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.B.[Kai-Bing],
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
Hui, Z.[Zheng],
Li, J.[Jie],
Wang, X.M.[Xiu-Mei],
Gao, X.B.[Xin-Bo],
Learning the Non-differentiable Optimization for Blind
Super-Resolution,
CVPR21(2093-2102)
IEEE DOI
2111
Measurement, Degradation, Adaptation models,
Computational modeling, Superresolution, Modulation, Predictive models
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
Wang, X.M.[Xiu-Mei],
Li, T.M.[Tian-Meng],
Hui, Z.[Zheng],
Cheng, P.T.[Pei-Tao],
Adaptive Modulation and Rectangular Convolutional Network for Stereo
Image Super-Resolution,
PRL(161), 2022, pp. 122-129.
Elsevier DOI
2209
stereo images, super-resolution, adaptive modulation,
rectangular convolutional layer
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
Huang, Y.F.[Yuan-Fei],
Li, J.[Jie],
Gao, X.B.[Xin-Bo],
He, L.H.[Li-Huo],
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],
Gao, X.B.[Xin-Bo],
Advanced Binary Neural Network for Single Image Super Resolution,
IJCV(131), No. 7, July 2023, pp. 1808-1824.
Springer DOI
2307
BibRef
Wu, Z.J.[Zhi-Jian],
Liu, W.H.[Wen-Hui],
Li, J.[Jun],
Xu, C.[Chang],
Huang, D.J.[Ding-Jiang],
SFHN: Spatial-Frequency Domain Hybrid Network for Image
Super-Resolution,
CirSysVideo(33), No. 11, November 2023, pp. 6459-6473.
IEEE DOI
2311
BibRef
Jiang, X.R.[Xin-Rui],
Wang, N.N.[Nan-Nan],
Xin, J.W.[Jing-Wei],
Li, K.Y.[Ke-Yu],
Yang, X.[Xi],
Li, J.[Jie],
Wang, X.Y.[Xiao-Yu],
Gao, X.B.[Xin-Bo],
FABNet: Frequency-Aware Binarized Network for Single Image
Super-Resolution,
IP(32), 2023, pp. 6234-6247.
IEEE DOI Code:
WWW Link.
2311
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
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],
Deep Back-Projecti Networks for Single Image Super-Resolution,
PAMI(43), No. 12, December 2021, pp. 4323-4337.
IEEE DOI
2112
BibRef
And:
Correction:
PAMI(44), No. 2, February 2022, pp. 1122-1122.
IEEE DOI
2201
BibRef
Earlier:
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
Feature extraction, Image reconstruction, Task analysis,
Training data, Superresolution, Image super-resolution, deep cnn, residual.
Task analysis, Training, 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
Wang, Y.[Yu],
Shao, Z.F.[Zhen-Feng],
Lu, T.[Tao],
Huang, X.[Xiao],
Wang, J.M.[Jia-Ming],
Chen, X.T.[Xi-Tong],
Huang, H.Y.[Hai-Yan],
Zuo, X.L.[Xiao-Long],
Remote Sensing Image Super-Resolution via Multi-Scale Texture
Transfer Network,
RS(15), No. 23, 2023, pp. 5503.
DOI Link
2312
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
Zheng, H.Y.[Hong-Yi],
Yong, H.W.[Hong-Wei],
Zhang, L.[Lei],
Unfolded Deep Kernel Estimation for Blind Image Super-Resolution,
ECCV22(XVIII:502-518).
Springer DOI
2211
BibRef
Yue, Z.S.[Zong-Sheng],
Zhao, Q.[Qian],
Xie, J.W.[Jian-Wen],
Zhang, L.[Lei],
Meng, D.Y.[De-Yu],
Wong, K.Y.K.[Kwan-Yee K.],
Blind Image Super-resolution with Elaborate Degradation Modeling on
Noise and Kernel,
CVPR22(2118-2128)
IEEE DOI
2210
Degradation, Monte Carlo methods, Laplace equations,
Superresolution, Machine learning, Probabilistic logic,
Machine learning
BibRef
Liang, J.[Jie],
Zeng, H.[Hui],
Zhang, L.[Lei],
Efficient and Degradation-Adaptive Network for Real-World Image
Super-Resolution,
ECCV22(XVIII:574-591).
Springer DOI
2211
BibRef
Zhang, X.D.[Xin-Dong],
Zeng, H.[Hui],
Guo, S.[Shi],
Zhang, L.[Lei],
Efficient Long-Range Attention Network for Image Super-Resolution,
ECCV22(XVII:649-667).
Springer DOI
2211
BibRef
Liang, J.[Jie],
Zeng, H.[Hui],
Zhang, L.[Lei],
Details or Artifacts: A Locally Discriminative Learning Approach to
Realistic Image Super-Resolution,
CVPR22(5647-5656)
IEEE DOI
2210
Training, Visualization, Codes, Computational modeling,
Superresolution, Generative adversarial networks, Low-level vision
BibRef
Wu, R.Y.[Rong-Yuan],
Yang, T.[Tao],
Sun, L.C.[Ling-Chen],
Zhang, Z.Q.[Zheng-Qiang],
Li, S.[Shuai],
Zhang, L.[Lei],
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution,
CVPR24(25456-25467)
IEEE DOI Code:
WWW Link.
2410
Degradation, Accuracy, Source coding, Semantics, Superresolution, Noise
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.Z.[Qi-Zhi],
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
Fuoli, D.[Dario],
Danelljan, M.[Martin],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Fast Online Video Super-Resolution with Deformable Attention Pyramid,
WACV23(1735-1744)
IEEE DOI
2302
Technological innovation, TV, Runtime, Superresolution,
Streaming media, Benchmark testing, Transformers,
and un-supervised learning)
BibRef
Cao, J.Z.[Jie-Zhang],
Liang, J.Y.[Jing-Yun],
Zhang, K.[Kai],
Li, Y.W.[Ya-Wei],
Zhang, Y.L.[Yu-Lun],
Wang, W.G.[Wen-Guan],
Van Gool, L.J.[Luc J.],
Reference-Based Image Super-Resolution with Deformable Attention
Transformer,
ECCV22(XVIII:325-342).
Springer DOI
2211
BibRef
Zhang, Y.L.[Yu-Lun],
Gu, K.Y.[Kai-Yu],
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],
Single Satellite Imagery Simultaneous Super-Resolution and
Colorization Using Multi-Task Deep Neural Networks,
JVCIR(53), 2018, pp. 20-30.
Elsevier DOI
1805
Image super-resolution, Satellite image colorization,
Deep neural networks, Multi-task learning
BibRef
Zhao, J.W.[Jian-Wei],
Chen, C.[Chen],
Zhou, Z.H.[Zheng-Hua],
Cao, F.L.[Fei-Long],
Single image super-resolution based on adaptive convolutional sparse
coding and convolutional neural networks,
JVCIR(58), 2019, pp. 651-661.
Elsevier DOI
1901
Super-resolution, Convolutional sparse coding,
Convolutional neural network, Adaptive
BibRef
Yang, J.X.[Jing-Xiang],
Zhao, Y.Q.A.[Yong-Qi-Ang],
Chan, J.C.W.[Jonathan Cheung-Wai],
Xiao, L.[Liang],
A Multi-Scale Wavelet 3D-CNN for Hyperspectral Image Super-Resolution,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link
1907
BibRef
Zhang, Z.,
Wang, X.,
Jung, C.,
DCSR: Dilated Convolutions for Single Image Super-Resolution,
IP(28), No. 4, April 2019, pp. 1625-1635.
