5.5.9.1 Convolutional Network, Deep Networks, Learning for Compressive Sensing

Chapter Contents (Back)
Compressive Sensing. Convolutional Neural Networks. Deep Nets. Neural Networks. For most of it:
See also Compressive Sensing, Compressive Imaging, Compressed Sensing, Compression, Reconstruction. And the related:
See also Matching Pursuits, Video Coding.

Yu, N.Y.[Nam Yul], Gan, L.[Lu],
Convolutional Compressed Sensing Using Decimated Sidelnikov Sequences,
SPLetters(21), No. 5, May 2014, pp. 591-594.
IEEE DOI 1404
Gaussian processes BibRef

Fang, J., Li, J., Shen, Y., Li, H., Li, S.,
Super-Resolution Compressed Sensing: An Iterative Reweighted Algorithm for Joint Parameter Learning and Sparse Signal Recovery,
SPLetters(21), No. 6, June 2014, pp. 761-765.
IEEE DOI 1404
Compressed sensing BibRef

Yang, J.B.[Jian-Bo], Liao, X.J.[Xue-Jun], Yuan, X.[Xin], Llull, P.[Patrick], Brady, D.J.[David J.], Sapiro, G.[Guillermo], Carin, L.[Lawrence],
Compressive Sensing by Learning a Gaussian Mixture Model From Measurements,
IP(24), No. 1, January 2015, pp. 106-119.
IEEE DOI 1502
Gaussian processes BibRef
Earlier: A1, A3, A2, A4, A6, A5, A7:
Gaussian mixture model for video compressive sensing,
ICIP13(19-23)
IEEE DOI 1402
BibRef

Yang, J.B.[Jian-Bo], Yuan, X.[Xin], Liao, X.J.[Xue-Jun], Llull, P.[Patrick], Brady, D.J.[David J.], Sapiro, G.[Guillermo], Carin, L.[Lawrence],
Video Compressive Sensing Using Gaussian Mixture Models,
IP(23), No. 11, November 2014, pp. 4863-4878.
IEEE DOI 1410
BibRef
Earlier: A2, A1, A4, A3, A6, A5, A7:
Adaptive temporal compressive sensing for video,
ICIP13(14-18)
IEEE DOI 1402
Cameras BibRef

Schwartz, S.[Shimon], Wong, A.[Alexander], Clausi, D.A.[David A.],
Optimized sampling distribution based on nonparametric learning for improved compressive sensing performance,
JVCIR(31), No. 1, 2015, pp. 26-40.
Elsevier DOI 1508
Compressed sampling BibRef

Zayyani, H., Korki, M., Marvasti, F.,
Dictionary Learning for Blind One Bit Compressed Sensing,
SPLetters(23), No. 2, February 2016, pp. 187-191.
IEEE DOI 1602
compressed sensing. the original signal to be reconstructed from one bit linear random measurements is sparse in an unknown domain. BibRef

Zhang, L.[Lei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.], Shi, Q.F.[Qin-Feng],
Dictionary Learning for Promoting Structured Sparsity in Hyperspectral Compressive Sensing,
GeoRS(54), No. 12, December 2016, pp. 7223-7235.
IEEE DOI 1612
Bayes methods BibRef

Zhang, L.[Lei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.], Shi, Q.F.[Qin-Feng],
Cluster Sparsity Field: An Internal Hyperspectral Imagery Prior for Reconstruction,
IJCV(126), No. 8, August 2018, pp. 797-821.
Springer DOI 1807
BibRef
And:
Cluster Sparsity Field for Hyperspectral Imagery Denoising,
ECCV16(V: 631-647).
Springer DOI 1611
BibRef

Zhang, L.[Lei], Wei, W.[Wei], Tian, C.N.[Chun-Na], Li, F.[Fei], Zhang, Y.N.[Yan-Ning],
Exploring Structured Sparsity by a Reweighted Laplace Prior for Hyperspectral Compressive Sensing,
IP(25), No. 10, October 2016, pp. 4974-4988.
IEEE DOI 1610
BibRef
Earlier: A1, A2, A5, A3, A4:
Reweighted laplace prior based hyperspectral compressive sensing for unknown sparsity,
CVPR15(2274-2281)
IEEE DOI 1510
Bayes methods BibRef

