
IEEE DOI 1704
Correlation BibRef

Kulkarni, K.[Kuldeep], Lohit, S.[Suhas], Turaga, P.K.[Pavan K.], Kerviche, R.[Ronan], Ashok, A.[Amit],
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements,
CVPR16(449-458)
IEEE DOI 1612
BibRef

Li, W.H., Yang, C.L., Ma, L.H.,
A multihypothesis-based residual reconstruction scheme in compressed video sensing,
ICIP17(2766-2770)
IEEE DOI 1803
Indexes, SPL algorithm, compressed video sensing, multihypothesis, residual reconstruction BibRef

Ou, W.F., Yang, C.L., Li, W.H., Ma, L.H.,
A two-stage multi-hypothesis reconstruction scheme in compressed video sensing,
ICIP16(2494-2498)
IEEE DOI 1610
Adaptation models BibRef

Zhao, C., Zhang, J., Ma, S., Xiong, R., Gao, W.,
A dual structured-sparsity model for compressive-sensed video reconstruction,
VCIP15(1-4)
IEEE DOI 1605
Compressed sensing BibRef

Ebrahim, M., Chai, W.C.,
Multi-phase joint reconstruction framework for multi-view video compression using block-based compressive sensing,
VCIP15(1-4)
IEEE DOI 1605
Compressed sensing BibRef

Xu, J., Djahel, S., Qiao, Y., Fu, Z.,
Perceptually-aware distributed compressive video sensing,
VCIP15(1-4)
IEEE DOI 1605
Codecs BibRef

Zhang, Y., Comerford, L., Beer, M., Kougioumtzoglou, I.,
Compressive sensing for power spectrum estimation of multi-dimensional processes under missing data,
WSSIP15(162-165)
IEEE DOI 1603
compressed sensing BibRef

Ebadi, S.E., Izquierdo, E.,
Approximated RPCA for fast and efficient recovery of corrupted and linearly correlated images and video frames,
WSSIP15(49-52)
IEEE DOI 1603
compressed sensing BibRef

Chen, B., Perona, P.,
Seeing into Darkness: Scotopic Visual Recognition,
CVPR17(7292-7301)
IEEE DOI 1711
BibRef
Earlier:
Scotopic Visual Recognition,
Extreme15(659-662)
IEEE DOI 1602
Cameras, Photonics, Robustness, Sensors, Visualization. Computational modeling. From a small number of photons. BibRef

Che, W.B.[Wen-Bin], Gao, X.W.[Xin-Wei], Fan, X.P.[Xiao-Peng], Jiang, F.[Feng], Zhao, D.B.[De-Bin],
Spatial-temporal recovery for hierarchical frame based video compressed sensing,
ICIP15(1110-1114)
IEEE DOI 1512
Video compressed sensing BibRef

Francis, K.J., Rajalakshmi, P., Channappayya, S.S.[Sumohana S.],
Distributed compressed sensing for photo-acoustic imaging,
ICIP15(1513-1517)
IEEE DOI 1512
Distributed Compressive Sensing BibRef

Tran, D.N.[Dung N.], Tran, D.N.[Duyet N.], Chin, S.P.[Sang Peter], Tran, T.D.[Trac D.],
Local sensing with global recovery,
ICIP15(4313-4316)
IEEE DOI 1512
Compressed sensing; imaging; sparse representation BibRef

Wang, J.[Jian], Gupta, M., Sankaranarayanan, A.C.,
LiSens- A Scalable Architecture for Video Compressive Sensing,
ICCP15(1-9)
IEEE DOI 1511
data compression BibRef

Spinoulas, L.[Leonidas], Cossairt, O.[Oliver], Katsaggelos, A.K.[Aggelos K.],
Sampling optimization for on-chip compressive video,
ICIP15(3329-3333)
IEEE DOI 1512
CMOS sensor; Sampling optimization; compressive sensing; high-speed video BibRef

Spinoulas, L.[Leonidas], He, K.[Kuan], Cossairt, O.[Oliver], Katsaggelos, A.K.[Aggelos K.],
Video compressive sensing with on-chip programmable subsampling,
CCD15(49-57)
IEEE DOI 1510
Cameras BibRef