IEEE DOI
1901
convolution, image reconstruction, image resolution, neural nets,
dilated convolutions, standard convolutions, receptive field,
correlation analysis
BibRef
Cosmo, D.L.,
Salles, E.O.T.[Evandro O. T.],
Multiple Sequential Regularized Extreme Learning Machines for Single
Image Super Resolution,
SPLetters(26), No. 3, March 2019, pp. 440-444.
IEEE DOI
1903
feedforward neural nets, image reconstruction, image resolution,
learning (artificial intelligence), reconstruction scheme,
neural networks
BibRef
Camponez, M.O.[Marcelo O.],
Salles, E.O.T.[Evandro O. T.],
Sarcinelli-Filho, M.[Mário],
A Closed Form Algorithm for Superresolution,
ISVC11(II: 338-347).
Springer DOI
1109
BibRef
Yang, W.M.[Wen-Ming],
Wang, W.[Wei],
Zhang, X.C.[Xue-Chen],
Sun, S.F.[Shui-Fa],
Liao, Q.M.[Qing-Min],
Lightweight Feature Fusion Network for Single Image Super-Resolution,
SPLetters(26), No. 4, April 2019, pp. 538-542.
IEEE DOI
1903
convolutional neural nets, feature extraction, image fusion,
image representation, image resolution, image texture,
spindle block
BibRef
Xie, C.[Chao],
Liu, Y.[Ying],
Zeng, W.[Weili],
Lu, X.B.[Xiao-Bo],
An improved method for single image super-resolution based on deep
learning,
SIViP(13), No. 3, April 2019, pp. 557-565.
WWW Link.
1904
BibRef
Yang, J.[Juan],
Li, W.J.[Wen-Jing],
Wang, R.G.[Rong-Gui],
Xue, L.X.[Li-Xia],
Hu, M.[Min],
Enhanced Two-Phase Residual Network for Single Image Super-Resolution,
JVCIR(61), 2019, pp. 188-197.
Elsevier DOI
1906
Super-resolution, Convolutional neural network,
Deep residual learning, Dilated convolution
BibRef
Chen, Y.[Yeyao],
Yu, M.[Mei],
Jiang, G.Y.[Gang-Yi],
Peng, Z.J.[Zong-Ju],
Chen, F.[Fen],
End-to-end single image enhancement based on a dual network cascade
model,
JVCIR(61), 2019, pp. 284-295.
Elsevier DOI
1906
Single image enhancement, Convolutional neural network,
Dual network cascade model, Exposure prediction, Exposure fusion
BibRef
Liu, D.[Ding],
Wang, Z.W.[Zhao-Wen],
Fan, Y.C.[Yu-Chen],
Liu, X.M.[Xian-Ming],
Wang, Z.Y.[Zhang-Yang],
Chang, S.Y.[Shi-Yu],
Wang, X.,
Huang, T.S.[Thomas S.],
Learning Temporal Dynamics for Video Super-Resolution:
A Deep Learning Approach,
IP(27), No. 7, July 2018, pp. 3432-3445.
IEEE DOI
1805
BibRef
Earlier: A1, A2, A3, A4, A5, A6, A8, Only:
Robust Video Super-Resolution with Learned Temporal Dynamics,
ICCV17(2526-2534)
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
Chaudhry, A.M.[Alina Majeed],
Riaz, M.M.[M. Mohsin],
Ghafoor, A.[Abdul],
Super-resolution based on self-example learning and guided filtering,
SIViP(13), No. 2, March 2019, pp. 237-244.
Springer DOI
1904
Denoise, then super resolution.
BibRef
Wang, Y.F.[Yi-Fan],
Wang, L.J.[Li-Jun],
Wang, H.Y.[Hong-Yu],
Li, P.H.[Pei-Hua],
Resolution-Aware Network for Image Super-Resolution,
CirSysVideo(29), No. 5, May 2019, pp. 1259-1269.
IEEE DOI
1905
Task analysis, Image resolution, Training, Correlation,
Image reconstruction, Image restoration, Learning systems,
cascade
BibRef
Wang, Y.F.[Yi-Fan],
Wang, L.J.[Li-Jun],
Wang, H.Y.[Hong-Yu],
Li, P.H.[Pei-Hua],
Lu, H.C.[Hu-Chuan],
Blind single image super-resolution with a mixture of deep networks,
PR(102), 2020, pp. 107169.
Elsevier DOI
2003
Blind super-resolution, Mixture of networks, Blur kernels,
Lower bound, Latent variables
BibRef
Zhang, Z.H.[Zhi-Hong],
Xu, C.[Chen],
Zhang, Z.H.[Zhong-Hao],
Chen, G.[Guo],
Cai, Y.[Yide],
Wang, Z.[Zeli],
Li, H.[Heng],
Hancock, E.R.[Edwin R.],
Single image super resolution via neighbor reconstruction,
PRL(125), 2019, pp. 157-165.
Elsevier DOI
1909
Manifold learning, Neighbor reconstruction, Super resolution
BibRef
Zhang, Z.H.[Zhi-Hong],
Xu, Z.B.[Zhuo-Bin],
Ye, Z.L.[Zhi-Ling],
Hu, Y.Q.[Yi-Qun],
Cui, L.X.[Li-Xin],
Bai, L.[Lu],
Single Image Super Resolution via Neighbor Reconstruction,
SSSPR18(406-415).
Springer DOI
1810
BibRef
Zhao, X.L.[Xiao-Le],
Zhang, Y.L.[Yu-Lun],
Zhang, T.[Tao],
Zou, X.M.[Xue-Ming],
Channel Splitting Network for Single MR Image Super-Resolution,
IP(28), No. 11, November 2019, pp. 5649-5662.
IEEE DOI
1909
Training, Spatial resolution, Imaging, Deep learning,
Signal resolution, super-resolution
BibRef
Zhang, X.M.[Xiao-Ming],
Li, T.R.[Tian-Rui],
Zhao, X.L.[Xiao-Le],
Boosting Single Image Super-Resolution via Partial Channel Shifting,
ICCV23(13177-13186)
IEEE DOI
2401
BibRef
Xie, C.X.[Cheng-Xing],
Zhang, X.M.[Xiao-Ming],
Li, L.[Linze],
Meng, H.T.[Hai-Teng],
Zhang, T.L.[Tian-Lin],
Li, T.R.[Tian-Rui],
Zhao, X.L.[Xiao-Le],
Large Kernel Distillation Network for Efficient Single Image
Super-Resolution,
NTIRE23(1283-1292)
IEEE DOI
2309
BibRef
Yan, B.,
Bare, B.,
Ma, C.,
Li, K.,
Tan, W.,
Deep Objective Quality Assessment Driven Single Image
Super-Resolution,
MultMed(21), No. 11, November 2019, pp. 2957-2971.
IEEE DOI
1911
Feature extraction, Image resolution, Image quality,
Quality assessment, Signal resolution, Deep learning, Measurement,
image enhancement
BibRef
Yang, X.[Xin],
Mei, H.Y.[Hai-Yang],
Zhang, J.Q.[Ji-Qing],
Xu, K.[Ke],
Yin, B.C.[Bao-Cai],
Zhang, Q.[Qiang],
Wei, X.P.[Xiao-Peng],
DRFN: Deep Recurrent Fusion Network for Single-Image Super-Resolution
with Large Factors,
MultMed(21), No. 2, February 2019, pp. 328-337.
