18.4.3.5.1 Learning, Neural Networks for Single Image Super Resolution

Chapter Contents (Back)
Super Resolution. Neural Networks. Single Image Super Resolution.
See also Single Image Super Resolution.

Kim, K.I.[Kwang In], Kwon, Y.H.[Young-Hee],
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior,
PAMI(32), No. 6, June 2010, pp. 1127-1133.
IEEE DOI 1004
BibRef
Earlier:
Example-Based Learning for Single-Image Super-Resolution,
DAGM08(xx-yy).
Springer DOI 0806
Learn a map from low resolution images to target high resolution images based on examples of input and output images. BibRef

Kwon, Y.H.[Young-Hee], Kim, K.I.[Kwang In], Tompkin, J., Kim, J.H.[Jin H.], Theobalt, C.[Christian],
Efficient Learning of Image Super-Resolution and Compression Artifact Removal with Semi-Local Gaussian Processes,
PAMI(37), No. 9, September 2015, pp. 1792-1805.
IEEE DOI 1508
BibRef
Earlier: A1, A2, A4, A5, Only:
Efficient Learning-based Image Enhancement: Application to Super-resolution and Compression Artifact Removal,
BMVC12(14).
DOI Link 1301
Approximation methods BibRef

Park, J.K.[Jang-Kyun], Kwon, Y.H.[Young-Hee], Kim, J.H.[Jin Hyung],
An example-based prior model for text image super-resolution,
ICDAR05(I: 374-378).
IEEE DOI 0508
BibRef

Wang, Z.Y.[Zhang-Yang], Yang, Y.Z.[Ying-Zhen], Wang, Z.W.[Zhao-Wen], Chang, S.Y.[Shi-Yu], Yang, J.C.[Jian-Chao], Huang, T.S.,
Learning Super-Resolution Jointly From External and Internal Examples,
IP(24), No. 11, November 2015, pp. 4359-4371.
IEEE DOI 1509
image coding BibRef

Wu, W.[Wei], Liu, Z.[Zheng], He, X.H.[Xiao-Hai],
Learning-based super resolution using kernel partial least squares,
IVC(29), No. 6, May 2011, pp. 394-406.
Elsevier DOI 1104
Learning-based super resolution; High resolution image; Kernel partial least squares; Residual image BibRef

He, R.[Rui], Zhang, Z.Y.[Zhen-Yue],
Locally affine patch mapping and global refinement for image super-resolution,
PR(44), No. 9, September 2011, pp. 2210-2219.
Elsevier DOI 1106
Image super-resolution; Manifold learning; Tangent coordinate; Principal component analysis BibRef

Gao, X.B.[Xin-Bo], Zhang, K.B.[Kai-Bing], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Joint Learning for Single-Image Super-Resolution via a Coupled Constraint,
IP(21), No. 2, February 2012, pp. 469-480.
IEEE DOI 1201
BibRef

Zhang, K.B.[Kai-Bing], Tao, D.C.[Da-Cheng], Gao, X.B.[Xin-Bo], Li, X.L.[Xue-Long], Xiong, Z.G.[Zeng-Gang],
Learning Multiple Linear Mappings for Efficient Single Image Super-Resolution,
IP(24), No. 3, March 2015, pp. 846-861.
IEEE DOI 1502
BibRef
Earlier: A1, A3, A2, A4, Only:
Multi-scale dictionary for single image super-resolution,
CVPR12(1114-1121).
IEEE DOI 1208
image resolution BibRef

Lu, X.Q.[Xiao-Qiang], Yuan, H.L.[Hao-Liang], Yan, P.K.[Ping-Kun],
Image Super-Resolution Via Double Sparsity Regularized Manifold Learning,
CirSysVideo(23), No. 12, 2013, pp. 2022-2033.
IEEE DOI 1312
Dictionaries BibRef

Lu, X.Q.[Xiao-Qiang], Yuan, Y.[Yuan], Yan, P.K.[Ping-Kun],
Alternatively Constrained Dictionary Learning For Image Superresolution,
Cyber(44), No. 3, March 2014, pp. 366-377.
IEEE DOI 1404
dictionaries BibRef

Lu, X.Q.[Xiao-Qiang], Yuan, H.L.[Hao-Liang], Yan, P.K.[Ping-Kun], Yuan, Y.[Yuan], Li, X.L.[Xue-Long],
Geometry Constrained Sparse Coding for Single Image Super-Resolution,
CVPR12(1648-1655).
IEEE DOI 1208
BibRef

Tang, Y.[Yi], Yuan, Y.[Yuan], Yan, P.K.[Ping-Kun], Li, X.L.[Xue-Long],
Single-Image Super-Resolution via Sparse Coding Regression,
ICIG11(267-272).
IEEE DOI 1109
BibRef

Gao, X.B.[Xin-Bo], Wang, Q.[Qian], Li, X.L.[Xue-Long], Tao, D.C.[Da-Cheng], Zhang, K.[Kaibing],
Zernike-Moment-Based Image Super Resolution,
IP(20), No. 10, October 2011, pp. 2738-2747.
IEEE DOI 1110

See also Relay Level Set Method for Automatic Image Segmentation, A. BibRef

Zhang, K.B.[Kai-Bing], Gao, X.B.[Xin-Bo], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression,
IP(21), No. 11, November 2012, pp. 4544-4556.
IEEE DOI 1210
BibRef

Zhang, K.B.[Kai-Bing], Gao, X.B.[Xin-Bo], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Image Super-Resolution Via Non-Local Steering Kernel Regression Regularization,
ICIP13(943-946)
IEEE DOI 1402
Estimation BibRef

Gao, X.B.[Xin-Bo], Zhang, K.B.[Kai-Bing], Tao, D.C.[Da-Cheng], Li, X.L.[Xue-Long],
Image Super-Resolution with Sparse Neighbor Embedding,
IP(21), No. 7, July 2012, pp. 3194-3205.
IEEE DOI 1206
BibRef

Wang, H.J.[Hai-Jun], Gao, X.B.[Xin-Bo], Zhang, K.B.[Kai-Bing], Li, J.[Jie],
Single-Image Super-Resolution Using Active-Sampling Gaussian Process Regression,
IP(25), No. 2, February 2016, pp. 935-948.
IEEE DOI 1601
Computational modeling BibRef

Wang, H.J.[Hai-Jun], Gao, X.B.[Xin-Bo], Zhang, K.B.[Kai-Bing], Li, J.[Jie],
Single Image Super-Resolution Using Gaussian Process Regression With Dictionary-Based Sampling and Student-t Likelihood,
IP(26), No. 7, July 2017, pp. 3556-3568.
IEEE DOI 1706
Computational modeling, Dictionaries, Ground penetrating radar, Image reconstruction, Image resolution, Kernel, Training, Gaussian process regression, Super-resolution, dictionary-based sampling, student-t, likelihood BibRef

Mu, G.W.[Guang-Wu], Gao, X.B.[Xin-Bo], Zhang, K.B.[Kai-Bing], Li, X.L.[Xue-Long], Tao, D.C.[Da-Cheng],
Single Image Super Resolution with High Resolution Dictionary,
ICIP11(1141-1144).
IEEE DOI 1201
BibRef

Huang, Y.F.[Yuan-Fei], Li, J.[Jie], Gao, X.B.[Xin-Bo], He, L.[Lihuo], Lu, W.[Wen],
Single Image Super-Resolution via Multiple Mixture Prior Models,
IP(27), No. 12, December 2018, pp. 5904-5917.
IEEE DOI 1810
Feature extraction, Training, Image reconstruction, Testing, Principal component analysis, Image resolution, Machine learning, mixed matching BibRef