Chen, H.[Huaijin], Asif, M.S.[M.Salman], Sankaranarayanan, A.C.[Aswin C.], Veeraraghavan, A.[Ashok],
FPA-CS: Focal plane array-based compressive imaging in short-wave infrared,
CVPR15(2358-2366)
IEEE DOI 1510
BibRef

Sato, S.[Satoshi], Ishii, M.[Motonori], Kato, Y.[Yoshihisa], Nobori, K.[Kunio], Azuma, T.[Takeo],
Compressive sensing reconstruction using collaborative sparsity among color channels,
MVA15(406-409)
IEEE DOI 1507
Cameras BibRef

Mourchid, Y., El Hassouni, M.,
Comparative study between different bases of transformation for compressive sensing of images,
ISCV15(1-7)
IEEE DOI 1506
compressed sensing BibRef

Dong, H.F.[Hai-Feng], Zhuang, B.[Bojin], Su, F.[Fei], Zhao, Z.C.[Zhi-Cheng],
A novel distributed compressive video sensing based on hybrid sparse basis,
VCIP14(320-323)
IEEE DOI 1504
compressed sensing BibRef

Zhao, C.[Chen], Ma, S.W.[Si-Wei], Gao, W.[Wen],
Video compressive sensing via structured Laplacian modelling,
VCIP14(402-405)
IEEE DOI 1504
Laplace equations BibRef

Kerviche, R.[Ronan], Zhu, N.[Nan], Ashok, A.[Amit],
Information optimal scalable compressive imager demonstrator,
ICIP14(2177-2179)
IEEE DOI 1502
System delivers high-resolution images from low resolution sensor with near real-time snapshots. BibRef

Hou, Y.[Ying], Zhang, Y.N.[Yan-Ning],
Effective Image Block Compressed Sensing,
ICPR14(1085-1090)
IEEE DOI 1412
Compressed sensing BibRef

Li, Y.[Yong], Xiong, H.K.[Hong-Kai], Ye, X.W.[Xin-Wei],
Compressive video sampling from a union of data-driven subspaces,
VCIP13(1-6)
IEEE DOI 1402
compressed sensing Sample and recover an unknown signal from a union of data-driven subspaces. BibRef

Li, X.W.[Xiang-Wei], Lan, X.G.[Xu-Guang], Yang, M.[Meng], Xue, J.R.[Jian-Ru], Zheng, N.N.[Nan-Ning],
Optimized truncation model for adaptive compressive sensing acquisition of images,
VCIP15(1-4)
IEEE DOI 1605
BibRef
Earlier:
Universal and low-complexity quantizer design for compressive sensing image coding,
VCIP13(1-5)
IEEE DOI 1402
Adaptation models. codecs BibRef

Dinh, K.Q.[Khanh Quoc], Shim, H.J.[Hiuk Jae], Jeon, B.W.[Byeung-Woo],
Measurement coding for compressive imaging using a structural measuremnet matrix,
ICIP13(10-13)
IEEE DOI 1402
Compressed sensing BibRef

Iliadis, M.[Michael], Watt, J.[Jeremy], Spinoulas, L.[Leonidas], Katsaggelos, A.K.[Aggelos K.],
Video compressive sensing using multiple measurement vectors,
ICIP13(136-140)
IEEE DOI 1402
Compressed sensing BibRef

Anaraki, F.P.[Farhad Pourkamali], Hughes, S.M.[Shannon M.],
Kernel compressive sensing,
ICIP13(494-498)
IEEE DOI 1402
Compressed sensing BibRef

Yang, F.[Fei], Jiang, H.[Hong], Shen, Z.[Zuowei], Deng, W.[Wei], Metaxas, D.N.[Dimitris N.],
Adaptive low rank and sparse decomposition of video using compressive sensing,
ICIP13(1016-1020)
IEEE DOI 1402
Cameras BibRef

Zhang, J.[Jian], Zhao, D.B.[De-Bin], Jiang, F.[Feng],
Spatially directional predictive coding for block-based compressive sensing of natural images,
ICIP13(1021-1025)
IEEE DOI 1402
Bit rate BibRef