IEEE DOI
1902
Feature extraction, Image reconstruction, Convolution,
Image resolution, Interpolation, Databases, Visualization,
large factors
BibRef
Zhang, J.Q.[Ji-Qing],
Long, C.J.[Cheng-Jiang],
Wang, Y.X.[Yu-Xin],
Piao, H.Y.[Hai-Yin],
Mei, H.Y.[Hai-Yang],
Yang, X.[Xin],
Yin, B.C.[Bao-Cai],
A Two-Stage Attentive Network for Single Image Super-Resolution,
CirSysVideo(32), No. 3, March 2022, pp. 1020-1033.
IEEE DOI
2203
Feature extraction, Image reconstruction, Superresolution,
Data mining, Ions, Convolution, Training,
cross-dimension interaction
BibRef
Gu, J.[Jun],
Sun, X.[Xian],
Zhang, Y.[Yue],
Fu, K.[Kun],
Wang, L.[Lei],
Deep Residual Squeeze and Excitation Network for Remote Sensing Image
Super-Resolution,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link
1908
BibRef
Li, X.G.[Xiao-Guang],
Sun, X.[Xu],
Lam, K.M.[Kin Man],
Zhuo, L.[Li],
Li, J.[Jiafeng],
Dong, N.[Ning],
Deep-network based method for joint image deblocking and
super-resolution,
IET-IPR(13), No. 10, 22 August 2019, pp. 1636-1647.
DOI Link
1909
BibRef
Feng, X.B.[Xu-Bin],
Su, X.Q.[Xiu-Qin],
Shen, J.[Junge],
Jin, H.[Humin],
Single Space Object Image Denoising and Super-Resolution
Reconstructing Using Deep Convolutional Networks,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link
1909
BibRef
Fu, B.[Bo],
Li, Y.[Yi],
Wang, X.H.[Xiang-Hai],
Ren, Y.G.[Yong-Gong],
Image super-resolution using TV priori guided convolutional network,
PRL(125), 2019, pp. 780-784.
Elsevier DOI
1909
Image super-resolution, TV priori, Non-local regression, Convolutional network
BibRef
Pan, Z.X.[Zong-Xu],
Ma, W.[Wen],
Guo, J.Y.[Jia-Yi],
Lei, B.[Bin],
Super-Resolution of Single Remote Sensing Image Based on Residual
Dense Backprojection Networks,
GeoRS(57), No. 10, October 2019, pp. 7918-7933.
IEEE DOI
1910
feature extraction, geophysical image processing,
image classification, image reconstruction, image resolution,
single image super-resolution (SISR)
BibRef
Ma, W.[Wen],
Pan, Z.X.[Zong-Xu],
Guo, J.Y.[Jia-Yi],
Lei, B.[Bin],
Achieving Super-Resolution Remote Sensing Images via the Wavelet
Transform Combined With the Recursive Res-Net,
GeoRS(57), No. 6, June 2019, pp. 3512-3527.
IEEE DOI
1906
Spatial resolution, Image reconstruction, Remote sensing,
Discrete wavelet transforms, Signal resolution,
wavelet transform (WT)
BibRef
Yu, B.[Bo],
Lei, B.[Bin],
Guo, J.Y.[Jia-Yi],
Sun, J.D.[Jian-De],
Li, S.T.[Sheng-Tao],
Xie, G.S.[Guang-Shuai],
Remote Sensing Image Super-Resolution via Residual-Dense Hybrid
Attention Network,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Ma, W.[Wen],
Pan, Z.X.[Zong-Xu],
Yuan, F.[Feng],
Lei, B.[Bin],
Super-Resolution of Remote Sensing Images via a Dense Residual
Generative Adversarial Network,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
1911
BibRef
Shamsolmoali, P.[Pourya],
Li, X.F.[Xiao-Fang],
Wang, R.[Ruili],
Single image resolution enhancement by efficient dilated densely
connected residual network,
SP:IC(79), 2019, pp. 13-23.
Elsevier DOI
1911
Image super-resolution, Dilated convolution, Dense network, Optimization
BibRef
Xie, C.[Chao],
Zeng, W.L.[Wei-Li],
Lu, X.B.[Xiao-Bo],
Fast Single-Image Super-Resolution via Deep Network With Component
Learning,
CirSysVideo(29), No. 12, December 2019, pp. 3473-3486.
IEEE DOI
1912
Computational modeling, Image reconstruction, Training,
Image resolution, Convolutional codes, Encoding,
deep convolutional neural networks
BibRef
Xie, C.[Chao],
Zhu, H.Y.[Hong-Yu],
Fei, Y.Q.[Ye-Qi],
Deep coordinate attention network for single image super-resolution,
IET-IPR(16), No. 1, 2022, pp. 273-284.
DOI Link
2112
BibRef
Yang, Y.[Yong],
Zhang, D.Y.[Dong-Yang],
Huang, S.Y.[Shu-Ying],
Wu, J.J.[Jia-Jun],
Multilevel and Multiscale Network for Single-Image Super-Resolution,
SPLetters(26), No. 12, December 2019, pp. 1877-1881.
IEEE DOI
2001
convolutional neural nets, feature extraction, image fusion,
image reconstruction, image representation, image resolution,
channel-wise attention
BibRef
Chang, J.,
Kang, K.,
Kang, S.,
An Energy-Efficient FPGA-Based Deconvolutional Neural Networks
Accelerator for Single Image Super-Resolution,
CirSysVideo(30), No. 1, January 2020, pp. 281-295.
IEEE DOI
2002
convolutional neural nets,
field programmable gate arrays, image resolution,
system architecture
BibRef
Nasrollahi, H.[Hamdollah],
Farajzadeh, K.[Kamran],
Hosseini, V.[Vahid],
Zarezadeh, E.[Esmaeil],
Abdollahzadeh, M.[Milad],
Deep artifact-free residual network for single-image super-resolution,
SIViP(14), No. 2, March 2020, pp. 407-415.
Springer DOI
2003
BibRef
Mei, K.F.[Kang-Fu],
Jiang, A.W.[Ai-Wen],
Li, J.C.[Jun-Cheng],
Liu, B.[Bo],
Ye, J.H.[Ji-Hua],
Wang, M.W.[Ming-Wen],
Deep residual refining based pseudo-multi-frame network for effective
single image super-resolution,
IET-IPR(13), No. 4, March 2019, pp. 591-599.
DOI Link
1903
BibRef
Li, J.C.[Jun-Cheng],
Pei, Z.[Zehua],
Li, W.J.[Wen-Jie],
Gao, G.W.[Guang-Wei],
Wang, L.G.[Long-Guang],
Wang, Y.Q.[Ying-Qian],
Zeng, T.Y.[Tie-Yong],
A Systematic Survey of Deep Learning-Based Single-Image
Super-Resolution,
Surveys(56), No. 10, May 2024, pp. xx-yy.
DOI Link Code:
WWW Link.
2407
Survey, Super-Resolution. Image super-resolution, single-image super-resolution, SISR, survey
BibRef
Li, J.C.[Jun-Cheng],
Fang, F.M.[Fa-Ming],
Li, J.Q.[Jia-Qian],
Mei, K.F.[Kang-Fu],
Zhang, G.X.[Gui-Xu],
MDCN: Multi-Scale Dense Cross Network for Image Super-Resolution,
CirSysVideo(31), No. 7, July 2021, pp. 2547-2561.