Hu, Y.T.[Yan-Ting], Li, J.[Jie], Huang, Y.F.[Yuan-Fei], Gao, X.B.[Xin-Bo],
Channel-Wise and Spatial Feature Modulation Network for Single Image Super-Resolution,
CirSysVideo(30), No. 11, November 2020, pp. 3911-3927.
IEEE DOI 2011
Spatial resolution, Modulation, Feature extraction, Neural networks, Blockchain, Image reconstruction, single image super-resolution BibRef

Huang, Y.F.[Yuan-Fei], Li, J.[Jie], Gao, X.B.[Xin-Bo], Hu, Y.T.[Yan-Ting], Lu, W.[Wen],
Interpretable Detail-Fidelity Attention Network for Single Image Super-Resolution,
IP(30), 2021, pp. 2325-2339.
IEEE DOI 2102
convolutional neural nets, deep learning (artificial intelligence), detail fidelity BibRef

Zhang, H.Y.[Hong-Yi], Lu, W.[Wen], Sun, X.P.[Xiao-Peng],
Cross-layer Information Refining Network for Single Image Super-Resolution,
ICPR21(1635-1640)
IEEE DOI 2105
Correlation, Superresolution, Refining, Benchmark testing, Feature extraction, Data mining BibRef

Bai, F.R.[Fu-Rui], Lu, W.[Wen], Zha, L.[Lin], Sun, X.P.[Xiao-Peng], Guan, R.X.[Ruo-Xuan],
Non-Local Hierarchical Residual Network for Single Image Super-Resolution,
ICIP19(2821-2825)
IEEE DOI 1910
Super resolution, CNNs, non-local module, hierarchical residual structure BibRef

Xin, J.W.[Jing-Wei], Wang, N.N.[Nan-Nan], Jiang, X.R.[Xin-Rui], Li, J.[Jie], Huang, H.[Heng], Gao, X.B.[Xin-Bo],
Binarized Neural Network for Single Image Super Resolution,
ECCV20(IV:91-107).
Springer DOI 2011
BibRef

Wang, R.[Rui], Lu, W.[Wen], Yang, J.C.[Jia-Chen], Huang, Y.F.[Yuan-Fei], Gao, X.B.[Xin-Bo], He, L.H.[Li-Huo],
Single Image Super Resolution Based on Deep Residual Network via Lateral Modules,
ICIP18(3568-3572)
IEEE DOI 1809
Training, Feature extraction, Image resolution, Image reconstruction, Convolutional neural networks, Testing, Variable channels BibRef

Hui, Z.[Zheng], Wang, X.M.[Xiu-Mei], Gao, X.B.[Xin-Bo],
Fast and Accurate Single Image Super-Resolution via Information Distillation Network,
CVPR18(723-731)
IEEE DOI 1812
BibRef
And:
Two-Stage Convolutional Network for Image Super-Resolution,
ICPR18(2670-2675)
IEEE DOI 1812
Feature extraction, Convolution, Image resolution, Image reconstruction, Convolutional neural networks, Image restoration Convolution, Image, Computational modeling, Feature extraction BibRef

Tang, Y.[Yi], Yuan, Y.[Yuan], Yan, P.K.[Ping-Kun], Li, X.L.[Xue-Long],
Greedy Regression in Sparse Coding Space for Single-Image Super-Resolution,
JVCIR(24), No. 2, February 2013, pp. 148-159.
Elsevier DOI 1302
Image quality improvement; Super-resolution; Sparsity; Nonlinear coding; Machine learning; Empirical risk minimization; Greedy regression; L2-Boosting BibRef

Guo, K., Yang, X., Lin, W., Zhang, R., Yu, S.,
Learning-based super-resolution method with a combining of both global and local constraints,
IET-IPR(6), No. 4, 2012, pp. 337-344.
DOI Link 1205

See also Multiscale Semilocal Interpolation With Antialiasing. BibRef

Haris, M.[Muhammad], Widyanto, M.R.[M. Rahmat], Nobuhara, H.[Hajime],
First-order derivative-based super-resolution,
SIViP(11), No. 1, January 2017, pp. 1-8.
WWW Link. 1702
BibRef

Haris, M.[Muhammad], Shakhnarovich, G.[Gregory], Ukita, N.[Norimichi],
Recurrent Back-Projection Network for Video Super-Resolution,
CVPR19(3892-3901).
IEEE DOI 2002
BibRef
Earlier:
Deep Back-Projection Networks for Super-Resolution,
CVPR18(1664-1673)
IEEE DOI 1812
Image reconstruction, Feature extraction, Task analysis, Training, Computer architecture, Spatial resolution BibRef

Bhowmik, A., Shit, S., Seelamantula, C.S.,
Training-Free, Single-Image Super-Resolution Using a Dynamic Convolutional Network,
SPLetters(25), No. 1, January 2018, pp. 85-89.
IEEE DOI 1801
Gaussian processes, Laplace equations, image representation, image resolution, Gaussian pyramids, HR ? LR generative model, super-resolution (SR) BibRef

Deng, X.,
Enhancing Image Quality via Style Transfer for Single Image Super-Resolution,
SPLetters(25), No. 4, April 2018, pp. 571-575.
IEEE DOI 1804
image enhancement, image reconstruction, image resolution, GAN based SISR method, SRGAN, generative adversarial network, style transfer BibRef

Haut, J.M., Fernandez-Beltran, R., Paoletti, M.E., Plaza, J., Plaza, A., Pla, F.,
A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution,
GeoRS(56), No. 11, November 2018, pp. 6792-6810.
IEEE DOI 1811
Spatial resolution, Remote sensing, Image reconstruction, Data models, Imaging, Training, super-resolution (SR) BibRef

Lu, T.[Tao], Wang, J.M.[Jia-Ming], Zhang, Y.D.[Yan-Duo], Wang, Z.Y.[Zhong-Yuan], Jiang, J.J.[Jun-Jun],
Satellite Image Super-Resolution via Multi-Scale Residual Deep Neural Network,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Lu, T.[Tao], Wang, Y.[Yu], Wang, J.M.[Jia-Ming], Liu, W.[Wei], Zhang, Y.D.[Yan-Duo],
Single Image Super-Resolution via Multi-Scale Information Polymerization Network,
SPLetters(28), 2021, pp. 1305-1309.
IEEE DOI 2107
Feature extraction, Polymers, Convolution, Image reconstruction, Data mining, Superresolution, Kernel, Convolution neural network, image super-resolution BibRef

Yang, J.C.[Jian-Chao], Wright, J.[John], Huang, T.S.[Thomas S.], Ma, Y.[Yi],
Image Super-Resolution Via Sparse Representation,
IP(19), No. 11, November 2010, pp. 2861-2873.
IEEE DOI 1011
BibRef
Earlier:
Image super-resolution as sparse representation of raw image patches,
CVPR08(1-8).
IEEE DOI 0806
Single image super-resolution based on sparse signal representation. Patch based. BibRef

Liu, D.[Ding], Wang, Z.Y.[Zhang-Yang], Wen, B., Yang, J.C.[Jian-Chao], Han, W.[Wei], Huang, T.S.[Thomas S.],
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior,
IP(25), No. 7, July 2016, pp. 3194-3207.
IEEE DOI 1606
BibRef
Earlier: A2, A1, A4, A5, A6, Only:
Deep Networks for Image Super-Resolution with Sparse Prior,
ICCV15(370-378)
IEEE DOI 1602
image coding. Dictionaries BibRef

Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T.S., Yan, S.,
Sparse Representation for Computer Vision and Pattern Recognition,
PIEEE(98), No. 6, June 2010, pp. 1031-1044.
IEEE DOI 1006
BibRef

Liu, D.[Ding], Wang, Z.W.[Zhao-Wen], Nasrabadi, N.[Nasser], Huang, T.S.[Thomas S.],
Learning a Mixture of Deep Networks for Single Image Super-Resolution,
ACCV16(III: 145-156).
Springer DOI 1704
BibRef

Wang, Z.Y.[Zhang-Yang], Chang, S.Y.[Shi-Yu], Yang, Y.Z.[Ying-Zhen], Liu, D., Huang, T.S.[Thomas S.],
Studying Very Low Resolution Recognition Using Deep Networks,
CVPR16(4792-4800)
IEEE DOI 1612
BibRef

Wang, Z.Y.[Zhang-Yang], Yang, Y.Z.[Ying-Zhen], Wang, Z.W.[Zhao-Wen], Chang, S.Y.[Shi-Yu], Han, W.[Wei], Yang, J.C.[Jian-Chao], Huang, T.S.[Thomas S.],
Self-tuned deep super resolution,
DeepLearn15(1-8)
IEEE DOI 1510
Adaptation models BibRef

Kim, C.H.[Chang-Hyun], Choi, K.[Kyuha], Ra, J.B.[Jong Beom],
Example-Based Super-Resolution via Structure Analysis of Patches,
SPLetters(20), No. 4, April 2013, pp. 407-410.
IEEE DOI 1303
BibRef
Earlier:
Improvement on learning-based super-resolution by adopting residual information and patch reliability,
ICIP09(1197-1200).
IEEE DOI 0911
BibRef

Yang, M.C.[Min-Chun], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
A Self-Learning Approach to Single Image Super-Resolution,
MultMed(15), No. 3, 2013, pp. 498-508.
IEEE DOI 1303
BibRef

Huang, D.A.[De-An], Kang, L.W.[Li-Wei], Wang, Y.C.F., Lin, C.W.[Chia-Wen],
Self-Learning Based Image Decomposition With Applications to Single Image Denoising,
MultMed(16), No. 1, January 2014, pp. 83-93.
IEEE DOI 1402
Gaussian noise BibRef

Huang, D.A.[De-An], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Coupled Dictionary and Feature Space Learning with Applications to Cross-Domain Image Synthesis and Recognition,
ICCV13(2496-2503)
IEEE DOI 1403
BibRef

Tsai, C.Y.[Chih-Yun], Huang, D.A.[De-An], Yang, M.C.[Min-Chun], Kang, L.W.[Li-Wei], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Context-Aware Single Image Super-Resolution Using Locality-Constrained Group Sparse Representation,
VCIP12(1-6).
IEEE DOI 1302

See also Locality-Constrained Group Sparse Representation for Robust Face Recognition. BibRef

Yang, M.C.[Min-Chun], Wang, C.H.[Chang-Heng], Hu, T.Y.[Ting-Yao], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Learning context-aware sparse representation for single image super-resolution,
ICIP11(1349-1352).
IEEE DOI 1201
BibRef
Earlier: A1, A3, A2, A4:
Learning of context-aware single image super-resolution,
VCIP11(1-4).
IEEE DOI 1201
BibRef

Yang, M.C.[Ming-Chun], Chu, C.T.[Chao-Tsung], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Learning sparse image representation with support vector regression for single-image super-resolution,
ICIP10(1973-1976).
IEEE DOI 1009
BibRef

Su, H.[Heng], Jiang, N.[Nan], Wu, Y.[Ying], Zhou, J.[Jie],
Single image super-resolution based on space structure learning,
PRL(34), No. 16, 2013, pp. 2094-2101.
Elsevier DOI 1310
Single image super-resolution BibRef

Gou, S., Liu, S., Wu, Y., Jiao, L.,
Image super-resolution based on the pairwise dictionary selected learning and improved bilateral regularisation,
IET-IPR(10), No. 2, 2016, pp. 101-112.
DOI Link 1602
image reconstruction BibRef

Hu, X.Y.[Xi-Yuan], Peng, S.L.[Si-Long], Hwang, W.L.[Wen-Liang],
Learning adaptive interpolation kernels for fast single-image super resolution,
SIViP(8), No. 6, September 2014, pp. 1077-1086.
WWW Link. 1408
BibRef

Zhang, K.[Kai], Wang, B.Q.[Bao-Quan], Zuo, W.M.[Wang-Meng], Zhang, H.Z.[Hong-Zhi], Zhang, L.[Lei],
Joint Learning of Multiple Regressors for Single Image Super-Resolution,
SPLetters(23), No. 1, January 2016, pp. 102-106.
IEEE DOI 1601
Dictionaries
See also Active Self-Paced Learning for Cost-Effective and Progressive Face Identification. BibRef

Xiao, J.[Jin], Yong, H.W.[Hong-Wei], Zhang, L.[Lei],
Degradation Model Learning for Real-world Single Image Super-resolution,
ACCV20(II:84-101).
Springer DOI 2103
BibRef

Zhang, K.[Kai], Zuo, W.M.[Wang-Meng], Zhang, L.[Lei],
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations,
CVPR18(3262-3271)
IEEE DOI 1812
Degradation, Kernel, Spatial resolution, Noise level, Computational modeling, Training BibRef

Kumar, N., Sethi, A.,
Fast Learning-Based Single Image Super-Resolution,
MultMed(18), No. 8, August 2016, pp. 1504-1515.
IEEE DOI 1608
image reconstruction BibRef

Kumar, N., Sethi, A.,
Super Resolution by Comprehensively Exploiting Dependencies of Wavelet Coefficients,
MultMed(20), No. 2, February 2018, pp. 298-309.
IEEE DOI 1801
Hidden Markov models, Image reconstruction, Image resolution, Mathematical model, Training, Wavelet analysis, wavelet transform BibRef

Ren, C.[Chao], He, X.H.[Xiao-Hai], Teng, Q.Z.[Qi-Zhi], Wu, Y.Y.[Yuan-Yuan], Nguyen, T.Q.[Truong Q.],
Single Image Super-Resolution Using Local Geometric Duality and Non-Local Similarity,
IP(25), No. 5, May 2016, pp. 2168-2183.
IEEE DOI 1604
computer vision BibRef

Ren, C.[Chao], He, X.H.[Xiao-Hai], Pu, Y.F.[Yi-Fei], Nguyen, T.Q.[Truong Q.],
Enhanced Non-Local Total Variation Model and Multi-Directional Feature Prediction Prior for Single Image Super Resolution,
IP(28), No. 8, August 2019, pp. 3778-3793.
IEEE DOI 1907
convolutional neural nets, image enhancement, image reconstruction, image resolution, iterative methods, multi-directional feature prediction BibRef

Suo, S.Y.[Shi-Yao], He, X.H.[Xiao-Hai], Chen, H.G.[Hong-Gang], Xiong, S.H.[Shu-Hua], Teng, Q.Z.[Qi-Zhi],
Single image super resolution based on feature enhancement,
ICIVC17(473-477)
IEEE DOI 1708
Dictionaries, Feature extraction, Image reconstruction, Image resolution, Image restoration, Sparse matrices, Training, anchored neighborhood regression, feature enhancement, learning-based, single, image, super-resolution BibRef