Hua, B.S.[Binh-Son], Sato, I.[Imari], Low, K.L.[Kok-Lim],
Direct and progressive reconstruction of dual photography images,
ICIP13(3157-3161)
IEEE DOI 1402
compressive sensing;dual photography BibRef

Chu, X.Y.[Xiao-Yu], Stamm, M.C.[Matthew C.], Liu, K.J.R.[K. J. Ray],
Forensic identification of compressively sensed signals,
ICIP12(257-260).
IEEE DOI 1302
BibRef

Zhang, X.Y.[Xin-Yu], Wen, J.T.[Jiang-Tao],
Compressive video sensing using non-linear mapping,
ICIP12(885-888).
IEEE DOI 1302
BibRef

Rao, N.S.[Nikhil S.], Nowak, R.D.[Robert D.],
Correlated gaussian designs for compressive imaging,
ICIP12(921-924).
IEEE DOI 1302
BibRef

Golbabaee, M.[Mohammad], Vandergheynst, P.[Pierre],
Joint trace/TV norm minimization: A new efficient approach for spectral compressive imaging,
ICIP12(933-936).
IEEE DOI 1302
BibRef

Kumar, N.R., Xiang, W.[Wei], Soar, J.,
A Novel Image Compressive Sensing Method Based on Complex Measurements,
DICTA11(175-179).
IEEE DOI 1205
BibRef

Papandreou, G.[George], Yuille, A.L.[Alan L.],
Efficient variational inference in large-scale Bayesian compressed sensing,
ITCVPR11(1332-1339).
IEEE DOI 1201
BibRef

Shu, X.B.[Xian-Biao], Yang, J.C.[Jian-Chao], Ahuja, N.,
Non-local compressive sampling recovery,
ICCP14(1-8)
IEEE DOI 1411
compressed sensing BibRef

Shu, X.B.[Xian-Biao], Ahuja, N.[Narendra],
Imaging via three-dimensional compressive sampling (3DCS),
ICCV11(439-446).
IEEE DOI 1201
I.e. sample below Nyquist rate. A lot of theory, but no camera yet. BibRef

Le Montagner, Y.[Yoann], Marim, M.M.[Marcio M.], Angelini, E.D.[Elsa D.], Olivo-Marin, J.C.[Jean-Christophe],
Numerical evaluation of sampling bounds for near-optimal reconstruction in compressed sensing,
ICIP11(3073-3076).
IEEE DOI 1201
BibRef

Li, B.[Bin], Zhu, X.[Xuqi], Liu, Y.[Yu], Zhang, L.[Lin],
An unequally protected Distributed Compressed Video Sensing algorithm,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Huang, H.L.[Hong-Lin], Makur, A., Venkatraman, D.,
Video object error coding method based on compressive sensing,
ICARCV08(1287-1291).
IEEE DOI 1109
BibRef

Xiao, L.[Liang], Shao, J.[Jun], Huang, L.[Lili], Wei, Z.H.[Zhi-Hui],
Compounded Regularization and Fast Algorithm for Compressive Sensing Deconvolution,
ICIG11(616-621).
IEEE DOI 1109
BibRef

Lin, X.F.[Xiao-Fen], Lu, G.[Gang], Yan, J.W.[Jing-Wen], Lin, W.[Wei],
Measurement Matrix of Compressive Sensing Based on Gram-Schmidt Orthogonalization,
ICIG11(205-210).
IEEE DOI 1109
BibRef

Ren, Y.M.[Yue-Mei], Zhang, Y.N.[Yang-Ning], Li, Y.[Ying], Huang, J.Y.[Jian-Yu], Hui, J.J.[Jian-Jiang],
A Space Target Recognition Method Based on Compressive Sensing,
ICIG11(582-586).
IEEE DOI 1109
BibRef

Borghi, A., Darbon, J., Peyronnet, S., Chan, T.F.[Tony F.], Osher, S.J.[Stanley J.],
A Compressive Sensing Algorithm for Many-Core Architectures,
ISVC10(II: 678-686).
Springer DOI 1011
BibRef

