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Super-resolution, Multi-layer perceptron, Probabilistic neural network;
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gradient methods, image resolution, image restoration, HR image,
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Two-Stream Action Recognition-Oriented Video Super-Resolution,
ICCV19(8798-8807)
IEEE DOI
2004
BibRef
Earlier:
Convolutional Neural Network-Based Video Super-Resolution for Action
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FG18(746-750)
IEEE DOI
1806
convolutional neural nets, image motion analysis,
image representation, image resolution, image sequences,
Task analysis.
Image recognition, Optical imaging, Optical losses,
Spatial resolution, Streaming media, Training, Action recognition,
video super-resolution
BibRef
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Huang, J.[Jie],
Cheng, Z.[Zhen],
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Space-Time Distillation for Video Super-Resolution,
CVPR21(2113-2122)
IEEE DOI
2111
Training, Performance evaluation, Knowledge engineering,
Wearable computers, Superresolution, Network architecture
BibRef
Yu, H.C.[Hai-Chao],
Liu, D.[Ding],
Shi, H.H.[Hong-Hui],
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Wang, X.C.[Xin-Chao],
Cross, B.[Brent],
Bramler, M.[Matthew],
Huang, T.S.[Thomas S.],
Computed Tomography Super-Resolution Using Convolutional Neural
Networks,
ICIP17(3944-3948)
IEEE DOI
1803
computerised tomography, feature extraction,
image reconstruction, image resolution,
Super-resolution (SR)
BibRef
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Celebi, M.E.[M. Emre],
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Diverse Adversarial Network for Image Super-Resolution,
SP:IC(74), 2019, pp. 191-200.
Elsevier DOI
1904
Super-resolution, Adversarial network, Diverse GAN, Deep learning
BibRef
Li, Y.[Yue],
Liu, D.[Dong],
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1812
data compression, image coding, image reconstruction,
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Weng, W.M.[Wen-Ming],
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Xiong, Z.W.[Zhi-Wei],
Boosting Event Stream Super-Resolution with a Recurrent Neural Network,
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Springer DOI
2211
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Fan, H.Z.[Han-Zhi],
Liu, D.[Dong],
Xiong, Z.W.[Zhi-Wei],
Wu, F.[Feng],
Two-Stage Convolutional Neural Network for Light Field
Super-Resolution,
ICIP17(1167-1171)
IEEE DOI
1803
Cameras, Convolutional neural networks, Correlation,
Spatial resolution, Training,
super-resolution
BibRef
Huang, Y.,
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Frangi, A.F.,
Cross-Modality Image Synthesis via Weakly Coupled and Geometry
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IEEE DOI
1804
BibRef
Earlier:
Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D
Medical Images Using Weakly-Supervised Joint Convolutional Sparse
Coding,
CVPR17(5787-5796)
IEEE DOI
1711
diseases, geometry, image matching, image registration,
image representation, learning (artificial intelligence),
sparse representation.
Biomedical imaging, Convolutional codes, Image coding,
Image reconstruction, Image resolution,
Training
BibRef
Zhang, F.[Fu],
Cai, N.[Nian],
Cen, G.D.[Guan-Dong],
Li, F.Y.[Fei-Yang],
Wang, H.[Han],
Chen, X.[Xindu],
Image Super-Resolution via a Novel Cascaded Convolutional Neural
Network Framework,
SP:IC(63), 2018, pp. 9-18.
Elsevier DOI
1804
Image super-resolution, Cascaded convolution neural network,
Multi-scale feature mapping, Residual learning, Gradient clipping
BibRef
Chu, J.,
Zhang, J.,
Lu, W.,
Huang, X.,
A Novel Multiconnected Convolutional Network for Super-Resolution,
SPLetters(25), No. 7, July 2018, pp. 946-950.
IEEE DOI
1807
convolution, feedforward neural nets, image representation,
image resolution, optimisation, SISR tasks,
super-resolution
BibRef
Zhao, J.W.[Jian-Wei],
Sun, T.T.[Tian-Tian],
Cao, F.L.[Fei-Long],
Image super-resolution via adaptive sparse representation and
self-learning,
IET-CV(12), No. 5, August 2018, pp. 753-761.
DOI Link
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BibRef
Li, X.H.[Xing-Hua],
Du, Z.S.[Zheng-Shun],
Huang, Y.Y.[Yan-Yuan],
Tan, Z.Y.[Zhen-Yu],
A deep translation (GAN) based change detection network for optical
and SAR remote sensing images,
PandRS(179), 2021, pp. 14-34.
Elsevier DOI
2108
Change detection, Deep translation,
Depthwise separable convolution, GAN, Multi-scale loss, Optical and SAR images
BibRef
Tan, Z.Y.[Zhen-Yu],
Yue, P.[Peng],
Di, L.P.[Li-Ping],
Tang, J.M.[Jun-Mei],
Deriving High Spatiotemporal Remote Sensing Images Using Deep
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RS(10), No. 7, 2018, pp. xx-yy.
DOI Link
1808
From High Temporal, Low Spatial resolution.
BibRef
Wen, R.[Ran],
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Sun, X.[Xian],
Wang, L.[Lei],
Image Superresolution Using Densely Connected Residual Networks,
SPLetters(25), No. 10, October 2018, pp. 1565-1569.
IEEE DOI
1810
image resolution, learning (artificial intelligence),
neural nets, densely connected residual networks,
residual learning
BibRef
Arun, P.V.[Pattathal V.],
Herrmann, I.[Ittai],
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Karnieli, A.[Arnon],
Convolutional network architectures for super-resolution/sub-pixel
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PR(88), 2019, pp. 431-446.
Elsevier DOI
1901
Sub-pixel mapping, Super-resolution,
Convolutional neural network, Class distribution, Drone, UAV
BibRef
Chen, Y.X.[Yan-Xiang],
Tan, H.D.[Hua-Dong],
Zhang, L.[Luming],
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Lu, Q.A.[Qi-Ang],
Hybrid image super-resolution using perceptual similarity from
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JVCIR(60), 2019, pp. 229-235.
Elsevier DOI
1903
Super-resolution, Hybrid method, Adaptive weight, Pre-trained VGG network
BibRef
Xu, J.,
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Fan, J.,
Zhao, X.,
Chang, Z.,
Self-Learning Super-Resolution Using Convolutional Principal
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MultMed(21), No. 5, May 2019, pp. 1108-1121.
IEEE DOI
1905
convolution, feature extraction, image matching,
image reconstruction, image resolution,
principal component analysis
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Jiang, K.,
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Wang, G.,
Lu, T.,
Jiang, J.,
Edge-Enhanced GAN for Remote Sensing Image Superresolution,
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IEEE DOI
1908
edge detection, feature extraction,
geophysical image processing, image enhancement, superresolution.
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IET-IPR(13), No. 14, 12 December 2019, pp. 2673-2679.
DOI Link
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Image Super-Resolution as a Defense Against Adversarial Attacks,
IP(29), No. 1, 2020, pp. 1711-1724.
IEEE DOI
1912
Image resolution, Perturbation methods, Computational modeling,
Manifolds, Transform coding, Robustness, Training,
image denoising
BibRef
Xue, S.K.[Sheng-Ke],
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Liu, F.[Fan],
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SIViP(14), No. 2, March 2020, pp. 257-265.
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Improved SRGAN for Remote Sensing Image Super-Resolution Across
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2004
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Elsevier DOI
2007
Grouped convolution, Low-dose X-ray CT, Residual-in-dense,
Super resolution, Wavelet sub-bands
BibRef
Kim, J.,
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Dual Back-Projection-Based Internal Learning for Blind
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IEEE DOI
2007
Super-resolution, blind super-resolution, internal learning
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Satellite Image Multi-Frame Super Resolution Using 3D Wide-Activation
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RS(12), No. 22, 2020, pp. xx-yy.
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2011
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Gao, H.X.[Hong-Xia],
Chen, Z.H.[Zhan-Hong],
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Image super-resolution based on conditional generative adversarial
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IET-IPR(14), No. 13, November 2020, pp. 3006-3013.
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2012
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2101
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DOI Link
2102
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Image compact-resolution and reconstruction using reversible network,
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Liu, Z.S.[Zhi-Song],
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Chan, Y.L.,
Photo-Realistic Image Super-Resolution via Variational Autoencoders,
CirSysVideo(31), No. 4, April 2021, pp. 1351-1365.