Ren, C.[Chao], He, X.H.[Xiao-Hai], Nguyen, T.Q.[Truong Q.],
Single Image Super-Resolution via Adaptive High-Dimensional Non-Local Total Variation and Adaptive Geometric Feature,
IP(26), No. 1, January 2017, pp. 90-106.
IEEE DOI 1612
computational geometry BibRef

Chen, H.G.[Hong-Gang], He, X.H.[Xiao-Hai], Teng, Q.[Qizhi], Ren, C.[Chao],
Single image super resolution using local smoothness and nonlocal self-similarity priors,
SP:IC(43), No. 1, 2016, pp. 68-81.
Elsevier DOI 1604
Single image super resolution BibRef

Zhang, Y.L.[Yu-Lun], Gu, K.[Kaiyu], Zhang, Y.B.[Yong-Bing], Zhang, J.[Jian], Dai, Q.H.[Qiong-Hai],
Image super-resolution based on dictionary learning and anchored neighborhood regression with mutual incoherence,
ICIP15(591-595)
IEEE DOI 1512
Dictionary learning BibRef

Shen, T.[Tao], Zhang, Y.B.[Yong-Bing], Zhang, Y.L.[Yu-Lun], Wang, X., Wang, H., Dai, Q.H.[Qiong-Hai],
Decompressed video enhancement via accurate regression prior,
VCIP16(1-4)
IEEE DOI 1701
Coherence BibRef

Feng, Y.H.[Yi-Hui], Liu, X., Zhang, Y.B.[Yong-Bing], Dai, Q.H.[Qiong-Hai],
Single depth image super-resolution and denoising based on sparse graphs via structure tensor,
ICIP17(4063-4067)
IEEE DOI 1803
Dictionaries, Image edge detection, Image resolution, Noise measurement, Noise reduction, Signal resolution, super-resolution BibRef

Feng, Y.H.[Yi-Hui], Zhang, Y.B.[Yong-Bing], Zhang, Y.L.[Yu-Lun], Shen, T.[Tao], Dai, Q.H.[Qiong-Hai],
Single image super-resolution via projective dictionary learning with anchored neighborhood regression,
VCIP16(1-4)
IEEE DOI 1701
Algorithm design and analysis BibRef

Zhang, Y.L.[Yu-Lun], Zhang, Y.B.[Yong-Bing], Zhang, J.[Jian], Wang, H.Q.[Hao-Qian], Wang, X.Z.[Xing-Zheng], Dai, Q.H.[Qiong-Hai],
Adaptive local nonparametric regression for fast single image super-resolution,
VCIP15(1-4)
IEEE DOI 1605
Adaptation models BibRef

Choi, J.S.[Jae-Seok], Kim, M.C.[Mun-Churl],
Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings,
IP(26), No. 3, March 2017, pp. 1300-1314.
IEEE DOI 1703
image resolution BibRef

Choi, J.S.[Jae-Seok], Kim, M.C.[Mun-Churl],
Single Image Super-Resolution Using Lightweight CNN with Maxout Units,
ACCV18(VI:471-487).
Springer DOI 1906
BibRef

Choi, J.S.[Jae-Seok], Bae, S.H.[Sung-Ho], Kim, M.C.[Mun-Churl],
Single image super-resolution based on self-examples using context-dependent subpatches,
ICIP15(2835-2839)
IEEE DOI 1512
Lloyd-Max quantization BibRef

Tang, Y.[Yi], Shao, L.[Ling],
Pairwise Operator Learning for Patch-Based Single-Image Super-Resolution,
IP(26), No. 2, February 2017, pp. 994-1003.
IEEE DOI 1702
image resolution BibRef

Tang, Y.[Yi], Chen, H.[Hong],
Local operator estimation for single-image super-resolution,
ICWAPR15(39-44)
IEEE DOI 1511
estimation theory BibRef

Mokari, A.[Azade], Ahmadyfard, A.[Alireza],
Fast single image SR via dictionary learning,
IET-IPR(11), No. 2, February 2017, pp. 135-144.
DOI Link 1703
BibRef

Wei, X.[Xian], Li, Y.X.[Yuan-Xiang], Shen, H.[Hao], Xiang, W.D.[Wei-Dong], Murphey, Y.L.[Yi Lu],
Joint learning sparsifying linear transformation for low-resolution image synthesis and recognition,
PR(66), No. 1, 2017, pp. 412-424.
Elsevier DOI 1704
Sparse representation BibRef

Wei, X.[Xian], Li, Y.X.[Yuan-Xiang], Shen, H.[Hao], Kleinsteuber, M., Murphey, Y.L.[Yi Lu],
Joint learning dictionary and discriminative features for high dimensional data,
ICPR16(366-371)
IEEE DOI 1705
Cost function, Covariance matrices, Dictionaries, Encoding, Manifolds, Sparse matrices, Visualization BibRef

Huang, J.J., Siu, W.C.,
Learning Hierarchical Decision Trees for Single-Image Super-Resolution,
CirSysVideo(27), No. 5, May 2017, pp. 937-950.
IEEE DOI 1705
Data models, Dictionaries, Image reconstruction, Image resolution, Regression tree analysis, Training, Classification, decision tree, image processing, regression and training, single-image, super-resolution, (SR) BibRef

Wang, S.[Shuang], Yue, B.[Bo], Liang, X.F.[Xue-Feng], Jiao, L.C.[Li-Cheng],
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution,
IP(27), No. 3, March 2018, pp. 1086-1099.
IEEE DOI 1801
BibRef
Earlier: A2, A1, A3, A4:
Robust Noisy Image Super-Resolution Using l1-norm Regularization and Non-local Constraint,
NTIRE16(I: 34-49).
Springer DOI 1704
image resolution, learning (artificial intelligence), matrix decomposition, sparse matrices, external learning method, low-rank matrix decomposition BibRef

Wang, S.[Shuang], Lin, S.P.[Shao-Peng], Liang, X.F.[Xue-Feng], Yue, B.[Bo], Jiao, L.C.[Li-Cheng],
External and internal learning for single-image super-resolution,
ICIP15(128-132)
IEEE DOI 1512
External dictionary learning BibRef

Li, X.S.[Xue-Song], Cao, G.[Guo], Zhang, Y.Q.[You-Qiang], Wang, B.S.[Bi-Sheng],
Single image super-resolution via adaptive sparse representation and low-rank constraint,
JVCIR(55), 2018, pp. 319-330.
Elsevier DOI 1809
Super-resolution, Adaptive sparse representation, Self-similarity learning, Robust principal component analysis BibRef

Liu, H.[Heng], Fu, Z.[Zilin], Han, J.G.[Jun-Gong], Shao, L.[Ling], Liu, H.S.[Hong-Shen],
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convolutional neural nets, feature extraction, image fusion, image representation, image resolution, image texture, spindle block BibRef

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IEEE DOI 1802
Adaptation models, Adaptive systems, Machine learning, Motion compensation, Neural networks, deep neural networks. image motion analysis, image resolution, image sequences, learning (artificial intelligence), video signal processing, Optical imaging BibRef

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Denoise, then super resolution. BibRef

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Task analysis, Image resolution, Training, Correlation, Image reconstruction, Image restoration, Learning systems, cascade BibRef

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Manifold learning, Neighbor reconstruction, Super resolution BibRef

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Feature extraction, Image resolution, Image quality, Quality assessment, Signal resolution, Deep learning, Measurement, image enhancement BibRef