Trocan, M.[Maria], Maugey, T.[Thomas], Tramel, E.W.[Eric W.], Fowler, J.E.[James E.], Pesquet-Popescu, B.[Beatrice],
Compressed sensing of multiview images using disparity compensation,
ICIP10(3345-3348).
IEEE DOI 1009
BibRef

Zhang, X.[Xin], Lam, E.Y.[Edmund Y.],
Sectional image reconstruction in optical scanning holography using compressed sensing,
ICIP10(3349-3352).
IEEE DOI 1009
BibRef

He, Z.X.[Zai-Xing], Ogawa, T.[Takahiro], Haseyama, M.[Miki],
The simplest measurement matrix for compressed sensing of natural images,
ICIP10(4301-4304).
IEEE DOI 1009
BibRef

Majumdar, A.[Angshul], Ward, R.K.[Rabab K.],
Compressive color imaging with group-sparsity on analysis prior,
ICIP10(1337-1340).
IEEE DOI 1009
BibRef

Shang, F.[Fei], Du, H.Q.[Hui-Qian], Jia, Y.D.[Yun-De],
Compressive Sampling Recovery for Natural Images,
ICPR10(2206-2209).
IEEE DOI 1008
BibRef

Yamamoto, S.[Satoshi], Itakura, Y.[Yasumasa], Sawabe, M.[Masashi],
Precomputed ROMP for light transport acquisition,
PROCAMS10(49-56).
IEEE DOI 1006
Compressive sensing. BibRef

Chang, H.S.[Hyun Sung], Weiss, Y.[Yair], Freeman, W.T.[William T.],
Informative sensing of natural images,
ICIP09(3025-3028).
IEEE DOI 0911
different random sampling. BibRef

Mun, S.K.[Sung-Kwang], Fowler, J.E.[James E.],
Block compressed sensing of images using directional transforms,
ICIP09(3021-3024).
IEEE DOI 0911
BibRef

Patel, V.M.[Vishal M.], Easley, G.R.[Glenn R.], Healy, D.M.[Dennis M.], Chellappa, R.[Rama],
Compressed sensing for Synthetic Aperture Radar imaging,
ICIP09(2141-2144).
IEEE DOI 0911
BibRef

Lu, W.[Wei], Vaswani, N.[Namrata],
Modified compressive sensing for real-time dynamic MR imaging,
ICIP09(3045-3048).
IEEE DOI 0911
BibRef

Zhang, X.[Xi], Wu, X.L.[Xiao-Lin],
Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton,
CVPR21(13349-13359)
IEEE DOI 2111
Deep learning, Image coding, Codes, Skeleton, Pattern recognition, Image reconstruction BibRef

Wu, X.L.[Xiao-Lin], Zhang, X.J.[Xiang-Jun],
Compressive-uniform hybrid sensing for image acquisition and communication,
ICIP09(3041-3044).
IEEE DOI 0911
BibRef

Wang, T.[Tao], Zhu, Z.G.[Zhi-Gang], Rhody, H.[Harvey],
A smart sensor with hyperspectral/range fovea and panoramic peripheral view,
OTCBVS09(98-105).
IEEE DOI 0906
BibRef

Wan, T.[Tao], Canagarajah, N.[Nishan], Achim, A.[Alin],
Compressive image fusion,
ICIP08(1308-1311).
IEEE DOI 0810
Sampling pattern. BibRef

Patel, V.M.[Vishal M.], Easley, G.R.[Glenn R.], Chellappa, R.[Rama], Healy, D.M.[Dennis M.],
Enhancing sparsity using gradients for compressive sensing,
ICIP09(3033-3036).
IEEE DOI 0911
BibRef

Hyder, M.M.[M. Mashud], Mahata, K.[Kaushik],
A fast decoder for Compressed Sensing based multiple description image coding,
ICIP09(2125-2128).
IEEE DOI 0911
BibRef

Schulz, A.[Adriana], Velho, L.[Luiz], da Silva, E.A.B.[Eduardo A.B.],
On the empirical rate-distortion performance of Compressive Sensing,
ICIP09(3049-3052).
IEEE DOI 0911
BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Coded Aperture Compressive Sensing .