IEEE DOI
2104
Generative adversarial networks, Distortion,
Image reconstruction, Feature extraction, Distortion measurement,
divergence
BibRef
Liu, Z.S.[Zhi-Song],
Siu, W.C.[Wan-Chi],
Wang, L.W.[Li-Wen],
Variational AutoEncoder for Reference based Image Super-Resolution,
NTIRE21(516-525)
IEEE DOI
2109
Quantization (signal),
Computational modeling, Superresolution, Space exploration
BibRef
Liu, Z.S.[Zhi-Song],
Siu, W.C.[Wan-Chi],
Wang, L.W.[Li-Wen],
Li, C.,
Cani, M.,
Chan, Y.L.,
Unsupervised Real Image Super-Resolution via Generative Variational
AutoEncoder,
NTIRE20(1788-1797)
IEEE DOI
2008
Noise reduction, Training, Mathematical model, Spatial resolution,
Decoding, Distortion
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Tian, C.W.[Chun-Wei],
Xu, Y.[Yong],
Zuo, W.M.[Wang-Meng],
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Fei, L.[Lunke],
Lin, C.W.[Chia-Wen],
Coarse-to-Fine CNN for Image Super-Resolution,
MultMed(23), 2021, pp. 1489-1502.
IEEE DOI
2106
Feature extraction, Training, Image reconstruction, Fuses,
Visualization, Residual neural networks, Cascaded structure,
Image super-resolution
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Huang, Y.S.[Yong-Song],
Jiang, Z.[Zetao],
Lan, R.[Rushi],
Zhang, S.Q.[Shao-Qin],
Pi, K.[Kui],
Infrared Image Super-Resolution via Transfer Learning and PSRGAN,
SPLetters(28), 2021, pp. 982-986.
IEEE DOI
2106
Feature extraction, Transfer learning, Superresolution, Training,
Generators, Task analysis, Neural networks, Super-resolution,
image processing
BibRef
Jiang, Z.[Zetao],
Pi, K.[Kui],
Huang, Y.S.[Yong-Song],
Qian, Y.[Yi],
Zhang, S.Q.[Shao-Qin],
Difference Value Network for Image Super-Resolution,
SPLetters(28), 2021, pp. 1070-1074.
IEEE DOI
2106
Image reconstruction, Feature extraction, Convolution,
Superresolution, Training, Graphics, Correlation, difference value
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Islam, S.R.[Sheikh Rafiul],
Maity, S.P.[Santi P.],
Ray, A.K.[Ajoy Kumar],
On learning based compressed sensing for high resolution image
reconstruction,
IET-IPR(15), No. 2, 2021, pp. 393-404.
DOI Link
2106
BibRef
Zhang, D.Y.[Dong-Yang],
Shao, J.[Jie],
Liang, Z.W.[Zhen-Wen],
Gao, L.L.[Lian-Li],
Shen, H.T.[Heng Tao],
Large Factor Image Super-Resolution With Cascaded Convolutional
Neural Networks,
MultMed(23), 2021, pp. 2172-2184.
IEEE DOI
2107
Convolution, Convolutional neural networks, Image reconstruction,
Computer architecture, Computational efficiency,
image super-resolution
BibRef
Li, X.G.[Xiao-Guang],
Dong, N.[Ning],
Huang, J.L.[Jiang-Lu],
Zhuo, L.[Li],
Li, J.F.[Jia-Feng],
A discriminative self-attention cycle GAN for face super-resolution
and recognition,
IET-IPR(15), No. 11, 2021, pp. 2614-2628.
DOI Link
2108
BibRef
Xi, S.[Si],
Wei, J.[Jia],
Zhang, W.D.[Wei-Dong],
Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring
and Super-Resolution,
NTIRE21(532-540)
IEEE DOI
2109
Training, Superresolution,
Feature extraction, Image restoration
BibRef
Nachaoui, M.,
Afraites, L.,
Laghrib, A.,
A Regularization by Denoising super-resolution method based on
genetic algorithms,
SP:IC(99), 2021, pp. 116505.
Elsevier DOI
2111
Super-resolution, Genetic algorithms, Nonlocal regularization
BibRef
Castillo, A.[Angela],
Escobar, M.[María],
Pérez, J.C.[Juan C.],
Romero, A.[Andrés],
Timofte, R.[Radu],
Van Gool, L.J.[Luc J.],
Arbelaez, P.[Pablo],
Generalized Real-World Super-Resolution through Adversarial
Robustness,
AIM21(1855-1865)
IEEE DOI
2112
Degradation, Training, Adaptation models, Computational modeling,
Superresolution, Robustness
BibRef
Liu, Y.H.[Yan-Hong],
Li, S.[Sumei],
Liu, A.[Anqi],
Two-Way Guided Super-Resolution Reconstruction Network Based on
Gradient Prior,
ICIP21(1819-1823)
IEEE DOI
2201
Convolution, Aggregates, Superresolution, Benchmark testing,
Feature extraction, Image restoration, Super-resolution,
multi-scale
BibRef
Yan, Y.T.[Yi-Tong],
Liu, C.C.[Chuang-Chuang],
Chen, C.Y.[Chang-You],
Sun, X.F.[Xian-Fang],
Jin, L.C.[Long-Cun],
Peng, X.Y.[Xin-Yi],
Zhou, X.[Xiang],
Fine-Grained Attention and Feature-Sharing Generative Adversarial
Networks for Single Image Super-Resolution,
MultMed(24), 2022, pp. 1473-1487.
IEEE DOI
2204
Generators, Feature extraction, Superresolution,
Generative adversarial networks, Image reconstruction, Standards,
image super-resolution
BibRef
Hu, Y.T.[Yan-Ting],
Li, J.[Jie],
Huang, Y.F.[Yuan-Fei],
Gao, X.B.[Xin-Bo],
Image Super-Resolution With Self-Similarity Prior Guided Network and
Sample-Discriminating Learning,
CirSysVideo(32), No. 4, April 2022, pp. 1966-1985.
IEEE DOI
2204
Feature extraction, Superresolution, Image reconstruction,
Training, Optimization, Generative adversarial networks,
single image super-resolution
BibRef
Liu, Y.Q.[Yu-Qing],
Wang, S.Q.[Shi-Qi],
Zhang, J.[Jian],
Wang, S.S.[Shan-She],
Ma, S.W.[Si-Wei],
Gao, W.[Wen],
Iterative Network for Image Super-Resolution,
MultMed(24), 2022, pp. 2259-2272.
IEEE DOI
2205
Degradation, Superresolution, Optimization, Image restoration,
Visualization, Convolution, Training,
feature normalization
BibRef
Liu, Y.Q.[Yu-Qing],
Jia, Q.[Qi],
Fan, X.[Xin],
Wang, S.S.[Shan-She],
Ma, S.W.[Si-Wei],
Gao, W.[Wen],
Cross-SRN: Structure-Preserving Super-Resolution Network With Cross
Convolution,
CirSysVideo(32), No. 8, August 2022, pp. 4927-4939.
IEEE DOI
2208
Image edge detection, Convolution, Image restoration,
Feature extraction, Information filters, Visualization,
structure-preservation
BibRef
Yin, G.H.[Guang-Hao],
Wang, W.[Wei],
Yuan, Z.H.[Ze-Huan],
Ji, W.[Wei],
Yu, D.D.[Dong-Dong],
Sun, S.[Shouqian],
Chua, T.S.[Tat-Seng],
Wang, C.H.[Chang-Hu],
Conditional Hyper-Network for Blind Super-Resolution With Multiple
Degradations,
IP(31), 2022, pp. 3949-3960.
IEEE DOI
2206
Degradation, Task analysis, Feature extraction, Adaptation models,
Kernel, Training, Superresolution, Blind super-resolution,
multi-degradation shift
BibRef
Frizza, T.[Tristan],
Dansereau, D.G.[Donald G.],
Seresht, N.M.[Nagita Mehr],
Bewley, M.[Michael],
Semantically accurate super-resolution Generative Adversarial
Networks,
CVIU(221), 2022, pp. 103464.
Elsevier DOI
2206
Super-resolution, Semantic segmentation,
Generative adversarial networks, Multi-modal learning
BibRef
Tian, C.[Chunwei],
Xu, Y.[Yong],
Zuo, W.M.[Wang-Meng],
Lin, C.W.[Chia-Wen],
Zhang, D.[David],
Asymmetric CNN for Image Superresolution,
SMCS(52), No. 6, June 2022, pp. 3718-3730.
IEEE DOI
2206
Task analysis, Convolution, Training, Superresolution,
Feature extraction, Kernel, Degradation, Asymmetric architecture,
multiple degradation task
BibRef
Kong, L.[Linhua],
Wang, Y.M.[Yi-Ming],
Chang, D.X.[Dong-Xia],
Zhao, Y.[Yao],
Contour enhanced image super-resolution,
JVCIR(89), 2022, pp. 103659.