Yang, X., Mei, H., Zhang, J., Xu, K., Yin, B., Zhang, Q., Wei, X.,
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Feature extraction, Image reconstruction, Convolution, Image resolution, Interpolation, Databases, Visualization, large factors BibRef

Gu, J.[Jun], Sun, X.[Xian], Zhang, Y.[Yue], Fu, K.[Kun], Wang, L.[Lei],
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feature extraction, geophysical image processing, image classification, image reconstruction, image resolution, single image super-resolution (SISR) BibRef

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Image super-resolution, Dilated convolution, Dense network, Optimization BibRef

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IEEE DOI 1912
Computational modeling, Image reconstruction, Training, Image resolution, Convolutional codes, Encoding, deep convolutional neural networks BibRef

Yang, Y., Zhang, D., Huang, S., Wu, J.,
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convolutional neural nets, feature extraction, image fusion, image reconstruction, image representation, image resolution, channel-wise attention BibRef

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Image reconstruction, Image edge detection, Image resolution, Feature extraction, Task analysis, Convolutional neural networks, image restoration BibRef

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Deep learning, generative adversarial network, remote sensing, saliency, single-image superresolution (SISR), unequal reconstruction BibRef

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Convolution, Hyperspectral imaging, Image reconstruction, Solid modeling, super-resolution (SR) BibRef

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Spatial resolution, Matrix decomposition, Hyperspectral imaging, Image segmentation, Machine learning, superpixel BibRef

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Image super-resolution, Video super-resolution, Video Deblurring, Dual motion warping with attention, HFR-Net BibRef

Singh, V.[Vikram], Ramnath, K.[Keerthan], Arunachalam, S., Mittal, A.[Anurag],
Going Much Wider with Deep Networks for Image Super-Resolution,
WACV20(2332-2343)
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Image resolution, Shape, Architecture, Computer vision, Training, Computer architecture, Feature extraction BibRef

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E-ProSRNet: An enhanced progressive single image super-resolution approach,
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CNN, Single image super-resolution, Residual network, Computationally efficient, Charbonnier loss function BibRef

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Computer architecture, Microprocessors, Computational modeling, Convolution, Training, Interpolation, Aerospace electronics, Super-Resolution BibRef

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Feature extraction, Image reconstruction, Spatial resolution, Convolutional neural networks, Training, Graphics, single-image super-resolution (SISR) BibRef

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Super-resolution, Convolution neural network, Pyramidal network, Residual-feature learning BibRef

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Feature extraction, Convolution, Image reconstruction, Computational modeling, Task analysis, Spatial resolution, multi-scale dense connections BibRef

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Dai, T.[Tao], Zha, H.[Hua], Jiang, Y.[Yong], Xia, S.T.[Shu-Tao],
Image Super-Resolution via Residual Block Attention Networks,
CLI19(3879-3886)
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convolutional neural nets, image representation, image resolution, learning (artificial intelligence), CNNs, Attention BibRef

Geng, T.Y.[Tian-Yu], Liu, X.Y.[Xiao-Yang], Wang, X.D.[Xiao-Dong], Sun, G.L.[Gui-Ling],
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Superresolution, Transforms, Deep learning, Training, Remote sensing, Neural networks, Image reconstruction, convolutional neural network BibRef

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Superresolution, Degradation, Feature extraction, Task analysis, Image edge detection, Kernel, Image restoration, convolutional neural networks BibRef

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Remote sensing, Image reconstruction, Spatial resolution, Training, Signal resolution, Image sensors, super-resolution (SR) BibRef

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Superresolution, Image reconstruction, Feature extraction, Task analysis, Image edge detection, Correlation, patch-recurrence BibRef

Niu, B.[Ben], Wen, W.L.[Wei-Lei], Ren, W.Q.[Wen-Qi], Zhang, X.D.[Xiang-De], Yang, L.P.[Lian-Ping], Wang, S.Z.[Shu-Zhen], Zhang, K.H.[Kai-Hao], Cao, X.C.[Xiao-Chun],
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Liang, Y.D.[Yu-Dong], Timofte, R.[Radu], Wang, J.J.[Jin-Jun], Zhou, S.P.[San-Ping], Gong, Y.H.[Yi-Hong], Zheng, N.N.[Nan-Ning],
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Shen, Y.[Yan], Zhang, L.[Liao], Wang, Z.L.[Zhong-Li], Hao, X.L.[Xiao-Li], Hou, Y.L.[Ya-Li],
Multi-Level Residual Up-Projection Activation Network for Image Super-Resolution,
ICIP19(2841-2845)
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Super-Resolution, Deep Learning, Attention Mechanism, Residual Learning BibRef

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Image reconstruction, Noise reduction, Optimization, Image edge detection, Image resolution, Degradation, super-resolution (SR) BibRef

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PR(121), 2022, pp. 108206.
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Semi-supervised learning, Image super-resolution, Student-teacher model, Adversarial learning BibRef

Li, Z.[Zheng], Wang, C.F.[Chao-Feng], Wang, J.[Jun], Ying, S.H.[Shi-Hui], Shi, J.[Jun],
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Lightweight, Single-image super-resolution, Adaptive weighted super-resolution network BibRef


Zhou, H.Q.[Hang-Qi], Huang, C.[Chao], Gao, S.Q.[Shang-Qi], Zhuang, X.H.[Xia-Hai],
VSpSR: Explorable Super-Resolution via Variational Sparse Representation,
NTIRE21(373-381)
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Visualization, Dictionaries, Superresolution, Neural networks, Space exploration BibRef

Chen, W.M.[Wei-Min], Ma, Y.Q.[Yu-Qing], Liu, X.L.[Xiang-Long], Yuan, Y.[Yi],
Hierarchical Generative Adversarial Networks for Single Image Super-Resolution,
WACV21(355-364)
IEEE DOI 2106
Measurement, Visualization, Superresolution, Semantics, Feature extraction, Generative adversarial networks BibRef

Luo, Z.X.[Zheng-Xiong], Huang, Y.[Yan], Li, S.[Shang], Wang, L.[Liang], Tan, T.N.[Tie-Niu],
Efficient Super Resolution by Recursive Aggregation,
ICPR21(8592-8599)
IEEE DOI 2105
Image resolution, Aggregates, Neural networks BibRef

Hou, Z.J.[Ze-Jiang], Kung, S.Y.[Sun-Yuan],
Hierarchically Aggregated Residual Transformation for Single Image Super Resolution,
ICPR21(2248-2255)
IEEE DOI 2105
Visualization, Superresolution, Optimal control, Ordinary differential equations, Feature extraction, Numerical models BibRef

Gu, J.H.[Jia-Hang], Qu, Z.W.[Zhao-Wei], Wang, X.R.[Xiao-Ru], Dan, J.W.[Jia-Wang], Sun, J.W.[Jun-Wei],
Residual Fractal Network for Single Image Super Resolution by Widening and Deepening,
ICPR21(1596-1603)
IEEE DOI 2105
Convolutional codes, Training, Measurement, Visualization, Superresolution, Supervised learning, Feature extraction BibRef

Wei, S.[Shuo], Sun, X.[Xin], Zhao, H.R.[Hao-Ran], Dong, J.Y.[Jun-Yu],
RSAN: Residual Subtraction and Attention Network for Single Image Super-Resolution,
ICPR21(1-6)
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Measurement, Learning systems, Visualization, Superresolution, Benchmark testing, Pattern recognition, Image reconstruction BibRef