IEEE DOI
2107
BibRef
Earlier: A1, A2, A4, A5, Only:
Multi-scale Residual Network for Image Super-Resolution,
ECCV18(VIII: 527-542).
Springer DOI
1810
Feature extraction, Image reconstruction, Adaptation models,
Correlation, Image resolution, Computational modeling,
dynamic reconstruction
BibRef
Li, W.J.[Wen-Jie],
Li, J.C.[Jun-Cheng],
Gao, G.W.[Guang-Wei],
Deng, W.H.[Wei-Hong],
Zhou, J.T.[Jian-Tao],
Yang, J.[Jian],
Qi, G.J.[Guo-Jun],
Cross-Receptive Focused Inference Network for Lightweight Image
Super-Resolution,
MultMed(26), 2024, pp. 864-877.
IEEE DOI
2402
Transformers, Computational modeling, Computed tomography,
Feature extraction, Convolution, Adaptation models, Task analysis,
efficient model
BibRef
Li, J.C.[Jun-Cheng],
Yuan, Y.T.[Yi-Ting],
Mei, K.F.[Kang-Fu],
Fang, F.M.[Fa-Ming],
Lightweight and Accurate Recursive Fractal Network for Image
Super-Resolution,
CLI19(3814-3823)
IEEE DOI
2004
Code, Super Resolution.
WWW Link. convolutional neural nets, fractals, image resolution,
learning (artificial intelligence), topology, CNN
BibRef
Fang, F.M.[Fa-Ming],
Li, J.C.[Jun-Cheng],
Zeng, T.Y.[Tie-Yong],
Soft-Edge Assisted Network for Single Image Super-Resolution,
IP(29), 2020, pp. 4656-4668.
IEEE DOI
2003
Image reconstruction, Image edge detection, Image resolution,
Feature extraction, Task analysis, Convolutional neural networks,
image restoration
BibRef
Zhang, H.[Hao],
Qi, T.[Te],
Zeng, T.Y.[Tie-Yong],
Scene recovery: Combining visual enhancement and resolution
improvement,
PR(153), 2024, pp. 110529.
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2405
Single image visual enhancement, Resolution improvement,
Variational scene recovery model, Various scenes
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Wen, Z.J.[Zhi-Jie],
Guan, J.W.[Jia-Wei],
Zeng, T.Y.[Tie-Yong],
Li, Y.[Ying],
Residual network with detail perception loss for single image
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CVIU(199), 2020, pp. 103007.
Elsevier DOI
2009
Detail perception loss, Multi-layer perceptron, Single image super-resolution
BibRef
Qin, M.J.[Meng-Jiao],
Mavromatis, S.[Sébastien],
Hu, L.S.[Lin-Shu],
Zhang, F.[Feng],
Liu, R.Y.[Ren-Yi],
Sequeira, J.[Jean],
Du, Z.H.[Zhen-Hong],
Remote Sensing Single-Image Resolution Improvement Using A Deep
Gradient-Aware Network with Image-Specific Enhancement,
RS(12), No. 5, 2020, pp. xx-yy.
DOI Link
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BibRef
He, Z.,
Cao, Y.,
Du, L.,
Xu, B.,
Yang, J.,
Cao, Y.,
Tang, S.,
Zhuang, Y.,
MRFN: Multi-Receptive-Field Network for Fast and Accurate Single
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MultMed(22), No. 4, April 2020, pp. 1042-1054.
IEEE DOI
2004
Feature extraction, Training, Image reconstruction,
Image resolution, Image restoration, loss function
BibRef
Zhang, L.B.[Li-Bao],
Chen, D.H.[Dong-Hui],
Ma, J.[Jie],
Zhang, J.[Jue],
Remote-Sensing Image Superresolution Based on Visual Saliency
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GeoRS(58), No. 6, June 2020, pp. 4099-4115.
IEEE DOI
2005
Deep learning, generative adversarial network, remote sensing,
saliency, single-image superresolution (SISR), unequal reconstruction
BibRef
Pashaei, M.[Mohammad],
Starek, M.J.[Michael J.],
Kamangir, H.[Hamid],
Berryhill, J.[Jacob],
Deep Learning-Based Single Image Super-Resolution: An Investigation
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RS(12), No. 11, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Xu, W.J.[Wen-Jie],
Song, H.H.[Hui-Hui],
Zhang, K.[Kaihua],
Liu, Q.S.[Qing-Shan],
Liu, J.[Jia],
Learning Lightweight Multi-Scale Feedback Residual Network for Single
Image Super-Resolution,
CVIU(197-198), 2020, pp. 103005.
Elsevier DOI
2008
Image super-resolution, CNNs, Residual learning, RNN, Feedback
BibRef
Li, B.[Biao],
Wang, B.[Bo],
Liu, J.B.[Jia-Bin],
Qi, Z.Q.[Zhi-Quan],
Shi, Y.[Yong],
s-LWSR: Super Lightweight Super-Resolution Network,
IP(29), 2020, pp. 8368-8380.
IEEE DOI
2008
Computational modeling, Image resolution, Computer architecture,
Biological system modeling, Image coding, Mobile handsets,
activation operation removal
BibRef
Lei, S.,
Shi, Z.,
Zou, Z.,
Coupled Adversarial Training for Remote Sensing Image
Super-Resolution,
GeoRS(58), No. 5, May 2020, pp. 3633-3643.
IEEE DOI
2005
Coupled adversarial training,
deep convolutional neural networks,
super-resolution
BibRef
Li, Q.A.[Qi-Ang],
Wang, Q.[Qi],
Li, X.L.[Xue-Long],
Mixed 2D/3D Convolutional Network for Hyperspectral Image
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RS(12), No. 10, 2020, pp. xx-yy.
DOI Link
2006
BibRef
Li, Q.A.[Qi-Ang],
Wang, Q.[Qi],
Li, X.L.[Xue-Long],
Exploring the Relationship Between 2D/3D Convolution for
Hyperspectral Image Super-Resolution,
GeoRS(59), No. 10, October 2021, pp. 8693-8703.
IEEE DOI
2109
Convolution, Hyperspectral imaging,
Image reconstruction, Solid modeling,
super-resolution (SR)
BibRef
Liu, J.,
Wu, Z.,
Xiao, L.,
Sun, J.,
Yan, H.,
A Truncated Matrix Decomposition for Hyperspectral Image
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IP(29), 2020, pp. 8028-8042.
IEEE DOI
2008
Spatial resolution, Matrix decomposition, Hyperspectral imaging,
Image segmentation, Machine learning,
superpixel
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Singh, V.[Vikram],
Ramnath, K.[Keerthan],
Mittal, A.[Anurag],
Refining high-frequencies for sharper super-resolution and deblurring,
CVIU(199), 2020, pp. 103034.
Elsevier DOI
2009
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)
IEEE DOI
2006
Image resolution, Shape, Architecture, Training,
Computer architecture, Feature extraction
BibRef
Chudasama, V.[Vishal],
Upla, K.[Kishor],
E-ProSRNet:
An enhanced progressive single image super-resolution approach,
CVIU(200), 2020, pp. 103038.
Elsevier DOI
2010
CNN, Single image super-resolution, Residual network,
Computationally efficient, Charbonnier loss function
BibRef
Guo, Y.,
Luo, Y.,
He, Z.,
Huang, J.,
Chen, J.,
Hierarchical Neural Architecture Search for Single Image
Super-Resolution,
SPLetters(27), 2020, pp. 1255-1259.