Elsevier DOI
2212
Contour, Attention mechanism, Deep convolution neural network
BibRef
Kim, H.[Heewon],
Hong, S.[Seokil],
Han, B.H.[Bo-Hyung],
Myeong, H.[Heesoo],
Lee, K.M.[Kyoung Mu],
Fine-grained neural architecture search for image super-resolution,
JVCIR(89), 2022, pp. 103654.
Elsevier DOI
2212
Image super-resolution, Neural architecture search, Convolutional neural network
BibRef
Bhasha, A.V.[A. Valli],
Reddy, B.D.V.[B. D. Venkatramana],
Automated Image Super Resolution with the Aid of Activation Function
Optimized Deep CNN and Adaptive Wavelet Lifting Approach,
IJIG(22), No. 5 2022, pp. 2250046.
DOI Link
2212
BibRef
Choi, Y.J.[Young-Ju],
Lee, Y.W.[Young-Woon],
Kim, B.G.[Byung-Gyu],
Group-based bi-directional recurrent wavelet neural network for
efficient video super-resolution (VSR),
PRL(164), 2022, pp. 246-253.
Elsevier DOI
2212
Attention mechanism, Discrete wavelet transform,
Recurrent neural network, Video super-resolution
BibRef
Chan, K.C.K.[Kelvin C.K.],
Xu, X.Y.[Xiang-Yu],
Wang, X.[Xintao],
Gu, J.[Jinwei],
Loy, C.C.[Chen Change],
GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond,
PAMI(45), No. 3, March 2023, pp. 3154-3168.
IEEE DOI
2302
BibRef
Earlier: A1, A3, A2, A4, A5:
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution,
CVPR21(14240-14249)
IEEE DOI
2111
Image restoration, Generative adversarial networks,
Task analysis, Superresolution, Generators, Faces, Optimization,
generative prior.
Runtime, Imaging, Switches, Generative adversarial networks
BibRef
Ma, H.C.[Hai-Chuan],
Liu, D.[Dong],
Wu, F.[Feng],
Rectified Wasserstein Generative Adversarial Networks for Perceptual
Image Restoration,
PAMI(45), No. 3, March 2023, pp. 3648-3663.
IEEE DOI
2302
Image restoration, Generative adversarial networks, Training,
Generators, Task analysis, Measurement, Superresolution,
Wasserstein GAN (WGAN)
BibRef
Zhao, G.H.[Guang-Hui],
Li, Q.X.[Qing-Xia],
Chen, Z.W.[Zhi-Wei],
Lei, Z.Y.[Zhen-Yu],
Xiao, C.W.[Cheng-Wang],
Huang, Y.H.[Yu-Hang],
Visibility Extension of 1-D Aperture Synthesis by a Residual CNN for
Spatial Resolution Enhancement,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link
2303
BibRef
Xu, N.J.[Nai-Jie],
Chen, X.H.[Xiao-Hui],
Cao, Y.L.[You-Long],
Zhang, W.[Wenyi],
Hybrid Post-Training Quantization for Super-Resolution Neural Network
Compression,
SPLetters(30), 2023, pp. 379-383.
IEEE DOI
2305
Quantization (signal), Neural networks, Distortion, Videos,
Sensitivity, Superresolution, Optimization,
super-resolution neural network
BibRef
Cao, J.F.[Jian-Fang],
Hu, X.H.[Xiao-Hui],
Cui, H.Y.[Hong-Yan],
Liang, Y.C.[Yun-Chuan],
Chen, Z.[Zeyu],
A generative adversarial network model fused with a self-attention
mechanism for the super-resolution reconstruction of ancient murals,
IET-IPR(17), No. 8, 2023, pp. 2336-2349.
DOI Link
2306
image processing, image reconstruction, image representation, image sampling
BibRef
Yuan, N.Z.[Nian-Zeng],
Sun, B.Y.[Bang-Yong],
Zheng, X.T.[Xiang-Tao],
Unsupervised real image super-resolution via knowledge distillation
network,
CVIU(234), 2023, pp. 103736.
Elsevier DOI
2307
Super-resolution, Knowledge distillation, Degradation module,
Convolutional neural network
BibRef
Altekruger, F.[Fabian],
Hertrich, J.[Johannes],
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for
Superresolution,
SIIMS(16), No. 3, 2023, pp. 1033-1067.
DOI Link
2309
BibRef
Wang, X.[Xuan],
Sun, L.J.[Li-Jun],
Chehri, A.[Abdellah],
Song, Y.C.[Yong-Chao],
A Review of GAN-Based Super-Resolution Reconstruction for Optical
Remote Sensing Images,
RS(15), No. 20, 2023, pp. 5062.
DOI Link
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BibRef
Pang, B.[Boyu],
Zhao, S.W.[Si-Wei],
Liu, Y.[Yinnian],
The Use of a Stable Super-Resolution Generative Adversarial Network
(SSRGAN) on Remote Sensing Images,
RS(15), No. 20, 2023, pp. 5064.
DOI Link
2310
BibRef
Guo, X.X.[Xiao-Xin],
Tu, Z.C.[Zhen-Chuan],
Zhang, H.R.[Hao-Ran],
Dong, H.L.[Hong-Liang],
Super-resolution reconstruction based on generative adversarial
networks with dual branch half instance normalization,
IET-IPR(18), No. 6, 2024, pp. 1434-1446.
DOI Link
2405
image reconstruction, image resolution
BibRef
Tang, N.[Ni],
Zhang, D.X.[Dong-Xiao],
Gao, J.[Juhao],
Qu, Y.[Yanyun],
FSRDiff: A fast diffusion-based super-resolution method using GAN,
JVCIR(101), 2024, pp. 104164.
Elsevier DOI
2406
Diffusion model, GAN, Super-resolution, Sampling speed
BibRef
Zuo, Y.F.[Yi-Fan],
Yao, W.H.[Wen-Hao],
Hu, Y.Q.[Yu-Qi],
Fang, Y.M.[Yu-Ming],
Liu, W.[Wei],
Peng, Y.X.[Yu-Xin],
Image Super-Resolution via Efficient Transformer Embedding Frequency
Decomposition With Restart,
IP(33), 2024, pp. 4670-4685.
IEEE DOI Code:
WWW Link.
2409
Transformers, Convolution, Convolutional neural networks,
Circuit faults, Complexity theory, Standards, Training,
octave convolution
BibRef
Yu, S.[Sibo],
Wu, K.[Kun],
Zhang, G.[Guang],
Yan, W.H.[Wan-Hong],
Wang, X.D.[Xiao-Dong],
Tao, C.[Chen],
MEFSR-GAN: A Multi-Exposure Feedback and Super-Resolution Multitask
Network via Generative Adversarial Networks,
RS(16), No. 18, 2024, pp. 3501.
DOI Link
2410
BibRef
Wu, G.[Gang],
Jiang, J.J.[Jun-Jun],
Jiang, J.P.[Jun-Peng],
Liu, X.M.[Xian-Ming],
Transforming Image Super-Resolution:
A ConvFormer-Based Efficient Approach,
IP(33), 2024, pp. 6071-6082.
IEEE DOI Code:
WWW Link.
2411
Transformers, Computational modeling, Superresolution, Kernel,
Computer architecture, Convolution, Mixers, self-attention
BibRef
Sharma, S.[Shailza],
Dhall, A.[Abhinav],
Johri, S.[Shikhar],
Kumar, V.[Vinay],
Singh, V.[Vivek],
Dual stage semantic information based generative adversarial network
for image super-resolution,
CVIU(250), 2025, pp. 104226.
Elsevier DOI
2501
Super-resolution, Convolutional Neural Networks,
Generative Adversarial Networks, Residual learning, Spectral normalization
BibRef
Zhang, Z.[Zhilu],
Wang, R.[Ruohao],
Zhang, H.Z.[Hong-Zhi],
Zuo, W.M.[Wang-Meng],
Self-Supervised Learning for Real-World Super-Resolution From Dual
and Multiple Zoomed Observations,
PAMI(47), No. 3, March 2025, pp. 1348-1361.