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Single Image Super-Resolution Via Residual Neuron Attention Networks,
ICIP20(1586-1590)
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Neurons, Feature extraction, RNA, Image resolution, Training, Image reconstruction, Mathematical model, global context BibRef

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Ladder Pyramid Networks For Single Image Super-Resolution,
ICIP20(578-582)
IEEE DOI 2011
Image resolution, Feature extraction, Convolutional neural networks, Computer architecture, Super-Resolution BibRef

Rad, M.S., Bozorgtabar, B., Marti, U., Basler, M., Ekenel, H.K., Thiran, J.,
SROBB: Targeted Perceptual Loss for Single Image Super-Resolution,
ICCV19(2710-2719)
IEEE DOI 2004
decoding, image resolution, image segmentation, image texture, neural nets, object detection, perceptual loss, Training BibRef

Deng, X., Yang, R., Xu, M., Dragotti, P.L.,
Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution,
ICCV19(3076-3085)
IEEE DOI 2004
convolutional neural nets, image reconstruction, image resolution, wavelet transforms, Histograms BibRef

He, X.Y.[Xiang-Yu], Mo, Z.[Zitao], Wang, P.S.[Pei-Song], Liu, Y.[Yang], Yang, M.Y.[Ming-Yuan], Cheng, J.[Jian],
ODE-Inspired Network Design for Single Image Super-Resolution,
CVPR19(1732-1741).
IEEE DOI 2002
BibRef

Park, D.[Dongwon], Kang, D.U.[Dong Un], Kim, J.[Jisoo], Chun, S.Y.[Se Young],
Multi-temporal Recurrent Neural Networks for Progressive Non-uniform Single Image Deblurring with Incremental Temporal Training,
ECCV20(VI:327-343).
Springer DOI 2011
BibRef

Guo, Y.[Yong], Chen, J.[Jian], Wang, J.D.[Jing-Dong], Chen, Q.[Qi], Cao, J.Z.[Jie-Zhang], Deng, Z.S.[Ze-Shuai], Xu, Y.W.[Yan-Wu], Tan, M.K.[Ming-Kui],
Closed-Loop Matters: Dual Regression Networks for Single Image Super-Resolution,
CVPR20(5406-5415)
IEEE DOI 2008
Data models, Image resolution, Adaptation models, Task analysis, Training, Image reconstruction, Training data BibRef

Prajapati, K.[Kalpesh], Chudasama, V.[Vishal], Patel, H.[Heena], Upla, K.[Kishor], Raja, K.[Kiran], Ramachandra, R.[Raghavendra], Busch, C.[Christoph],
Direct Unsupervised Super-Resolution Using Generative Adversarial Network (DUS-GAN) for Real-World Data,
IP(30), 2021, pp. 8251-8264.
IEEE DOI 2110
BibRef
Earlier:
Unsupervised Real-world Super-resolution Using Variational Auto-encoder and Generative Adversarial Network,
DLPR20(703-718).
Springer DOI 2103
BibRef
Earlier: A1, A2, A3, A4, A6, A5, A7:
Unsupervised Single Image Super-Resolution Network (USISResNet) for Real-World Data Using Generative Adversarial Network,
NTIRE20(1904-1913)
IEEE DOI 2008
Training, Degradation, Generative adversarial networks, Superresolution, Task analysis, Unsupervised learning, interpolation. Image resolution, Machine learning BibRef

Sidorov, O., Hardeberg, J.Y.,
Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution,
CLI19(3844-3851)
IEEE DOI 2004
convolutional neural nets, hyperspectral imaging, image denoising, image resolution, image restoration, Single image BibRef

Chang, M.[Meng], Li, Q.[Qi], Feng, H.J.[Hua-Jun], Xu, Z.H.[Zhi-Hai],
Spatial-adaptive Network for Single Image Denoising,
ECCV20(XXX: 171-187).
Springer DOI 2010
BibRef

Qiu, Y.J.[Ya-Jun], Wang, R.X.[Ru-Xin], Tao, D.P.[Da-Peng], Cheng, J.[Jun],
Embedded Block Residual Network: A Recursive Restoration Model for Single-Image Super-Resolution,
ICCV19(4179-4188)
IEEE DOI 2004
convolutional neural nets, image resolution, image restoration, image texture, recurrent neural nets, Learning systems BibRef

Zhu, F., Zhao, Q.,
Efficient Single Image Super-Resolution via Hybrid Residual Feature Learning with Compact Back-Projection Network,
LPCV19(2453-2460)
IEEE DOI 2004
image reconstruction, image resolution, learning (artificial intelligence), hybrid residual feature learning BibRef

Zhang, Z.[Zhifei], Wang, Z.[Zhaowen], Lin, Z.[Zhe], Qi, H.R.[Hai-Rong],
Image Super-Resolution by Neural Texture Transfer,
CVPR19(7974-7983).
IEEE DOI 2002
BibRef

Li, Z.[Zhen], Yang, J.[Jinglei], Liu, Z.[Zheng], Yang, X.M.[Xiao-Min], Jeon, G.G.[Gwang-Gil], Wu, W.[Wei],
Feedback Network for Image Super-Resolution,
CVPR19(3862-3871).
IEEE DOI 2002
BibRef

Hu, X.C.[Xue-Cai], Mu, H.Y.[Hao-Yuan], Zhang, X.Y.[Xiang-Yu], Wang, Z.[Zilei], Tan, T.N.[Tie-Niu], Sun, J.[Jian],
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution,
CVPR19(1575-1584).
IEEE DOI 2002
BibRef

Gao, Z., Edirisinghe, E., Chesnokov, S.,
Image Super-Resolution Using CNN Optimised By Self-Feature Loss,
ICIP19(2816-2820)
IEEE DOI 1910
Single image super resolution, deep learning, CNN, loss function BibRef

Wang, S.L.[Shen-Long], Zhang, L.[Lei], Liang, Y.[Yan], Pan, Q.[Quan],
Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis,
CVPR12(2216-2223).
IEEE DOI 1208
BibRef

Rivadeneira, R.E.[Rafael E.], Suárez, P.L.[Patricia L.], Sappa, A.D.[Angel D.], Vintimilla, B.X.[Boris X.],
Thermal Image Super-Resolution Through Deep Convolutional Neural Network,
ICIAR19(II:417-426).
Springer DOI 1909
BibRef

Nazeri, K., Thasarathan, H., Ebrahimi, M.,
Edge-Informed Single Image Super-Resolution,
AIM19(3275-3284)
IEEE DOI 2004
image resolution, image restoration, image texture, interpolation, interpolation schemes, higher-resolution image reconstruction, convolutional neural network BibRef

Xie, T., Yang, X., Jia, Y., Zhu, C., LI, X.,
Adaptive Densely Connected Single Image Super-Resolution,
AIM19(3432-3440)
IEEE DOI 2004
convolutional neural nets, feature extraction, image reconstruction, image resolution, Deep Learning BibRef

Hsu, C., Lin, C.,
Dual Reconstruction with Densely Connected Residual Network for Single Image Super-Resolution,
AIM19(3643-3650)
IEEE DOI 2004
backpropagation, gradient methods, image reconstruction, image resolution, gradient information, ESRGAN, Dual reconstruction BibRef

Xu, X.Y.[Xiao-Yu], Qian, J.[Jian], Yu, L.[Li], Yu, S.J.[Sheng-Ju], Tao, H.[Hao], Zhu, R.[Ran],
A Compact Deep Neural Network for Single Image Super-resolution,
MMMod20(II:148-160).
Springer DOI 2003
BibRef