Wei, W.[Wei], Zhang, L.[Lei], Tian, C.N.[Chun-Na], Plaza, A.[Antonio], Zhang, Y.N.[Yan-Ning],
Structured Sparse Coding-Based Hyperspectral Imagery Denoising With Intracluster Filtering,
GeoRS(55), No. 12, December 2017, pp. 6860-6876.
IEEE DOI 1712
Covariance matrices, Dictionaries, Encoding, Hyperspectral imaging, Noise measurement, Noise reduction, Tensile stress, structured sparse coding BibRef

Wang, C.[Cong], Zhang, L.[Lei], Wei, W.[Wei], Zhang, Y.[Yanning],
When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804
BibRef

Zhang, L.[Lei], Wei, W.[Wei], Zhang, Y.N.[Yan-Ning], Li, F.[Fei], Shen, C.H.[Chun-Hua], Shi, Q.F.[Qin-Feng],
Hyperspectral Compressive Sensing Using Manifold-Structured Sparsity Prior,
ICCV15(3550-3558)
IEEE DOI 1602
Correlation BibRef

Zhao, Z.F.[Zhi-Fu], Xie, X.M.[Xue-Mei], Wang, C.Y.[Chen-Ye], Mao, S.Y.[Si-Ying], Liu, W.[Wan], Shi, G.M.[Guang-Ming],
ROI-CSNet: Compressive sensing network for ROI-aware image recovery,
SP:IC(78), 2019, pp. 113-124.
Elsevier DOI 1909
Compressive sensing (CS), Region of interest (ROI), Convolutional neural network (CNN) BibRef

Xu, H., Zhang, C., Kim, I.,
Coupled Online Robust Learning of Observation and Dictionary for Adaptive Analog-to-Information Conversion,
SPLetters(26), No. 1, January 2019, pp. 139-143.
IEEE DOI 1901
compressed sensing, learning (artificial intelligence), optimisation, signal denoising, signal reconstruction, robust dictionary learning BibRef

Fu, W., Lu, T., Li, S.,
Context-Aware Compressed Sensing of Hyperspectral Image,
GeoRS(58), No. 1, January 2020, pp. 268-280.
IEEE DOI 2001
Image reconstruction, Dictionaries, Hyperspectral imaging, Imaging, Machine learning, Sparse matrices, Compressed sensing (CS), sparse reconstruction BibRef

Sun, Y.B.[Yu-Bao], Chen, J.W.[Ji-Wei], Liu, Q.S.[Qing-Shan], Liu, B.[Bo], Guo, G.D.[Guo-Dong],
Dual-Path Attention Network for Compressed Sensing Image Reconstruction,
IP(29), 2020, pp. 9482-9495.
IEEE DOI 1806
Image reconstruction, Optimization, Compressed sensing, Machine learning, Iterative methods, Periodic structures, texture attention BibRef

Sun, Y.B.[Yu-Bao], Yang, Y., Liu, Q.S.[Qing-Shan], Chen, J.W.[Ji-Wei], Yuan, X.T., Guo, G.D.[Guo-Dong],
Learning Non-Locally Regularized Compressed Sensing Network With Half-Quadratic Splitting,
MultMed(22), No. 12, December 2020, pp. 3236-3248.
IEEE DOI 2011
Image reconstruction, Image sequences, Compressed sensing, Training, Machine learning, Electronics packaging, half-quadratic splitting BibRef

Shi, W.Z.[Wu-Zhen], Jiang, F.[Feng], Liu, S.H.[Shao-Hui], Zhao, D.B.[De-Bin],
Image Compressed Sensing Using Convolutional Neural Network,
IP(29), No. 1, 2020, pp. 375-388.
IEEE DOI 1910
BibRef
Earlier:
Multi-Scale Deep Networks for Image Compressed Sensing,
ICIP18(46-50)
IEEE DOI 1809
compressed sensing, convolutional neural nets, image reconstruction, image sampling. Convolution, Training, Computational modeling, Image coding, Mean square error methods, BibRef