IEEE DOI
2502
Degradation, Training, Self-supervised learning, Superresolution,
Optical flow, Kernel, Cameras, Real world,
self-supervised learning
BibRef
Li, J.[Junyi],
Zhang, Z.[Zhilu],
Liu, X.Y.[Xiao-Yu],
Feng, C.[Chaoyu],
Wang, X.T.[Xiao-Tao],
Lei, L.[Lei],
Zuo, W.M.[Wang-Meng],
Spatially Adaptive Self-Supervised Learning for Real-World Image
Denoising,
CVPR23(9914-9924)
IEEE DOI
2309
BibRef
Zhang, Z.[Zhilu],
Wang, R.[Ruohao],
Zhang, H.Z.[Hong-Zhi],
Chen, Y.J.[Yun-Jin],
Zuo, W.M.[Wang-Meng],
Self-supervised Learning for Real-World Super-Resolution from Dual
Zoomed Observations,
ECCV22(XVIII:610-627).
Springer DOI
2211
BibRef
Tian, Y.C.[Yu-Chuan],
Chen, H.T.[Han-Ting],
Xu, C.[Chao],
Wang, Y.H.[Yun-He],
Image Processing GNN: Breaking Rigidity in Super-Resolution,
CVPR24(24108-24117)
IEEE DOI
2410
Codes, Superresolution, Nearest neighbor methods,
Extraterrestrial measurements, Rigidity, Image reconstruction, GNN
BibRef
Trevithick, A.[Alex],
Chan, M.[Matthew],
Takikawa, T.[Towaki],
Iqbal, U.[Umar],
de Mello, S.[Shalini],
Chandraker, M.[Manmohan],
Ramamoorthi, R.[Ravi],
Nagano, K.[Koki],
What You See is What You GAN: Rendering Every Pixel for High-Fidelity
Geometry in 3D GANs,
CVPR24(22765-22775)
IEEE DOI
2410
Geometry, Training, Shape, Superresolution,
Rendering (computer graphics), Generative adversarial networks
BibRef
Shou, J.[Jiateng],
Xiao, Z.[Zeyu],
Deng, S.Y.[Shi-Yu],
Huang, W.[Wei],
Shi, P.[Peiyao],
Zhang, R.[Ruobing],
Xiong, Z.W.[Zhi-Wei],
Wu, F.[Feng],
Learning Large-Factor EM Image Super-Resolution with Generative
Priors,
CVPR24(11313-11322)
IEEE DOI Code:
WWW Link.
2410
Image segmentation, Visualization, Scanning electron microscopy,
Accuracy, Superresolution, Coherence, Vectors, Super-resolution,
Generative priors
BibRef
Li, J.[Jia],
Chen, Z.[Ziling],
Wu, X.L.[Xiao-Long],
Wang, L.[Lu],
Wang, B.B.[Bei-Bei],
Zhang, L.[Lei],
Neural Super-Resolution for Real-Time Rendering with Radiance
Demodulation,
CVPR24(4357-4367)
IEEE DOI Code:
WWW Link.
2410
Video games, Superresolution, Neural networks, Lighting,
Virtual reality, Rendering (computer graphics),
Super-resolution
BibRef
Korkmaz, C.[Cansu],
Tekalp, A.M.[A. Murat],
Dogan, Z.[Zafer],
Training Generative Image Super-Resolution Models by Wavelet-Domain
Losses Enables Better Control of Artifacts,
CVPR24(5926-5936)
IEEE DOI
2410
Training, Visualization, Inverse problems, Computational modeling,
Superresolution, Hafnium, image super-resolution,
adversarial training
BibRef
Lee, H.[Hwayoon],
Kang, K.[Kyoungkook],
Lee, H.[Hyeongmin],
Baek, S.H.[Seung-Hwan],
Cho, S.H.[Sung-Hyun],
UGPNet: Universal Generative Prior for Image Restoration,
WACV24(1587-1597)
IEEE DOI
2404
Uncertainty, Computational modeling, Superresolution,
Noise reduction, Merging, Measurement uncertainty, Algorithms,
image and video synthesis
BibRef
Ma, W.[Wen],
Lou, Q.W.[Qiu-Wen],
Kazemi, A.[Arman],
Faraone, J.[Julian],
Afzal, T.[Tariq],
Super Efficient Neural Network for Compression Artifacts Reduction
and Super Resolution,
VAQuality24(460-468)
IEEE DOI
2404
Training, Performance evaluation, Interpolation, Superresolution,
Neural networks, Bit rate, Hardware
BibRef
Park, J.K.[Joon-Kyu],
Son, S.[Sanghyun],
Lee, K.M.[Kyoung Mu],
Content-Aware Local GAN for Photo-Realistic Super-Resolution,
ICCV23(10551-10560)
IEEE DOI Code:
WWW Link.
2401
BibRef
Rempakos, P.[Pantelis],
Vrigkas, M.[Michalis],
Plissiti, M.E.[Marina E.],
Nikou, C.[Christophoros],
Spatial Transformer Generative Adversarial Network for Image
Super-resolution,
CIAP23(I:399-411).
Springer DOI
2312
BibRef
Pan, P.C.[Pin-Chi],
Hsu, T.H.[Tzu-Hao],
Wei, W.L.[Wen-Li],
Lin, J.C.[Jen-Chun],
Global-Local Awareness Network for Image Super-Resolution,
ICIP23(1150-1154)
IEEE DOI
2312
BibRef
Kim, D.[Dayeon],
Kim, M.C.[Mun-Churl],
SGSR: A Saliency-Guided Image Super-Resolution Network,
ICIP23(980-984)
IEEE DOI
2312
BibRef
Wei, M.[Min],
Zhang, X.S.[Xue-Song],
Super-Resolution Neural Operator,
CVPR23(18247-18256)
IEEE DOI
2309
BibRef
Panaetov, A.[Alexander],
Daou, K.E.[Karim Elhadji],
Samenko, I.[Igor],
Tetin, E.[Evgeny],
Ivanov, I.[Ilya],
RDRN: Recursively Defined Residual Network for Image Super-resolution,
ACCV22(II:629-645).
Springer DOI
2307
BibRef
Zhang, X.D.[Xin-Dong],
Zeng, H.[Hui],
Zhang, L.[Lei],
Efficient Hardware-aware Neural Architecture Search for Image
Super-resolution on Mobile Devices,
ACCV22(III:409-426).
Springer DOI
2307
BibRef
Chira, D.[Darius],
Haralampiev, I.[Ilian],
Winther, O.[Ole],
Dittadi, A.[Andrea],
Liévin, V.[Valentin],
Image Super-resolution with Deep Variational Autoencoders,
AIM22(395-411).
Springer DOI
2304
BibRef
Yoo, J.[Jinsu],
Kim, T.[Taehoon],
Lee, S.[Sihaeng],
Kim, S.H.[Seung Hwan],
Lee, H.L.[Hong-Lak],
Kim, T.H.[Tae Hyun],
Enriched CNN-Transformer Feature Aggregation Networks for
Super-Resolution,
WACV23(4945-4954)
IEEE DOI
2302
Aggregates, Superresolution, Benchmark testing, Transformers,
Cognition, Algorithms: Computational photography,
Low-level and physics-based vision
BibRef
Kansy, M.[Manuel],
Balletshofer, J.[Julian],
Naruniec, J.[Jacek],
Schroers, C.[Christopher],
Mignone, G.[Graziana],
Gross, M.[Markus],
Weber, R.M.[Romann M.],
Self-Supervised Effective Resolution Estimation with Adversarial
Augmentations,
VAQuality23(573-582)
IEEE DOI
2302
Training, Image quality, Superresolution, Neural networks,
Estimation, Training data, Self-supervised learning
BibRef
Xue, B.X.[Bo-Xiang],
Zhou, Z.H.[Zheng-Hua],
Multi-scale Visual Aggregation Residual Network for Super-Resolution,
ICIVC22(682-687)
IEEE DOI
2301
Visualization, Convolution, Superresolution, Neural networks, MIMICs,
Feature extraction, Kernel, Multi-scale Residual Network, Super-Resolution
BibRef
Wang, S.[Sen],
Zheng, J.[Jin],
Multi-Scale Detail Enhancement Network for Image Super-Resolution,
ICPR22(161-167)
IEEE DOI
2212
Visualization, Fuses, Superresolution, Feature extraction,
Image restoration, Data mining, Convolutional neural networks
BibRef
Wang, Y.[Yan],
Edge-enhanced Feature Distillation Network for Efficient
Super-Resolution,
NTIRE22(776-784)
IEEE DOI
2210
Training, Convolution, Image edge detection,
Superresolution, Neural networks
BibRef
Gu, J.J.[Jin-Jin],
Cai, H.M.[Hao-Ming],
Dong, C.Y.[Chen-Yu],
Zhang, R.F.[Ruo-Fan],
Zhang, Y.[Yulun],
Yang, W.M.[Wen-Ming],
Yuan, C.[Chun],
Super-Resolution by Predicting Offsets: An Ultra-Efficient
Super-Resolution Network for Rasterized Images,
ECCV22(XIX:583-598).