Ji, X.Z.[Xiao-Zhong], Wu, Y.R.[Yi-Rui], Lu, T.[Tong],
Context-aware Residual Network with Promotion Gates for Single Image Super-resolution,
MMMod20(II:136-147).
Springer DOI 2003
BibRef

Wu, G., Zhao, L., Wang, W., Zeng, L., Chen, J.,
PRED: A Parallel Network for Handling Multiple Degradations via Single Model in Single Image Super-Resolution,
ICIP19(2881-2885)
IEEE DOI 1910
SISR, multiple degradation, CNN, PRED BibRef

Aljadaany, R.[Raied], Pal, D.K.[Dipan K.], Savvides, M.[Marios],
Proximal Splitting Networks for Image Restoration,
ICIAR19(I:3-17).
Springer DOI 1909
BibRef

Pendurkar, S.[Sumedh], Banerjee, B.[Biplab], Saha, S.[Sudipan], Bovolo, F.[Francesca],
Single Image Super-Resolution for Optical Satellite Scenes Using Deep Deconvolutional Network,
CIAP19(I:410-420).
Springer DOI 1909
BibRef

Vu, T.[Thang], Luu, T.M.[Tung M.], Yoo, C.D.[Chang D.],
Perception-Enhanced Image Super-Resolution via Relativistic Generative Adversarial Networks,
PerceptualRest18(V:98-113).
Springer DOI 1905
BibRef

Vasu, S.[Subeesh], Madam, N.T.[Nimisha Thekke], Rajagopalan, A.N.,
Analyzing Perception-Distortion Tradeoff Using Enhanced Perceptual Super-Resolution Network,
PerceptualRest18(V:114-131).
Springer DOI 1905
BibRef

Purohit, K.[Kuldeep], Mandal, S.[Srimanta], Rajagopalan, A.N.,
Scale-Recurrent Multi-residual Dense Network for Image Super-Resolution,
PerceptualRest18(V:132-149).
Springer DOI 1905
BibRef

Li, Y.W.[Ya-Wei], Agustsson, E.[Eirikur], Gu, S.H.[Shu-Hang], Timofte, R.[Radu], Van Gool, L.J.[Luc J.],
CARN: Convolutional Anchored Regression Network for Fast and Accurate Single Image Super-Resolution,
PerceptualRest18(V:166-181).
Springer DOI 1905
BibRef

Gu, S.H.[Shu-Hang], Sang, N.[Nong], Ma, F.[Fan],
Fast image super resolution via local regression,
ICPR12(3128-3131).
WWW Link. 1302
BibRef

Luo, X.T.[Xiao-Tong], Chen, R.[Rong], Xie, Y.[Yuan], Qu, Y.Y.[Yan-Yun], Li, C.H.[Cui-Hua],
Bi-GANs-ST for Perceptual Image Super-Resolution,
PerceptualRest18(V:20-34).
Springer DOI 1905
BibRef

Wu, X.Y.[Xian-Yu], Li, X.J.[Xiao-Jie], He, J.[Jia], Wu, X.[Xi], Mumtaz, I.[Imran],
Generative Adversarial Networks with Enhanced Symmetric Residual Units for Single Image Super-Resolution,
MMMod19(I:483-494).
Springer DOI 1901
BibRef

Cheng, X.[Xi], Li, X.[Xiang], Yang, J.[Jian], Tai, Y.[Ying],
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks,
ICPR18(147-152)
IEEE DOI 1812
Computational modeling, Image reconstruction, Convolution, Training, Task analysis, Spatial resolution, super resolution, recursive networks BibRef

Chen, R.[Rong], Qu, Y.Y.[Yan-Yun], Zeng, K.[Kun], Guo, J.K.[Jin-Kang], Li, C.H.[Cui-Hua], Xie, Y.[Yuan],
Persistent Memory Residual Network for Single Image Super Resolution,
Restoration18(922-9227)
IEEE DOI 1812
Degradation, Image resolution, Logic gates, Training, Image restoration, Convolution, Image reconstruction BibRef

Sharma, M., Mukhopadhyay, R., Upadhyay, A., Koundinya, S., Shukla, A., Chaudhury, S.,
IRGUN: Improved Residue Based Gradual Up-Scaling Network for Single Image Super Resolution,
Restoration18(947-94709)
IEEE DOI 1812
Image resolution, Image reconstruction, Convolution, Training, Interpolation, Image color analysis BibRef

Wang, Y., Perazzi, F., McWilliams, B., Sorkine-Hornung, A., Sorkine-Hornung, O., Schroers, C.[Christopher],
A Fully Progressive Approach to Single-Image Super-Resolution,
Restoration18(977-97709)
IEEE DOI 1812
Training, Image resolution, Image reconstruction, Generative adversarial networks, Computer architecture, Generators BibRef

Shi, Z.[Zhan], Chen, C.[Chang], Xiong, Z.W.[Zhi-Wei], Liu, D.[Dong], Zha, Z.J.[Zheng-Jun], Wu, F.[Feng],
Deep Residual Attention Network for Spectral Image Super-Resolution,
PerceptualRest18(V:214-229).
Springer DOI 1905
BibRef

Vu, T.[Thang], Nguyen, C.V.[Cao V.], Pham, T.X.[Trung X.], Luu, T.M.[Tung M.], Yoo, C.D.[Chang D.],
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks,
PerceptualRest18(V:243-259).
Springer DOI 1905
BibRef

Bei, Y.J.[Yi-Jie], Damian, A.[Alex], Hu, S.J.[Shi-Jia], Menon, S.[Sachit], Ravi, N.[Nikhil], Rudin, C.[Cynthia],
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution,
Restoration18(987-9877)
IEEE DOI 1812
Image resolution, Noise reduction, Training, Task analysis, Neural networks, Computer architecture, Knowledge engineering BibRef

Park, D., Kim, K., Chun, S.Y.,
Efficient Module Based Single Image Super Resolution for Multiple Problems,
Restoration18(995-9958)
IEEE DOI 1812
Noise reduction, Training, Image resolution, Neural networks, Recycling, Tuning BibRef

Zeng, K., Zheng, H., Qu, Y., Qu, X., Bao, L., Chen, Z.,
Single Image Super-Resolution With Learning Iteratively Non-Linear Mapping Between Low- and High-Resolution Sparse Representations,
ICPR18(507-512)
IEEE DOI 1812
Dictionaries, Encoding, Image resolution, Training, Sparse matrices, Image reconstruction, Degradation BibRef

Jiang, T., Zhang, Y., Wu, X., Lu, G., Hao, F., Zhang, Y.,
Single Image Super Resolution via a Refined Densely Connected Inception Network,
ICIP18(3588-3592)
IEEE DOI 1809
Image reconstruction, Image resolution, Feature extraction, Training, Task analysis, Machine learning, Super Resolution, Image Restoration BibRef

Shang, X., Yang, W., Sun, S., Tian, Y., Chen, H., Chen, K.,
Adaptive anchor-point selection for single image super-resolution,
VCIP17(1-4)
IEEE DOI 1804
image reconstruction, image resolution, learning (artificial intelligence), regression analysis, locality-constrained regression BibRef

Shi, W., Jiang, F., Zhao, D.,
Single image super-resolution with dilated convolution based multi-scale information learning inception module,
ICIP17(977-981)
IEEE DOI 1803
Convolution, Fuses, Image resolution, Image restoration, Kernel, Periodic structures, Training, Image super-resolution, multi-scale information BibRef