Shi, W.Z.[Wu-Zhen], Jiang, F.[Feng], Liu, S.H.[Shao-Hui], Zhao, D.B.[De-Bin],
Scalable Convolutional Neural Network for Image Compressed Sensing,
CVPR19(12282-12291).
IEEE DOI 2002
BibRef

Cui, W.X.[Wen-Xue], Jiang, F.[Feng], Gao, X.W.[Xin-Wei], Tao, W.[Wen], Zhao, D.B.[De-Bin],
Deep Neural Network Based Sparse Measurement Matrix for Image Compressed Sensing,
ICIP18(3883-3887)
IEEE DOI 1809
Image reconstruction, Sparse matrices, Training, Neural networks, Compressed sensing, Kernel, Memory management, Compressed sensing, sparsity BibRef

Yang, Y.[Yan], Sun, J.[Jian], Li, H.B.[Hui-Bin], Xu, Z.B.[Zong-Ben],
ADMM-CSNet: A Deep Learning Approach for Image Compressive Sensing,
PAMI(42), No. 3, March 2020, pp. 521-538.
IEEE DOI 2002
Image reconstruction, Transforms, Imaging, Task analysis, Computer architecture, Data models, Compressive sensing, ADMM-CSNet BibRef

Su, Y.M.[Yue-Ming], Lian, Q.S.[Qiu-Sheng],
iPiano-Net: Nonconvex optimization inspired multi-scale reconstruction network for compressed sensing,
SP:IC(89), 2020, pp. 115989.
Elsevier DOI 2010
iPiano algorithm, Compressed sensing, Convolutional neural network, Deep learning BibRef

Barajas-Solano, C.[Crisostomo], Ramirez, J.M.[Juan-Marcos], Arguello, H.[Henry],
Convolutional sparse coding framework for compressive spectral imaging,
JVCIR(66), 2020, pp. 102690.
Elsevier DOI 2003
Compressive spectral imaging, Convolutional sparse coding, Sparse representation, Spectral images BibRef

Rueda, H.[Hoover], Arguello, H.[Henry], Arce, G.R.[Gonzalo R.],
Dual-ARM VIS/NIR compressive spectral imager,
ICIP15(2572-2576)
IEEE DOI 1512
Compressive sensing BibRef


Chen, J.W.[Ji-Wei], Sun, Y.B.[Yu-Bao], Liu, Q.S.[Qing-Shan], Huang, R.[Rui],
Learning Memory Augmented Cascading Network for Compressed Sensing of Images,
ECCV20(XXII:513-529).
Springer DOI 2011
BibRef

Gupta, P.S., Yuan, X., Choi, G.S.,
DRCAS: Deep Restoration Network for Hardware Based Compressive Acquisition Scheme,
ICIP20(291-295)
IEEE DOI 2011
Image coding, Transform coding, Image resolution, Image restoration, Image reconstruction, Image sensors, Hardware, image restoration BibRef

Pei, H., Yang, C., Cao, Y.,
Deep Smoothed Projected Landweber Network for Block-Based Image Compressive Sensing,
ICIP20(2870-2874)
IEEE DOI 2011
Image reconstruction, Integrated circuits, Convolution, Transforms, Compressed sensing, Training, Gray-scale, image reconstruction BibRef

Yamada, M., Adachi, H., Horisaki, R., Sato, I.,
A Comparison of Compressed Sensing and DNN Based Reconstruction For Ghost Motion Imaging,
ICIP20(2910-2914)
IEEE DOI 2011
Image reconstruction, Optical imaging, Optical detectors, Detectors, Throughput, Ghost Imaging, Ghost Motion Imaging, Deep Learning BibRef

Yuan, X.[Xin], Ren, L.L.[Liang-Liang], Lu, J.W.[Ji-Wen], Zhou, J.[Jie],
Enhanced Bayesian Compression via Deep Reinforcement Learning,
CVPR19(6939-6948).
IEEE DOI 2002
BibRef

Xie, Y., Wang, Z., Pei, W., Tang, G.,
Fast Approximation of Non-Negative Sparse Recovery via Deep Learning,
ICIP19(2921-2925)
IEEE DOI 1910
Deep learning, algorithm approximation, non-negative sparse recovery, compressive sensing BibRef