IEEE DOI
2008
Computer architecture, Microprocessors, Computational modeling,
Convolution, Training, Interpolation, Aerospace electronics,
Super-Resolution
BibRef
Lan, R.,
Sun, L.,
Liu, Z.,
Lu, H.,
Su, Z.,
Pang, C.,
Luo, X.,
Cascading and Enhanced Residual Networks for Accurate Single-Image
Super-Resolution,
Cyber(51), No. 1, January 2021, pp. 115-125.
IEEE DOI
2012
Feature extraction, Image reconstruction, Spatial resolution,
Convolutional neural networks, Training, Graphics,
single-image super-resolution (SISR)
BibRef
Cao, F.L.[Fei-Long],
Chen, B.J.[Bai-Jie],
Densely connected network with improved pyramidal bottleneck residual
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JVCIR(74), 2021, pp. 102963.
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2101
Super-resolution, Convolution neural network,
Pyramidal network, Residual-feature learning
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Wu, H.P.[Hua-Peng],
Zou, Z.X.[Zheng-Xia],
Gui, J.[Jie],
Zeng, W.J.[Wen-Jun],
Ye, J.P.[Jie-Ping],
Zhang, J.[Jun],
Liu, H.Y.[Hong-Yi],
Wei, Z.H.[Zhi-Hui],
Multi-Grained Attention Networks for Single Image Super-Resolution,
CirSysVideo(31), No. 2, February 2021, pp. 512-522.
IEEE DOI
2102
Feature extraction, Convolution, Image reconstruction,
Computational modeling, Task analysis, Spatial resolution,
multi-scale dense connections
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Wu, H.P.[Hua-Peng],
Gui, J.[Jie],
Zhang, J.[Jun],
Kwok, J.T.[James T.],
Wei, Z.H.[Zhi-Hui],
Feedback Pyramid Attention Networks for Single Image Super-Resolution,
CirSysVideo(33), No. 9, September 2023, pp. 4881-4892.
IEEE DOI
2310
BibRef
Wu, H.P.[Hua-Peng],
Gui, J.[Jie],
Zhang, J.[Jun],
Kwok, J.T.[James T.],
Wei, Z.H.[Zhi-Hui],
Pyramidal dense attention networks for single image super-resolution,
IET-IPR(16), No. 12, 2022, pp. 3247-3257.
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Jin, D.,
Ji, M.,
Xu, L.,
Wu, G.,
Wang, L.,
Fang, L.,
Boosting Single Image Super-Resolution Learnt From Implicit
Multi-Image Prior,
IP(30), 2021, pp. 3240-3251.
IEEE DOI
2103
Training, Superresolution,
Spatial resolution, Image edge detection, Convolution,
convolutional neural networks
BibRef
Zhang, L.[Lei],
Lang, Z.Q.[Zhi-Qiang],
Wei, W.[Wei],
Zhang, Y.N.[Yan-Ning],
Embarrassingly Simple Binarization for Deep Single Imagery
Super-Resolution Networks,
IP(30), 2021, pp. 3934-3945.
IEEE DOI
2104
Training, Degradation, Quantization (signal),
Performance evaluation, Superresolution, Computational modeling,
curriculum learning
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Dai, T.[Tao],
Cai, J.R.[Jian-Rui],
Zhang, Y.B.[Yong-Bing],
Xia, S.T.[Shu-Tao],
Zhang, L.[Lei],
Second-Order Attention Network for Single Image Super-Resolution,
CVPR19(11057-11066).
IEEE DOI
2002
BibRef
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)
IEEE DOI
2004
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],
Deep Shearlet Residual Learning Network for Single Image
Super-Resolution,
IP(30), 2021, pp. 4129-4142.
IEEE DOI
2104
Superresolution, Transforms, Deep learning, Training, Remote sensing,
Neural networks, Image reconstruction,
convolutional neural network
BibRef
Zhang, M.L.[Meng-Lei],
Ling, Q.[Qiang],
Bilateral Upsampling Network for Single Image Super-Resolution With
Arbitrary Scaling Factors,
IP(30), 2021, pp. 4395-4408.
IEEE DOI
2104
Superresolution, Degradation, Feature extraction, Task analysis,
Image edge detection, Kernel, Image restoration,
convolutional neural networks
BibRef
Zhang, H.P.[Hao-Peng],
Wang, P.R.[Peng-Rui],
Jiang, Z.G.[Zhi-Guo],
Nonpairwise-Trained Cycle Convolutional Neural Network for Single
Remote Sensing Image Super-Resolution,
GeoRS(59), No. 5, May 2021, pp. 4250-4261.
IEEE DOI
2104
Remote sensing, Image reconstruction, Spatial resolution, Training,
Signal resolution, Image sensors,
super-resolution (SR)
BibRef
Zhang, H.P.[Hao-Peng],
Zhang, C.[Cong],
Xie, F.Y.[Feng-Ying],
Jiang, Z.G.[Zhi-Guo],
A Closed-Loop Network for Single Infrared Remote Sensing Image
Super-Resolution in Real World,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
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Yan, Y.Y.[Yan-Yang],
Ren, W.Q.[Wen-Qi],
Hu, X.B.[Xia-Bin],
Li, K.[Kun],
Shen, H.F.[Hai-Feng],
Cao, X.C.[Xiao-Chun],
SRGAT: Single Image Super-Resolution with Graph Attention Network,
IP(30), 2021, pp. 4905-4918.
IEEE DOI
2106
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],
Single Image Super-Resolution via a Holistic Attention Network,
ECCV20(XII: 191-207).
Springer DOI
2010
See also Single Image Dehazing via Multi-Scale Convolutional Neural Networks with Holistic Edges.
BibRef
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],
Single-Image super-resolution: When model adaptation matters,
PR(116), 2021, pp. 107931.
Elsevier DOI
2106
Internal prior, Model adaptation,
Deep convolutional neural network, Projection skip connection
BibRef
Shen, Y.[Yan],
Zhang, L.[Liao],
Chen, Y.[Yun],
Xie, Y.[Yi],
Wang, Z.L.[Zhong-Li],
Shao, X.T.[Xiao-Tao],
Deep Multi-Level Up-Projection Network for Single Image
Super-Resolution,
IET-IPR(15), No. 2, 2021, pp. 325-336.
DOI Link
2106
BibRef
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)
IEEE DOI
1910
Super-Resolution, Deep Learning, Attention Mechanism, Residual Learning
BibRef
Ren, C.[Chao],
He, X.[Xiaohai],
Pu, Y.F.[Yi-Fei],
Nguyen, T.Q.[Truong Q.],
Learning Image Profile Enhancement and Denoising Statistics Priors
for Single-Image Super-Resolution,
Cyber(51), No. 7, July 2021, pp. 3535-3548.
IEEE DOI
2106
Image reconstruction, Noise reduction, Optimization,
Image edge detection, Image resolution, Degradation,
super-resolution (SR)
BibRef
Wang, L.[Lin],
Yoon, K.J.[Kuk-Jin],
Semi-supervised student-teacher learning for single image
super-resolution,
PR(121), 2022, pp. 108206.
Elsevier DOI
2109
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],
Lightweight Adaptive Weighted Network for Single Image
Super-Resolution,
CVIU(211), 2021, pp. 103254.