Springer DOI
2211
BibRef
Ji, Y.T.[Yan-Tao],
Jiang, P.L.[Pei-Lin],
Shi, J.G.[Jin-Gang],
Guo, Y.[Yu],
Zhang, R.[Ruiteng],
Wang, F.[Fei],
Information-Growth Swin Transformer Network for Image
Super-Resolution,
ICIP22(3993-3997)
IEEE DOI
2211
Adaptation models, Current transformers, Superresolution,
Benchmark testing, Feature extraction, Data mining, Transformer
BibRef
Vasileiou, C.[Christos],
Smith, J.[Josiah],
Thiagarajan, S.[Shiva],
Nigh, M.[Matthew],
Makris, Y.[Yiorgos],
Torlak, M.[Murat],
Efficient CNN-Based Super Resolution Algorithms for MMwave Mobile
Radar Imaging,
ICIP22(3803-3807)
IEEE DOI
2211
Laser radar, Superresolution, Radar imaging, Apertures,
Radar polarimetry, Convolutional neural networks, Task analysis,
Depth-wise Convolution
BibRef
Korkmaz, C.[Cansu],
Tekalp, A.M.[A. Murat],
Dogan, Z.[Zafer],
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting from
Class-Specific Image Priors,
ICIP22(2816-2820)
IEEE DOI
2211
Training, Measurement, Degradation, Fuses, Superresolution,
image super-resolution, multiple learned models, zero-shot learning
BibRef
Montanaro, A.[Antonio],
Valsesia, D.[Diego],
Magli, E.[Enrico],
Exploring the Solution Space of Linear Inverse Problems with GAN
Latent Geometry,
ICIP22(1381-1385)
IEEE DOI
2211
Geometry, Inverse problems, Atmospheric measurements,
Superresolution, Semantics, Optimization methods, GANs
BibRef
Li, J.C.[Jia-Cheng],
Chen, C.[Chang],
Cheng, Z.[Zhen],
Xiong, Z.W.[Zhi-Wei],
MuLUT: Cooperating Multiple Look-Up Tables for Efficient Image
Super-Resolution,
ECCV22(XVIII:238-256).
Springer DOI
2211
BibRef
Li, Z.H.[Zhi-Heng],
Li, M.[Muheng],
Fan, J.[Jixuan],
Chen, L.[Lei],
Tang, Y.S.[Yan-Song],
Lu, J.W.[Ji-Wen],
Zhou, J.[Jie],
Learning Dual-level Deformable Implicit Representation for Real-world
Scale Arbitrary Super-resolution,
ECCV24(LXIX: 352-368).
Springer DOI
2412
BibRef
Ma, C.[Cheng],
Zhang, J.Y.[Jing-Yi],
Zhou, J.[Jie],
Lu, J.W.[Ji-Wen],
Learning Series-Parallel Lookup Tables for Efficient Image
Super-Resolution,
ECCV22(XVII:305-321).
Springer DOI
2211
BibRef
Zhuang, K.[Kai],
Yuan, Y.[Yuan],
Wang, Q.[Qi],
DTransGAN: Deblurring Transformer Based on Generative Adversarial
Network,
ICIP22(701-705)
IEEE DOI
2211
Training, Image registration, Surveillance, Semantics, Transformers,
Generative adversarial networks, Cameras, Motion deblurring, skip connection
BibRef
Wang, W.[Wei],
Zhang, H.C.[Hao-Chen],
Yuan, Z.H.[Ze-Huan],
Wang, C.H.[Chang-Hu],
Unsupervised Real-World Super-Resolution:
A Domain Adaptation Perspective,
ICCV21(4298-4307)
IEEE DOI
2203
Training, Convolution, Superresolution, Neural networks, Force,
Generative adversarial networks, Decoding,
BibRef
Yoon, K.J.[Kwang-Jin],
Simple and Efficient Unpaired Real-world Super-Resolution using Image
Statistics,
AIM21(1983-1990)
IEEE DOI
2112
Measurement, Training, Degradation,
Superresolution, Generative adversarial networks
BibRef
Wang, L.G.[Long-Guang],
Wang, Y.Q.[Ying-Qian],
Lin, Z.P.[Zai-Ping],
Yang, J.G.[Jun-Gang],
An, W.[Wei],
Guo, Y.L.[Yu-Lan],
Learning A Single Network for Scale-Arbitrary Super-Resolution,
ICCV21(4781-4790)
IEEE DOI
2203
Adaptation models, Costs, Convolution, Computational modeling,
Superresolution, Task analysis,
Vision applications and systems
BibRef
Pesavento, M.[Marco],
Volino, M.[Marco],
Hilton, A.[Adrian],
Attention-based Multi-Reference Learning for Image Super-Resolution,
ICCV21(14677-14686)
IEEE DOI
2203
Superresolution, Memory management, Spatial coherence,
Graphics processing units, Benchmark testing,
Datasets and evaluation
BibRef
Tu, Z.J.[Zhi-Jun],
Hu, J.[Jie],
Chen, H.T.[Han-Ting],
Wang, Y.H.[Yun-He],
Toward Accurate Post-Training Quantization for Image Super Resolution,
CVPR23(5856-5865)
IEEE DOI
2309
BibRef
Xie, W.B.[Wen-Bin],
Song, D.H.[De-Hua],
Xu, C.[Chang],
Xu, C.J.[Chun-Jing],
Zhang, H.[Hui],
Wang, Y.H.[Yun-He],
Learning Frequency-aware Dynamic Network for Efficient
Super-Resolution,
ICCV21(4288-4297)
IEEE DOI
2203
Training, Visualization, Frequency-domain analysis,
Computational modeling, Superresolution, Computer architecture,
Machine learning architectures and formulations
BibRef
Duong, V.V.[Vinh Van],
Huu, T.N.[Thuc Nguyen],
Yim, J.[Jonghoon],
Jeon, B.W.[Byeung-Woo],
A Fast and Efficient Super-Resolution Network Using Hierarchical
Dense Residual Learning,
ICIP21(1809-1813)
IEEE DOI
2201
Training, Performance evaluation, Superresolution,
Computational efficiency, Convolutional neural networks,
hierarchical dense residual learning
BibRef
Gao, T.X.[Tian-Xiao],
Xiong, R.Q.[Rui-Qin],
Zhao, R.[Rui],
Zhang, J.[Jian],
Zhu, S.Y.[Shu-Yuan],
Huang, T.J.[Tie-Jun],
Recover the Residual of Residual: Recurrent Residual Refinement
Network for Image Super-Resolution,
ICIP21(1804-1808)
IEEE DOI
2201
Superresolution, Optimization methods, Transforms,
Convolutional neural networks, Image reconstruction,
recurrent convolutional neural network
BibRef
Michelini, P.N.[Pablo Navarrete],
Liu, H.[Hanwen],
Lu, Y.[Yunhua],
Jiang, X.Q.[Xing-Qun],
Back-Projection Pipeline,
ICIP21(1949-1953)
IEEE DOI
2201
Image resolution, Heuristic algorithms, Pipelines, Superresolution,
Iterative algorithms, Task analysis, Super-resolution,
causality
BibRef
Huang, Q.[Qiu],
Zhang, Y.X.[Yu-Xin],
Hu, H.J.[Hao-Ji],
Zhu, Y.D.[Yong-Dong],
Zhao, Z.F.[Zhi-Feng],
Binarizing Super-Resolution Networks by Pixel-Correlation Knowledge
Distillation,
ICIP21(1814-1818)
IEEE DOI
2201
Knowledge engineering, Quantization (signal),
Computational modeling, Superresolution, Knowledge Distillation
BibRef
Keles, O.[Onur],
Tekalp, A.M.[A. Murat],
Malik, J.[Junaid],
Kiranyaz, S.[Serkan],
Self-Organized Residual Blocks for Image Super-Resolution,
ICIP21(589-593)
IEEE DOI
2201
Training, Superresolution, Neurons, Computer architecture,
Network architecture, Taylor series, Task analysis, super-resolution
BibRef
Song, D.H.[De-Hua],
Wang, Y.H.[Yun-He],
Chen, H.T.[Han-Ting],
Xu, C.[Chang],
Xu, C.J.[Chun-Jing],
Tao, D.C.[Da-Cheng],
AdderSR: Towards Energy Efficient Image Super-Resolution,
CVPR21(15643-15652)
IEEE DOI
2111
Energy consumption, Visualization, Computational modeling,
Superresolution, Refining, Neural networks
BibRef
Zhang, L.[Leheng],
Li, Y.[Yawei],
Zhou, X.Y.[Xing-Yu],
Zhao, X.R.[Xiao-Rui],
Gu, S.[Shuhang],
Transcending the Limit of Local Window: Advanced Super-Resolution
Transformer with Adaptive Token Dictionary,
CVPR24(2856-2865)
IEEE DOI
2410
Dictionaries, Adaptive systems, Superresolution, Training data,
Machine learning, Artificial neural networks,
vision transformer
BibRef
Wang, L.G.[Long-Guang],
Guo, Y.L.[Yu-Lan],
Li, J.C.[Jun-Cheng],
Liu, H.[Hongda],
Zhao, Y.[Yang],
Wang, Y.Q.[Ying-Qian],
Jin, Z.[Zhi],
Gu, S.H.[Shu-Hang],
Timofte, R.[Radu],
NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and
Results,
NTIRE24(6198-6207)
IEEE DOI
2410
Degradation, Superresolution, Benchmark testing
BibRef
Elezabi, O.[Omar],
Wu, Z.W.[Zong-Wei],
Timofte, R.[Radu],
Enhanced Super-resolution Training via Mimicked Alignment for
Real-world Scenes,
ACCV24(IV: 226-245).