Ding, P.L.K., Li, B., Chang, K.,
Convex dictionary learning for single image super-resolution,
ICIP17(4058-4062)
IEEE DOI 1803
Dictionaries, Image reconstruction, Image resolution, Machine learning, Testing, Training, Training data, super-resolution BibRef

Grundhöfer, A., Röthlin, G.,
Camera-specific image quality enhancement using a convolutional neural network,
ICIP17(1392-1396)
IEEE DOI 1803
cameras, image colour analysis, image enhancement, image reconstruction, image sensors, interpolation, neural nets, Moiré BibRef

Sajjadi, M.S.M.[Mehdi S. M.], Schölkopf, B.[Bernhard], Hirsch, M.[Michael],
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis,
ICCV17(4501-4510)
IEEE DOI 1802
image reconstruction, image resolution, image texture, learning (artificial intelligence), neural nets, BibRef

Han, W.[Wei], Chang, S.[Shiyu], Liu, D.[Ding], Yu, M.[Mo], Witbrock, M.[Michael], Huang, T.S.[Thomas S.],
Image Super-Resolution via Dual-State Recurrent Networks,
CVPR18(1654-1663)
IEEE DOI 1812
Spatial resolution, Signal resolution, Recurrent neural networks, Computational modeling, Computer architecture BibRef

Fan, Y.C.[Yu-Chen], Shi, H.H.[Hong-Hui], Yu, J.H.[Jia-Hui], Liu, D.[Ding], Han, W.[Wei], Yu, H.C.[Hai-Chao], Wang, Z.Y.[Zhang-Yang], Wang, X.C.[Xin-Chao], Huang, T.S.[Thomas S.],
Balanced Two-Stage Residual Networks for Image Super-Resolution,
NTIRE17(1157-1164)
IEEE DOI 1709
Computational modeling, Dictionaries, Image resolution, Logic gates, Neural networks, Training BibRef

Choi, J.S.[Jae-Seok], Kim, M.C.[Mun-Churl],
A Deep Convolutional Neural Network with Selection Units for Super-Resolution,
NTIRE17(1150-1156)
IEEE DOI 1709
Convergence, Image reconstruction, Image resolution, Machine learning, Switches, Training BibRef

Donn, S., Meeus, L., Luong, H.Q., Goossens, B., Philips, W.,
Exploiting Reflectional and Rotational Invariance in Single Image Superresolution,
NTIRE17(1043-1049)
IEEE DOI 1709
Convergence, Image reconstruction, Image resolution, Neural networks, Training, Transforms BibRef

Zhao, M.D.[Man-Dan], Cheng, C.Q.[Chuan-Qi], Zhang, Z.J.[Zhen-Jie], Hao, X.Y.[Xiang-Yang],
Deep convolutional networks super-resolution method for reconstructing high frequency information of the single image,
ICIVC17(531-535)
IEEE DOI 1708
Computer vision, Convergence, Convolution, Image reconstruction, Image resolution, Signal resolution, Training, convolutional neural networks, frequency information, image super-resolution, residual, nerwork BibRef

Yang, Z.[Ze], Zhang, K.[Kai], Liang, Y.D.[Yu-Dong], Wang, J.J.[Jin-Jun],
Single Image Super-Resolution with a Parameter Economic Residual-Like Convolutional Neural Network,
MMMod17(I: 353-364).
Springer DOI 1701
BibRef

Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.,
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network,
CVPR17(105-114)
IEEE DOI 1711
Image reconstruction, Image resolution, Manifolds, Network architecture, Signal resolution, Training BibRef

Caballero, J., Ledig, C., Aitken, A., Acosta, A., Totz, J., Wang, Z., Shi, W.Z.,
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation,
CVPR17(2848-2857)
IEEE DOI 1711
Convolution, Motion compensation, Real-time systems, Spatial resolution, Streaming media, BibRef

Shi, W.Z.[Wen-Zhe], Caballero, J.[Jose], Huszár, F.[Ferenc], Totz, J.[Johannes], Aitken, A.P.[Andrew P.], Bishop, R.[Rob], Rueckert, D.[Daniel], Wang, Z.H.[Ze-Han],
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network,
CVPR16(1874-1883)
IEEE DOI 1612
BibRef

Liebel, L., Körner, M.,
Single-image Super Resolution For Multispectral Remote Sensing Data Using Convolutional Neural Networks,
ISPRS16(B3: 883-890).
DOI Link 1610
BibRef

Qu, Y.Y.[Yan-Yun], Shi, C.T.[Cui-Ting], Liu, J.R.[Jun-Ran], Peng, L.Y.[Li-Ying], Du, X.F.[Xiao-Feng],
Single Image Super-Resolution via Convolutional Neural Network and Total Variation Regularization,
MMMod16(II: 28-38).
Springer DOI 1601
BibRef

Youm, G.Y., Bae, S.H.[Sung-Ho], Kim, M.C.[Mun-Churl],
Image super-resolution based on convolution neural networks using multi-channel input,
IVMSP16(1-5)
IEEE DOI 1608
Convolution BibRef

Shi, J., Qi, C.,
Low-rank sparse representation for single image super-resolution via self-similarity learning,
ICIP16(1424-1428)
IEEE DOI 1610
Dictionaries BibRef

Lin, F.J.[Fang-Ju],
Super-resolution from learning the enhancement ratio and texture/residual dictionary,
ICIP15(2135-2139)
IEEE DOI 1512
BibRef

Wieschollek, P.[Patrick], Hirsch, M.[Michael], Schölkopf, B.[Bernhard], Lensch, H.P.A.[Hendrik P. A.],
Learning Blind Motion Deblurring,
ICCV17(231-240)
IEEE DOI 1802
BibRef
Earlier: A1, A3, A5, A2:
End-to-End Learning for Image Burst Deblurring,
ACCV16(IV: 35-51).
Springer DOI 1704
cameras, image motion analysis, image restoration, learning (artificial intelligence), recurrent neural nets, Training data BibRef

He, L.[Li], Qi, H.R.[Hai-Rong], Zaretzki, R.[Russell],
Beta Process Joint Dictionary Learning for Coupled Feature Spaces with Application to Single Image Super-Resolution,
CVPR13(345-352)
IEEE DOI 1309
Beta Process BibRef

An, L.[Le], Bhanu, B.[Bir],
Image super-resolution by extreme learning machine,
ICIP12(2209-2212).
IEEE DOI 1302
BibRef

Adler, A.[Amir], Hel-Or, Y.[Yacov], Elad, M.[Michael],
A Shrinkage Learning Approach for Single Image Super-Resolution with Overcomplete Representations,
ECCV10(II: 622-635).
Springer DOI 1009
BibRef

Begin, I.[Isabelle], Ferrie, F.P.[Frank P.],
PSF Recovery from Examples for Blind Super-Resolution,
ICIP07(V: 421-424).
IEEE DOI 0709
BibRef
And:
Training Database Adequacy Analysis for Learning-Based Super-Resolution,
CRV07(29-35).
IEEE DOI 0705
BibRef
Earlier:
Comparison of Super-Resolution Algorithms Using Image Quality Measures,
CRV06(72-72).
IEEE DOI 0607
BibRef
Earlier:
Blind super-resolution using a learning-based approach,
ICPR04(II: 85-89).
IEEE DOI 0409
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Super Resolution Analysis Using Edges, Edge Analysis for Superresolution .


Last update:Oct 24, 2021 at 16:35:58