Elsevier DOI
2110
Lightweight, Single-image super-resolution,
Adaptive weighted super-resolution network
BibRef
Mo, F.[Fei],
Wu, H.[Heng],
Qu, S.[Shuo],
Luo, S.J.[Shao-Juan],
Cheng, L.L.[Liang-Lun],
Single infrared image super-resolution based on lightweight
multi-path feature fusion network,
IET-IPR(16), No. 7, 2022, pp. 1880-1896.
DOI Link
2205
BibRef
Zhang, Z.L.[Zi-Li],
Favaro, P.[Paolo],
Tian, Y.[Yan],
Li, J.X.[Jian-Xiang],
Learn to Zoom in Single Image Super-Resolution,
SPLetters(29), 2022, pp. 1237-1241.
IEEE DOI
2206
Superresolution, Training, Image reconstruction, Signal resolution,
Measurement, Neural networks, Crops, Extreme SR, mask, ZoomGAN
BibRef
Mo, L.F.[Ling-Fei],
Guan, X.[Xuchen],
Neural component search for single image super-resolution,
SP:IC(106), 2022, pp. 116725.
Elsevier DOI
2206
Neural component search, Super-resolution, Reinforcement learning
BibRef
Wang, C.L.[Cai-Ling],
Shen, Q.[Qi],
Wang, X.B.[Xing-Bo],
Jiang, G.P.[Guo-Ping],
Momentum feature comparison network based on generative adversarial
network for single image super-resolution,
SP:IC(106), 2022, pp. 116726.
Elsevier DOI
2206
Unsupervised learning, Momentum feature comparison,
Generative adversarial networks, Super resolution
BibRef
Lin, Z.C.[Zheng-Chun],
Li, S.Y.[Si-Yuan],
Jiang, Y.Z.[Yun-Zhi],
Wang, J.[Jing],
Luo, Q.X.[Qing-Xing],
Feedback Multi-scale Residual Dense Network for image
super-resolution,
SP:IC(107), 2022, pp. 116760.
Elsevier DOI
2208
Deep learning, Super-resolution, Multi-scale feature, Feedback network
BibRef
Ketsoi, V.[Vachiraporn],
Raza, M.[Muhammad],
Chen, H.P.[Hao-Peng],
Yang, X.B.[Xu-Bo],
SREFBN: Enhanced feature block network for single-image
super-resolution,
IET-IPR(16), No. 12, 2022, pp. 3143-3154.
DOI Link
2209
BibRef
Cheng, R.[Rui],
Wu, Y.Z.[Yu-Zhe],
Wang, J.[Jia],
Ma, M.M.[Ming-Ming],
Niu, Y.[Yi],
Shi, G.M.[Guang-Ming],
Adaptive Feature Denoising Based Deep Convolutional Network for
Single Image Super-Resolution,
CVIU(223), 2022, pp. 103518.
Elsevier DOI
2210
Super-resolution, Feature denoising, Deep learning, Soft thresholding
BibRef
Liu, Z.Y.[Zi-Yang],
Li, Z.G.[Zheng-Guo],
Wu, X.M.[Xing-Ming],
Liu, Z.[Zhong],
Chen, W.H.[Wei-Hai],
DSRGAN: Detail Prior-Assisted Perceptual Single Image
Super-Resolution via Generative Adversarial Networks,
CirSysVideo(32), No. 11, November 2022, pp. 7418-7431.
IEEE DOI
2211
Generative adversarial networks, Image edge detection,
High frequency, Image restoration, Measurement,
model-based and data-driven
BibRef
Gao, S.Q.[Shang-Qi],
Zhuang, X.H.[Xia-Hai],
Bayesian Image Super-Resolution With Deep Modeling of Image
Statistics,
PAMI(45), No. 2, February 2023, pp. 1405-1423.
IEEE DOI
2301
Image restoration, Bayes methods, Computational modeling,
Mathematical models, Superresolution, Task analysis,
generative learning
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)
IEEE DOI
2109
Visualization, Dictionaries,
Superresolution, Neural networks, Space exploration
BibRef
Hsu, W.Y.[Wei-Yen],
Jian, P.W.[Pei-Wen],
Wavelet detail perception network for single image super-resolution,
PRL(166), 2023, pp. 16-23.
Elsevier DOI
2302
Wavelet transform, Detail perception enhancement, Single image super-resolution
BibRef
Chen, S.[Shi],
Bi, X.P.[Xiu-Ping],
Zhang, L.F.[Le-Fei],
Fused pyramid attention network for single image super-resolution,
IET-IPR(17), No. 6, 2023, pp. 1681-1693.
DOI Link
2305
hyperspectral imaging, image restoration
BibRef
Tang, Y.G.[Ying-Gan],
Zhang, X.[Xiang],
Zhang, X.G.[Xu-Guang],
An Efficient Lightweight Network for Single Image Super-Resolution,
JVCIR(93), 2023, pp. 103834.
Elsevier DOI
2305
Super-resolution, Sparse, Self-attention, Efficiency, Lightweight
BibRef
Wu, J.[Jun],
Wang, Y.X.[Yu-Xi],
Zhang, X.G.[Xu-Guang],
Lightweight Asymmetric Convolutional Distillation Network for Single
Image Super-Resolution,
SPLetters(30), 2023, pp. 733-737.
IEEE DOI
2307
Convolution, Feature extraction, Superresolution,
Image reconstruction, Computational modeling, Data mining,
asymmetric convolutional distillation block
BibRef
Su, J.N.[Jian-Nan],
Gan, M.[Min],
Chen, G.Y.[Guang-Yong],
Yin, J.L.[Jia-Li],
Chen, C.L.P.[C. L. Philip],
Global Learnable Attention for Single Image Super-Resolution,
PAMI(45), No. 7, July 2023, pp. 8453-8465.
IEEE DOI
2306
Task analysis, Image reconstruction, Degradation,
Computational modeling, Feature extraction, Superresolution,
deep learning
BibRef
Su, J.N.[Jian-Nan],
Gan, M.[Min],
Chen, G.Y.[Guang-Yong],
Guo, W.Z.[Wen-Zhong],
Chen, C.L.P.[C. L. Philip],
High-Similarity-Pass Attention for Single Image Super-Resolution,
IP(33), 2024, pp. 610-624.
IEEE DOI Code:
WWW Link.
2402
Probability distribution, Image reconstruction,
Thresholding (Imaging), Superresolution, Standards, Transformers,
deep learning
BibRef
Cai, Q.[Qing],
Qian, Y.M.[Yi-Ming],
Li, J.X.[Jin-Xing],
Lyu, J.[Jun],
Yang, Y.H.[Yee-Hong],
Wu, F.[Feng],
Zhang, D.[David],
HIPA: Hierarchical Patch Transformer for Single Image Super
Resolution,
IP(32), 2023, pp. 3226-3237.
IEEE DOI
2306
Transformers, Feature extraction, Convolution, Image restoration,
Superresolution, Visualization, Image restoration,
attention-based position embedding
BibRef
Bilecen, B.B.[Bahri Batuhan],
Ayazoglu, M.[Mustafa],
Bicubic++: Slim, Slimmer, Slimmest Designing an Industry-Grade
Super-Resolution Network,
NTIRE23(1623-1332)
IEEE DOI
2309
BibRef
Liu, H.[Huan],
Shao, M.W.[Ming-Wen],
Qiao, Y.J.[Yuan-Jian],
Wan, Y.[Yecong],
Meng, D.Y.[De-Yu],
Unpaired Image Super-Resolution Using a Lightweight Invertible Neural
Network,
PR(144), 2023, pp. 109822.