Springer DOI
2412
BibRef
Wei, Y.X.[Yun-Xuan],
Gu, S.H.[Shu-Hang],
Li, Y.[Yawei],
Timofte, R.[Radu],
Jin, L.C.[Long-Cun],
Song, H.J.[Heng-Jie],
Unsupervised Real-World Image Super Resolution via Domain-Distance
Aware Training,
CVPR21(13380-13389)
IEEE DOI
2111
Training, Philosophical considerations, Codes,
Superresolution, Training data
BibRef
Kong, X.T.[Xiang-Tao],
Zhao, H.[Hengyuan],
Qiao, Y.[Yu],
Dong, C.[Chao],
ClassSR: A General Framework to Accelerate Super-Resolution Networks
by Data Characteristic,
CVPR21(12011-12020)
IEEE DOI
2111
Training, Superresolution, Pipelines, Containers,
Image restoration
BibRef
Wu, H.P.[Hai-Ping],
Wang, X.L.[Xiao-Long],
Contrastive Learning of Image Representations with Cross-Video
Cycle-Consistency,
ICCV21(10129-10139)
IEEE DOI
2203
Representation learning, Visualization, Image recognition,
Semantics, Image representation, Object tracking,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Chen, Y.[Yinbo],
Liu, S.F.[Si-Fei],
Wang, X.L.[Xiao-Long],
Learning Continuous Image Representation with Local Implicit Image
Function,
CVPR21(8624-8634)
IEEE DOI
2111
Training, Bridges, Visualization,
Superresolution, Image representation
BibRef
Zhang, Y.[Yiman],
Chen, H.T.[Han-Ting],
Chen, X.H.[Xing-Hao],
Deng, Y.P.[Yi-Ping],
Xu, C.J.[Chun-Jing],
Wang, Y.H.[Yun-He],
Data-Free Knowledge Distillation For Image Super-Resolution,
CVPR21(7848-7857)
IEEE DOI
2111
Training, Knowledge engineering, Image coding, Superresolution,
Training data, Smart cameras, Generators
BibRef
Xing, W.Z.[Wen-Zhu],
Egiazarian, K.[Karen],
End-to-End Learning for Joint Image Demosaicing, Denoising and
Super-Resolution,
CVPR21(3506-3515)
IEEE DOI
2111
Training, Superresolution, Noise reduction, Switches,
Image restoration, Convolutional neural networks
BibRef
Kim, Y.[Younggeun],
Son, D.[Donghee],
Noise Conditional Flow Model for Learning the Super-Resolution Space,
NTIRE21(424-432)
IEEE DOI
2109
Training, Degradation, Visualization,
Computational modeling, Superresolution
BibRef
Cho, W.Y.[Woo-Yeong],
Son, S.[Sanghyeok],
Kim, D.S.[Dae-Shik],
Weighted Multi-Kernel Prediction Network for Burst Image
Super-Resolution,
NTIRE21(404-413)
IEEE DOI
2109
Visualization, Computational modeling, Superresolution,
Prediction methods, Motion compensation, Image restoration
BibRef
Chen, L.[Liang],
Zhang, J.W.[Jia-Wei],
Pan, J.S.[Jin-Shan],
Lin, S.N.[Song-Nan],
Fang, F.[Faming],
Ren, J.S.[Jimmy S.],
Learning a Non-blind Deblurring Network for Night Blurry Images,
CVPR21(10537-10545)
IEEE DOI
2111
Deconvolution, Convolution, Noise reduction,
Estimation, Image restoration
BibRef
Bai, H.R.[Hao-Ran],
Cheng, S.S.[Song-Sheng],
Tang, J.H.[Jin-Hui],
Pan, J.S.[Jin-Shan],
Learning A Cascaded Non-Local Residual Network for Super-resolving
Blurry Images,
NTIRE21(223-232)
IEEE DOI
2109
Training, Image edge detection,
Superresolution, Benchmark testing, Image restoration
BibRef
Rad, M.S.[Mohammad Saeed],
Yu, T.[Thomas],
Musat, C.[Claudiu],
Ekenel, H.K.[Hazim Kemal],
Bozorgtabar, B.[Behzad],
Thiran, J.P.[Jean-Philippe],
Benefiting from Bicubically Down-Sampled Images for Learning
Real-World Image Super-Resolution,
WACV21(1589-1598)
IEEE DOI
2106
Degradation, Training, Analytical models,
Computational modeling, Superresolution
BibRef
Roziere, B.[Baptiste],
Rakotonirina, N.C.[Nathanaël Carraz],
Hosu, V.[Vlad],
Rasoanaivo, A.[Andry],
Lin, H.[Hanhe],
Couprie, C.[Camille],
Teytaud, O.[Olivier],
Tarsier: Evolving Noise Injection in Super-Resolution GANs,
ICPR21(7028-7035)
IEEE DOI
2105
Training, Image quality, Gaussian noise, Superresolution,
Quality assessment, Standards
BibRef
Lee, W.[Wonkyung],
Lee, J.[Junghyup],
Kim, D.[Dohyung],
Ham, B.[Bumsub],
Learning with Privileged Information for Efficient Image
Super-resolution,
ECCV20(XXIV:465-482).
Springer DOI
2012
BibRef
Park, S.[Seobin],
Yoo, J.[Jinsu],
Cho, D.[Donghyeon],
Kim, J.[Jiwon],
Kim, T.H.[Tae Hyun],
Fast Adaptation to Super-resolution Networks via Meta-learning,
ECCV20(XXVII:754-769).
Springer DOI
2011
BibRef
Mousavi, S.,
Lee, D.,
Griffin, T.,
Steadman, D.,
Mockus, A.,
Collaborative Learning Of Semi-Supervised Clustering And
Classification For Labeling Uncurated Data,
ICIP20(1716-1720)
IEEE DOI
2011
Labeling, Manuals, Data models, Training data, Training,
Image analysis, Analytical models, image labeling, CNNs
BibRef
He, Z.,
Dai, T.,
Lu, J.,
Jiang, Y.,
Xia, S.T.,
FAKD: Feature-Affinity Based Knowledge Distillation for Efficient
Image Super-Resolution,
ICIP20(518-522)
IEEE DOI
2011
Knowledge engineering, Computational modeling, Correlation,
Image resolution, Task analysis, Feature extraction,
Convolutional neural networks
BibRef
Lee, R.[Royson],
Dudziak, L.[Lukasz],
Abdelfattah, M.[Mohamed],
Venieris, S.I.[Stylianos I.],
Kim, H.[Hyeji],
Wen, H.K.[Hong-Kai],
Lane, N.D.[Nicholas D.],
Journey Towards Tiny Perceptual Super-Resolution,
ECCV20(XXVI:85-102).
Springer DOI
2011
Embedded super-resolution.
BibRef
Xie, Y.C.[Yan-Chun],
Xiao, J.M.[Ji-Min],
Sun, M.J.[Ming-Jie],
Yao, C.[Chao],
Huang, K.Z.[Kai-Zhu],
Feature Representation Matters: End-to-end Learning for Reference-based
Image Super-resolution,
ECCV20(IV:230-245).