Reddy K, P.K., Chaudhury, K.N.,
Learning Iteration-Dependent Denoisers for Model-Consistent Compressive Sensing,
ICIP19(2090-2094)
IEEE DOI 1910
compressive sensing, noise model, denoising, deep neural networks, consistency BibRef

Canh, T.N., Jeon, B.,
Difference of Convolution for Deep Compressive Sensing,
ICIP19(2105-2109)
IEEE DOI 1910
compressive sensing, deep learning, difference of Gaussian, difference of convolution BibRef

Liu, R., Li, S., Hou, C.,
An End-to-End Multi-Scale Residual Reconstruction Network for Image Compressive Sensing,
ICIP19(2070-2074)
IEEE DOI 1910
image compressive sensing, convolutional neural network, reconstruction, multi-scale, end-to-end BibRef

Benjilali, W., Guicquero, W., Jacques, L., Sicard, G.,
Hardware-Friendly Compressive Imaging Based on Random Modulations Permutations for Image Acquisition and Classification,
ICIP19(2085-2089)
IEEE DOI 1910
Compressive sensing, random modulations, random permutations, image sensor, machine learning BibRef

Cui, W.X.[Wen-Xue], Liu, S.H.[Shao-Hui], Zhang, S.P.[Sheng-Ping], Liu, Y.S.[Ya-Shu], Xu, H.Y.[He-Yao], Gao, X.W.[Xin-Wei], Jiang, F.[Feng], Zhao, D.B.[De-Bin],
Classification Guided Deep Convolutional Network for Compressed Sensing,
ICPR18(2905-2910)
IEEE DOI 1812
Image reconstruction, Feature extraction, Compressed sensing, Adaptation models, Loss measurement, Convolution, Image coding BibRef

Du, J.[Jiang], Xie, X.M.[Xue-Mei], Wang, C.Y.[Chen-Ye], Shi, G.M.[Guang-Ming],
Color Image Reconstruction with Perceptual Compressive Sensing,
ICPR18(1512-1517)
IEEE DOI 1812
Image color analysis, Color, Image reconstruction, Compressed sensing, Image resolution, Gray-scale, deep learning BibRef

Zhang, J., Ghanem, B.,
ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing,
CVPR18(1828-1837)
IEEE DOI 1812
Transforms, Image reconstruction, Optimization, Magnetic resonance imaging, Compressed sensing, Ear, Inverse problems BibRef

Dave, A., Kumar, A., Vadathya, Mitra, K.,
Compressive image recovery using recurrent generative model,
ICIP17(1702-1706)
IEEE DOI 1803
Cameras, Entropy, Image coding, Image reconstruction, Multiplexing, Sensors, Compressive imaging, LSTMs, MAP inference, deep learning, generative models BibRef

Yuan, X., Pu, Y.,
Convolutional factor analysis inspired compressive sensing,
ICIP17(550-554)
IEEE DOI 1803
Compressed sensing, Convex functions, Convolution, Dictionaries, Feature extraction, Image coding, Image reconstruction, image processing BibRef

Perdios, D., Besson, A., Rossinelli, P., Thiran, J.P.,
Learning the weight matrix for sparsity averaging in compressive imaging,
ICIP17(3056-3060)
IEEE DOI 1803
Image coding, Image reconstruction, Imaging, Iterative algorithms, Neural networks, Thresholding (Imaging), Training, fast iterative soft thresholding BibRef

Lohit, S.[Suhas], Kulkarni, K.[Kuldeep], Turaga, P.K.[Pavan K.],
Direct inference on compressive measurements using convolutional neural networks,
ICIP16(1913-1917)
IEEE DOI 1610
Correlation BibRef

Lohit, S.[Suhas], Kulkarni, K.[Kuldeep], Turaga, P.K.[Pavan K.], Wang, J.[Jian], Sankaranarayanan, A.C.[Aswin C.],
Reconstruction-free inference on compressive measurements,
CCD15(16-24)
IEEE DOI 1510
Correlation BibRef

Chapter on Image Processing, Restoration, Enhancement, Filters, Image and Video Coding continues in
Matching Pursuits, Video Coding .


Last update:Jan 17, 2021 at 16:22:28