Elsevier DOI
2310
Image super-resolution, Unpaired SR, Image degradation,
Invertible neural network, Generative adversarial network
BibRef
Wang, K.D.[Kai-Dong],
Liao, X.W.[Xiu-Wu],
Li, J.[Jun],
Meng, D.Y.[De-Yu],
Wang, Y.[Yao],
Hyperspectral Image Super-Resolution via Knowledge-Driven Deep
Unrolling and Transformer Embedded Convolutional Recurrent Neural
Network,
IP(32), 2023, pp. 4581-4594.
IEEE DOI
2309
BibRef
Fu, J.H.[Jia-Hong],
Wang, H.[Hong],
Xie, Q.[Qi],
Zhao, Q.[Qian],
Meng, D.Y.[De-Yu],
Xu, Z.B.[Zong-Ben],
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution,
ECCV22(XIX:235-253).
Springer DOI
2211
BibRef
Zhao, L.L.[Liang-Liang],
Gao, J.Y.[Jun-Yu],
Deng, D.[Donghu],
Li, X.L.[Xue-Long],
SSIR: Spatial shuffle multi-head self-attention for Single Image
Super-Resolution,
PR(148), 2024, pp. 110195.
Elsevier DOI
2402
Single Image Super-Resolution, Long-range attention, Vision transformer
BibRef
Jiang, Z.W.[Zhi-Wei],
Xue, Z.Z.[Zhi-Zhong],
Wang, J.[Jue],
Hu, Y.B.[Yi-Biao],
Zheng, Q.[Qiufu],
Single Image Denoising Based on Adaptive Fusion Dual-Domain Network,
IET-IPR(18), No. 3, 2024, pp. 561-571.
DOI Link Code:
WWW Link.
2402
adaptive signal processing, convolutional neural nets,
Gaussian noise, image denoising
BibRef
Zhang, J.Y.[Jing-Yi],
Wang, Z.W.[Zi-Wei],
Wang, H.Y.[Hao-Yu],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
Anycost Network Quantization for Image Super-Resolution,
IP(33), 2024, pp. 2279-2292.
IEEE DOI
2404
Quantization (signal), Superresolution, Training, Costs,
Complexity theory, Adaptation models, Task analysis, resource budget
BibRef
Tian, H.Y.[Hui-Yuan],
Zhang, L.[Li],
Li, S.J.[Shi-Jian],
Yao, M.[Min],
Pan, G.[Gang],
Multi-Depth Branch Network for Efficient Image Super-Resolution,
IVC(144), 2024, pp. 104949.
Elsevier DOI Code:
WWW Link.
2404
Efficient super-resolution, Multi-depth branch network,
Feature map visualization, Fourier spectral analysis, Feature fusion
BibRef
Wu, Q.Y.[Qian-Yu],
Hu, Z.Q.[Zhong-Qian],
Zhu, A.[Aichun],
Tang, H.[Hui],
Zou, J.X.[Jia-Xin],
Xi, Y.[Yan],
Chen, Y.[Yang],
A flow-based multi-scale learning network for single image stochastic
super-resolution,
SP:IC(125), 2024, pp. 117132.
Elsevier DOI Code:
WWW Link.
2405
Single image super-resolution, Attention mechanism,
Normalizing flow, Stochastic super-resolution
BibRef
Zhang, Z.Z.[Zhi-Zhong],
Xie, Y.[Yuan],
Zhang, C.[Chong],
Wang, Y.B.[Yan-Bo],
Qu, Y.[Yanyun],
Lin, S.H.[Shao-Hui],
Ma, L.Z.[Li-Zhuang],
Tian, Q.[Qi],
Dynamic image super-resolution via progressive contrastive
self-distillation,
PR(153), 2024, pp. 110502.
Elsevier DOI
2405
Single Image Super-Resolution, Model compression,
Model acceleration, Dynamic neural networks
BibRef
Qin, Y.[Yi],
Wang, J.[Jiarong],
Cao, S.[Shenyi],
Zhu, M.[Ming],
Sun, J.Q.[Jia-Qi],
Hao, Z.C.[Zhi-Cheng],
Jiang, X.[Xin],
SRBPSwin: Single-Image Super-Resolution for Remote Sensing Images
Using a Global Residual Multi-Attention Hybrid Back-Projection
Network Based on the Swin Transformer,
RS(16), No. 12, 2024, pp. 2252.
DOI Link
2406
BibRef
Xiao, J.[Jun],
Ye, Q.[Qian],
Zhao, R.[Rui],
Lam, K.M.[Kin-Man],
Wan, K.[Kao],
Deep Multi-Scale Feature Mixture Model for Image Super-Resolution
with Multiple-Focal-Length Degradation,
SP:IC(127), 2024, pp. 117139.
Elsevier DOI
2408
Single image super-resolution, Real-world degradation
BibRef
Mou, C.[Chong],
Wang, X.T.[Xin-Tao],
Wu, Y.[Yanze],
Shan, Y.[Ying],
Zhang, J.[Jian],
Empowering Real-World Image Super-Resolution With Flexible
Interactive Modulation,
PAMI(46), No. 11, November 2024, pp. 7317-7330.
IEEE DOI
2410
Degradation, Image restoration, Superresolution, Measurement, Modulation,
Kernel, Noise, Image super-resolution, real-world image degradation
BibRef
Mou, C.[Chong],
Wu, Y.[Yanze],
Wang, X.T.[Xin-Tao],
Dong, C.[Chao],
Zhang, J.[Jian],
Shan, Y.[Ying],
Metric Learning Based Interactive Modulation for Real-World
Super-Resolution,
ECCV22(XVII:723-740).