Springer DOI
2011
BibRef
Lugmayr, A.[Andreas],
Danelljan, M.[Martin],
Van Gool, L.J.[Luc J.],
Timofte, R.[Radu],
Srflow: Learning the Super-resolution Space with Normalizing Flow,
ECCV20(V:715-732).
Springer DOI
2011
BibRef
Li, S.,
Zhu, H.,
Zha, K.,
Li, W.,
Super-Resolution Reconstruction Algorithm of Target Image Based on
Learning Background,
ICIVC20(133-138)
IEEE DOI
2009
Image reconstruction, Dictionaries, Image restoration,
Spatial resolution, Training, Video surveillance, super-resolution,
interesting target
BibRef
Gao, W.[Wei],
Tao, L.F.[Lv-Fang],
Zhou, L.J.[Lin-Jie],
Yang, D.H.[Ding-Hao],
Zhang, X.Y.[Xiao-Yu],
Guo, Z.X.[Zi-Xuan],
Low-rate Image Compression with Super-resolution Learning,
CLIC20(607-610)
IEEE DOI
2008
Image coding, Image resolution, Bit rate, Training, Codecs,
Pattern recognition
BibRef
Xu, Y.,
Tseng, S.R.,
Tseng, Y.,
Kuo, H.,
Tsai, Y.,
Unified Dynamic Convolutional Network for Super-Resolution With
Variational Degradations,
CVPR20(12493-12502)
IEEE DOI
2008
Convolution, Degradation, Kernel, Feature extraction, Training,
Spatial resolution
BibRef
Yang, F.,
Yang, H.,
Fu, J.,
Lu, H.,
Guo, B.,
Learning Texture Transformer Network for Image Super-Resolution,
CVPR20(5790-5799)
IEEE DOI
2008
Feature extraction, Image resolution, Task analysis,
Machine learning, Indexes
BibRef
Ahn, N.,
Yoo, J.,
Sohn, K.,
SimUSR: A Simple but Strong Baseline for Unsupervised Image
Super-resolution,
NTIRE20(1953-1961)
IEEE DOI
2008
Image resolution, Runtime, Training, Task analysis,
Adaptation models, Degradation, Kernel
BibRef
Lee, J.Y.[Jun-Yeop],
Park, J.[Jaihyun],
Lee, K.[Kanghyu],
Min, J.[Jeongki],
Kim, G.[Gwantae],
Lee, B.[Bokyeung],
Ku, B.[Bonhwa],
Han, D.K.[David K.],
Ko, H.S.[Han-Seok],
FBRNN: Feedback Recurrent Neural Network for Extreme Image
Super-Resolution,
NTIRE20(2021-2028)
IEEE DOI
2008
Image resolution, Image reconstruction, Training,
Feature extraction, Computational modeling,
Image restoration
BibRef
Yoo, J.,
Ahn, N.,
Sohn, K.,
Rethinking Data Augmentation for Image Super-resolution:
A Comprehensive Analysis and a New Strategy,
CVPR20(8372-8381)
IEEE DOI
2008
Task analysis, Spatial resolution, Training, Image restoration,
Data models, Adaptation models
BibRef
Liu, J.[Jie],
Zhang, W.J.[Wen-Jie],
Tang, Y.T.[Yu-Ting],
Tang, J.[Jie],
Wu, G.S.[Gang-Shan],
Residual Feature Aggregation Network for Image Super-Resolution,
CVPR20(2356-2365)
IEEE DOI
2008
Feature extraction, Convolution, Spatial resolution,
Image reconstruction, Training, Task analysis
BibRef
Maeda, S.[Shunta],
Unpaired Image Super-Resolution Using Pseudo-Supervision,
CVPR20(288-297)
IEEE DOI
2008
Training, Generators, Degradation, Image resolution,
Image reconstruction, Kernel
BibRef
Rout, L.,
Shah, S.,
Moorthi, S.M.,
Dhar, D.,
Monte-Carlo Siamese Policy on Actor for Satellite Image Super
Resolution,
EarthVision20(757-767)
IEEE DOI
2008
Image resolution, Remote sensing,
Learning (artificial intelligence), Mathematical model,
Feature extraction
BibRef
Soh, J.W.,
Cho, S.,
Cho, N.I.,
Meta-Transfer Learning for Zero-Shot Super-Resolution,
CVPR20(3513-3522)
IEEE DOI
2008
Task analysis, Kernel, Image resolution, Training, Adaptation models,
Degradation, Optimization
BibRef
Ji, X.Z.[Xiao-Zhong],
Cao, Y.[Yun],
Tai, Y.[Ying],
Wang, C.J.[Cheng-Jie],
Li, J.L.[Ji-Lin],
Huang, F.Y.[Fei-Yue],
Real-World Super-Resolution via Kernel Estimation and Noise Injection,
NTIRE20(1914-1923)
IEEE DOI
2008
Kernel, Degradation, Data models, Training, Estimation, Spatial resolution
BibRef
Cai, J.,
Meng, Z.,
Ho, C.M.,
Residual Channel Attention Generative Adversarial Network for Image
Super-Resolution and Noise Reduction,
NTIRE20(1852-1861)
IEEE DOI
2008
Feature extraction, Spatial resolution,
Signal resolution, Convolution, Generators
BibRef
Kim, G.[Gwantae],
Park, J.[Jaihyun],
Lee, K.[Kanghyu],
Lee, J.Y.[Jun-Yeop],
Min, J.[Jeongki],
Lee, B.[Bokyeung],
Han, D.K.[David K.],
Ko, H.S.[Han-Seok],
Unsupervised Real-World Super Resolution with Cycle Generative
Adversarial Network and Domain Discriminator,
NTIRE20(1862-1871)
IEEE DOI
2008
Image resolution, Image color analysis, Generators, Training,
Task analysis, Colored noise
BibRef
Ren, H.Y.[Hao-Yu],
Kheradmand, A.[Amin],
El-Khamy, M.[Mostafa],
Wang, S.Q.[Shuang-Quan],
Bai, D.W.[Dong-Woon],
Lee, J.W.[Jung-Won],
Real-World Super-Resolution using Generative Adversarial Networks,
NTIRE20(1760-1768)
IEEE DOI
2008
Image resolution, Degradation, Training, Kernel, Generative adversarial networks
BibRef
Wang, L.,
Kim, T.,
Yoon, K.,
EventSR: From Asynchronous Events to Image Reconstruction,
Restoration, and Super-Resolution via End-to-End Adversarial Learning,
CVPR20(8312-8322)
IEEE DOI
2008
Image reconstruction, Cameras, Streaming media, Spatial resolution,
Image restoration, Training
BibRef
Kim, Y.[Yongwoo],
Choi, J.S.[Jae-Seok],
Lee, J.[Jaehyup],
Kim, M.C.[Mun-Churl],
A CNN-based Multi-scale Super-resolution Architecture on FPGA for 4k/8k
Uhd Applications,
MMMod20(II:739-744).
Springer DOI
2003
BibRef
Noh, J.,
Bae, W.,
Lee, W.,
Seo, J.,
Kim, G.,
Better to Follow, Follow to Be Better: Towards Precise Supervision of
Feature Super-Resolution for Small Object Detection,
ICCV19(9724-9733)
IEEE DOI
2004
convolutional neural nets, image resolution, object detection,
proposal-based detectors, feature pooling, Detectors
BibRef
Liu, Z.S.[Zhi-Song],
Wang, L.W.[Li-Wen],
Li, C.T.[Chu-Tak],
Siu, W.C.[Wan-Chi],
Chan, Y.L.[Yui-Lam],
Image Super-Resolution via Attention Based Back Projection Networks,
AIM19(3517-3525)
IEEE DOI
2004
Big Data, feature extraction, image reconstruction,
image resolution, image sampling, iterative methods, back projection
BibRef
Tariq, T.,
Gonzalez Bello, J.L.,
Kim, M.,
A HVS-Inspired Attention to Improve Loss Metrics for CNN-Based
Perception-Oriented Super-Resolution,
CLI19(3904-3912)
IEEE DOI
2004
convolutional neural nets, feature extraction, image resolution,
image restoration, visual perception, Visual Perception
BibRef
Zhou, R.,
Süsstrunk, S.,
Kernel Modeling Super-Resolution on Real Low-Resolution Images,
ICCV19(2433-2443)
IEEE DOI
2004
cameras, convolutional neural nets, image resolution,
image restoration, image sampling, interpolation,
Generative adversarial networks
BibRef
Xiong, D.,
Huang, K.,
Chen, S.,
Li, B.,
Jiang, H.,
Xu, W.,
NoUCSR: Efficient Super-Resolution Network without Upsampling
Convolution,
AIM19(3378-3387)
IEEE DOI
2004
convolutional neural nets, image resolution,
learning (artificial intelligence), NoUCSR, inference runtime,
efficient neural networks
BibRef
Huang, Y.F.[Yuan-Fei],
Sun, X.P.[Xiao-Peng],
Lu, W.[Wen],
Li, J.[Jie],
Gao, X.B.[Xin-Bo],
Un-Paired Real World Super-Resolution with Degradation Consistency,
AIM19(3458-3466)
IEEE DOI
2004
convolutional neural nets, image coding, image representation,
image resolution, learning (artificial intelligence), degradation consistency
BibRef
Fuoli, D.,
Gu, S.,
Timofte, R.,
Efficient Video Super-Resolution through Recurrent Latent Space
Propagation,
AIM19(3476-3485)
IEEE DOI
2004
image resolution, motion compensation, motion estimation,
recurrent neural nets, video signal processing
BibRef
Chen, B.X.[Bo-Xun],
Liu, T.J.[Tsung-Jung],
Liu, K.H.[Kuan-Hsien],
Liu, H.H.[Hsin-Hua],
Pei, S.C.[Soo-Chang],
Image Super-Resolution Using Complex Dense Block on Generative
Adversarial Networks,
ICIP19(2866-2870)
IEEE DOI
1910
Super-resolution (SR), dense block,
generative adversarial network (GAN), visual quality.