Springer DOI
2211
BibRef
Ji, X.[Xiang],
Wang, Z.X.[Zhi-Xiang],
Satoh, S.[Shin'ichi],
Zheng, Y.Q.[Yin-Qiang],
Single Image Deblurring with Row-dependent Blur Magnitude,
ICCV23(12235-12246)
IEEE DOI
2401
BibRef
Chao, J.H.[Jia-Hao],
Zhou, Z.[Zhou],
Gao, H.F.[Hong-Fan],
Gong, J.L.[Jia-Li],
Yang, Z.F.[Zheng-Feng],
Zeng, Z.B.[Zhen-Bing],
Dehbi, L.[Lydia],
Equivalent Transformation and Dual Stream Network Construction for
Mobile Image Super-Resolution,
CVPR23(14102-14111)
IEEE DOI
2309
BibRef
Yu, F.[Fanghua],
Wang, X.[Xintao],
Cao, M.[Mingdeng],
Li, G.[Gen],
Shan, Y.[Ying],
Dong, C.[Chao],
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware
Transformer,
CVPR23(13283-13292)
IEEE DOI
2309
BibRef
Yao, J.E.[Jie-En],
Tsao, L.Y.[Li-Yuan],
Lo, Y.C.[Yi-Chen],
Tseng, R.[Roy],
Chang, C.C.[Chia-Che],
Lee, C.Y.[Chun-Yi],
Local Implicit Normalizing Flow for Arbitrary-Scale Image
Super-Resolution,
CVPR23(1776-1785)
IEEE DOI
2309
BibRef
Chen, H.W.[Hao-Wei],
Xu, Y.S.[Yu-Syuan],
Hong, M.F.[Min-Fong],
Tsai, Y.M.[Yi-Min],
Kuo, H.K.[Hsien-Kai],
Lee, C.Y.[Chun-Yi],
Cascaded Local Implicit Transformer for Arbitrary-Scale
Super-Resolution,
CVPR23(18257-18267)
IEEE DOI
2309
BibRef
Qian, L.Y.[Liu-Yihui],
Liu, X.J.[Xiao-Jun],
Wu, J.[Juan],
Xu, X.Q.[Xiao-Qing],
Zeng, H.[Han],
360-Degree Image Super-Resolution Based on Single Image Sample and
Progressive Residual Generative Adversarial Network,
ICIVC22(654-661)
IEEE DOI
2301
Training, Measurement, Image quality, Solid modeling,
Computational modeling, Superresolution, Virtual reality, virtual reality
BibRef
Zhang, W.W.[Wen-Wei],
Wang, X.F.[Xiao-Feng],
Chen, D.F.[Dong-Fang],
Zhang, X.[Xuan],
Hybrid Adaptive Enhanced Network for Single Image Super Resolution,
ICPR22(345-351)
IEEE DOI
2212
Adaptation models, Adaptive systems, Superresolution, Semantics,
Feature extraction, Propagation losses
BibRef
Jiang, B.[Biao],
Long, K.[Kun],
Yang, Y.B.[Yu-Bin],
AEBSR: Active-Sampling and Energy-Based Single Image Super-Resolution,
ICIP22(1831-1835)
IEEE DOI
2211
Training, Image edge detection, Superresolution, Energy resolution,
Crops, Information entropy, Image reconstruction, Active sampling,
Deep Learning
BibRef
Dargahi, S.[Sedighe],
Aghagolzadeh, A.[Ali],
Ezoji, M.[Mehdi],
Single Image Super Resolution Using Multi-Path Convolutional Neural
Network,
IPRIA21(1-6)
IEEE DOI
2201
Learning systems, Visualization, Image resolution,
Memory management, Merging, Feature extraction, Data mining,
multi-scale manner
BibRef
Liu, Z.[Zhe],
Han, X.H.[Xian-Hua],
Generalized Deep Internal Learning for Hyperspectral Image Super
Resolution,
ICIP22(2641-2645)
IEEE DOI
2211
Training, Degradation, Superresolution, Estimation, Transforms,
Benchmark testing, Task analysis, Deep internal learning,
unsupervised learning
BibRef
Liu, Z.[Zhe],
Han, X.H.[Xian-Hua],
Sun, J.[Jiande],
Chen, Y.W.[Yen-Wei],
Unsupervised Generative Network for Blind Hyperspectral Image
Super-Resolution,
ICIP22(2621-2625)
IEEE DOI
2211
Degradation, Training, Superresolution, Imaging, Predictive models,
Benchmark testing, Spatial resolution, Hyperspectral image SR,
degradation model
BibRef
Liu, Z.[Zhe],
Han, X.H.[Xian-Hua],
Deep RGB-Driven Learning Network for Unsupervised Hyperspectral Image
Super-resolution,
MLCSA22(226-239).
Springer DOI
2307
BibRef
Yamawaki, K.[Kazuhiro],
Han, X.H.[Xian-Hua],
Deep Blind Un-Supervised Learning Network for Single Image Super
Resolution,
ICIP21(1789-1793)
IEEE DOI
2201
Degradation, Training, Deep learning, Image resolution,
Network architecture, Benchmark testing, Performance gain,
generative network
BibRef
Bian, P.C.[Peng-Cheng],
Zheng, Z.L.[Zhong-Long],
Zhang, D.W.[Da-Wei],
Chen, L.Y.[Li-Yuan],
Li, M.[Minglu],
Single Image Super-Resolution Via Global-Context Attention Networks,
ICIP21(1794-1798)
IEEE DOI
2201
Correlation, Fuses, Aggregates, Superresolution, Stacking,
Benchmark testing, Single image super-resolution,
inter-group fusion
BibRef
Nishiyama, A.[Akito],
Ikehata, S.[Satoshi],
Aizawa, K.[Kiyoharu],
360° Single Image Super Resolution via Distortion-Aware Network and
Distorted Perspective Images,
ICIP21(1829-1833)
IEEE DOI
2201
Training, Image analysis, Superresolution, Merging, Imaging,
Training data, Distortion, 360° single image super-resolution,
distortion-aware
BibRef
Chen, T.Y.[Tian-Yu],
Xiao, G.Q.[Guo-Qiang],
Tang, X.Q.[Xiao-Qin],
Han, X.F.[Xian-Feng],
Ma, W.Z.[Wen-Zhuo],
Gou, X.Y.[Xin-Ye],
Cascade Attention Blend Residual Network for Single Image
Super-Resolution,
ICIP21(559-563)
IEEE DOI
2201
Adaptation models, Correlation, Superresolution, Benchmark testing,
Image restoration, Convolutional neural networks,
image restoration
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)
IEEE DOI
2105
Measurement, Learning systems, Visualization, Superresolution,
Benchmark testing, Image reconstruction
BibRef
Thurnhofer-Hemsi, K.[Karl],
Ruiz-Álvarez, G.[Guillermo],
Luque-Baena, R.M.[Rafael Marcos],
Molina-Cabello, M.A.[Miguel A.],
López-Rubio, E.[Ezequiel],
Performance of Deep Learning and Traditional Techniques in Single Image
Super-resolution of Noisy Images,
MOI2QDN20(623-638).
Springer DOI
2103
BibRef
Ma, H.L.[Hai-Long],
Chu, X.X.[Xiang-Xiang],
Zhang, B.[Bo],
Accurate and Efficient Single Image Super-resolution with Matrix
Channel Attention Network,
ACCV20(II:20-35).
Springer DOI
2103
BibRef
Wang, X.H.[Xue-Hui],
Wang, Q.[Qing],
Zhao, Y.Z.[Yu-Zhi],
Yan, J.C.[Jun-Chi],
Fan, L.[Lei],
Chen, L.[Long],
Lightweight Single-image Super-resolution Network with Attentive
Auxiliary Feature Learning,
ACCV20(II:268-285).
Springer DOI
2103
BibRef
Ai, W.,
Tu, X.,
Cheng, S.,
Xie, M.,
Single Image Super-Resolution Via Residual Neuron Attention Networks,
ICIP20(1586-1590)
IEEE DOI
2011
Neurons, Feature extraction, RNA, Image resolution, Training,
Image reconstruction, Mathematical model,
global context
BibRef
Mo, Z.T.[Zi-Tao],
He, X.Y.[Xiang-Yu],
Li, G.[Gang],
Chen, J.[Jian],
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.F.[Zhi-Fei],
Wang, Z.W.[Zhao-Wen],
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.L.[Jing-Lei],
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
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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
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
Liang, J.Y.[Jing-Yun],
Zhang, K.[Kai],
Gu, S.H.[Shu-Hang],
Van Gool, L.J.[Luc J.],
Timofte, R.[Radu],
Flow-based Kernel Prior with Application to Blind Super-Resolution,
CVPR21(10596-10605)
IEEE DOI
2111
Training, Runtime, Computational modeling, Superresolution,
Estimation, Network architecture, Stability analysis
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.
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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.[Tao],
Zhang, Y.[Yu],
Wu, X.J.[Xiao-Jun],
Lu, G.[Gang],
Hao, F.[Fei],
Zhang, Y.M.[Yu-Mei],
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.Y.[Shi-Yu],
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
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 .