BibRef
Niu, Z.H.[Zhong-Han],
Zhou, Y.H.[Yang-Hao],
Yang, Y.B.[Yu-Bin],
Fan, J.C.[Jian-Cong],
A Novel Attention Enhanced Dense Network for Image Super-resolution,
MMMod20(I:568-580).
Springer DOI
2003
BibRef
Navarrete Michelini, P.[Pablo],
Chen, W.B.[Wen-Bin],
Liu, H.W.[Han-Wen],
Zhu, D.[Dan],
MGBPv2: Scaling Up Multi-Grid Back-Projection Networks,
AIM19(3399-3407)
IEEE DOI
2004
image reconstruction, image resolution, iterative methods, MGBPv2,
multigrid back-projection networks, perceptual quality, adversarial
BibRef
Kim, S.Y.[Soo Ye],
Kim, M.C.[Mun-Churl],
A Multi-purpose Convolutional Neural Network for Simultaneous
Super-Resolution and High Dynamic Range Image Reconstruction,
ACCV18(III:379-394).
Springer DOI
1906
BibRef
Zhao, Z.Q.[Zhong-Qiu],
Hu, J.[Jian],
Tian, W.D.[Wei-Dong],
Ling, N.[Ning],
Cooperative Adversarial Network for Accurate Super Resolution,
ACCV18(II:98-114).
Springer DOI
1906
BibRef
Cheon, M.[Manri],
Kim, J.H.[Jun-Hyuk],
Choi, J.H.[Jun-Ho],
Lee, J.S.[Jong-Seok],
Generative Adversarial Network-Based Image Super-Resolution Using
Perceptual Content Losses,
PerceptualRest18(V:51-62).
Springer DOI
1905
BibRef
Tan, W.,
Yan, B.,
Bare, B.,
Feature Super-Resolution: Make Machine See More Clearly,
CVPR18(3994-4002)
IEEE DOI
1812
Image resolution, Feature extraction,
Generative adversarial networks, Data models, Image recognition,
Euclidean distance
BibRef
Gao, P.,
Xue, J.,
Lu, K.,
Yan, Y.,
A fast Cascade Shape Regression Method based on CNN-based
Initialization,
ICPR18(3037-3042)
IEEE DOI
1812
Shape, Face, Training, Interpolation, Convolution,
Neural networks, Splines (mathematics)
BibRef
Jiang, T.,
Wu, X.,
Yu, Z.,
Shui, W.,
Lu, G.,
Guo, S.,
Fei, H.,
Zhang, Q.,
Recursive Inception Network for Super-Resolution,
ICPR18(2759-2764)
IEEE DOI
1812
Feature extraction, Training, Image reconstruction,
Image resolution, Periodic structures, Interpolation, Convolution
BibRef
Kasem, H.M.,
Hung, K.,
Jiang, J.,
Revised Spatial Transformer Network towards Improved Image
Super-resolutions,
ICPR18(2688-2692)
IEEE DOI
1812
Spatial resolution, Signal resolution, Image reconstruction,
Training, Transforms, Interpolation
BibRef
Bulat, A.[Adrian],
Yang, J.[Jing],
Tzimiropoulos, G.[Georgios],
To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image
Degradation First,
ECCV18(VI: 187-202).
Springer DOI
1810
BibRef
Liu, Z.S.[Zhi-Song],
Wan-Chi, S.[Siu],
Cascaded Random Forests for Fast Image Super-Resolution,
ICIP18(2531-2535)
IEEE DOI
1809
Image resolution, Decision trees, Mathematical model,
Feature extraction, Image reconstruction, Signal resolution,
machine learning
BibRef
Sugawara, Y.,
Shiota, S.,
Kiya, H.,
Super-Resolution Using Convolutional Neural Networks Without Any
Checkerboard Artifacts,
ICIP18(66-70)
IEEE DOI
1809
Convolution, Deconvolution, Training, Linear systems, Steady-state,
Image resolution, Kernel, Super-Resolution, Checkerboard Artifacts
BibRef
Zhang, K.H.[Kai-Hao],
Li, D.X.[Dong-Xu],
Luo, W.H.[Wen-Han],
Ren, W.Q.[Wen-Qi],
Stenger, B.[Björn],
Liu, W.[Wei],
Li, H.D.[Hong-Dong],
Yang, M.H.[Ming-Hsuan],
Benchmarking Ultra-High-Definition Image Super-resolution,
ICCV21(14749-14758)
IEEE DOI
2203
Training, Performance evaluation, Correlation,
Computational modeling, Superresolution,
BibRef
Xu, J.,
Chae, Y.,
Stenger, B.,
Datta, A.,
Dense Bynet: Residual Dense Network for Image Super Resolution,
ICIP18(71-75)
IEEE DOI
1809
Convolutional codes, Training, Spatial resolution, Task analysis,
Benchmark testing, Network architecture, image super resolution,
image enhancement
BibRef
Wang, Q.A.[Qi-Ang],
Fan, H.J.[Hui-Jie],
Cong, Y.[Yang],
Tang, Y.D.[Yan-Dong],
Large receptive field convolutional neural network for image
super-resolution,
ICIP17(958-962)
IEEE DOI
1803
Convolution, Convolutional neural networks, Feature extraction,
Kernel, Spatial resolution, Training, Convolutional neural network,
Super resolution
BibRef
Xu, J.,
Chae, Y.,
Stenger, B.,
BYNET-SR: Image super resolution with a bypass connection network,
ICIP17(4053-4057)
IEEE DOI
1803
Convergence, Convolution, Image resolution, Mathematical model,
Signal resolution, Task analysis, Training, super resolution
BibRef
Bao, W.B.[Wen-Bo],
Zhang, X.Y.[Xiao-Yun],
Yan, S.P.[Shang-Peng],
Gao, Z.Y.[Zhi-Yong],
Iterative convolutional neural network for noisy image
super-resolution,
ICIP17(4038-4042)
IEEE DOI
1803
Convolutional neural networks, Image reconstruction,
Noise measurement, Noise reduction, Spatial resolution, Training,
super-resolution
BibRef
Mu, Y.Y.[Yan-Yan],
Dimitrakopoulos, R.[Roussos],
Ferrie, F.P.[Frank P.],
Generalizing Generative Models: Application to Image Super-Resolution,
CRV16(8-15)
IEEE DOI
1612
BibRef
Earlier: A1, A3, A2:
Sparse image reconstruction by two phase RBM learning:
Application to mine planning,
MVA15(316-320)
IEEE DOI
1507
Computer vision. Location of ore bodies.
Boltzmann machine.
BibRef
Abolhassani, A.A.H.,
Dimitrakopoulos, R.[Roussos],
Ferrie, F.P.[Frank P.],
Anisotropic Interpolation of Sparse Images,
CRV16(440-447)
IEEE DOI
1612
anisotropic interpolation
BibRef
Ma, L.[Lin],
Zhang, Y.H.[Yong-Hua],
Lu, Y.[Yan],
Wu, F.[Feng],
Zhao, D.B.[De-Bin],
Three-tiered network model for image hallucination,
ICIP08(357-360).
IEEE DOI
0810
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Deep Neural Networks, Deep Learning for Super Resolution .