Dong, C.[Chao],
Loy, C.C.[Chen Change],
He, K.[Kaiming],
Tang, X.O.[Xiao-Ou],
Image Super-Resolution Using Deep Convolutional Networks,
PAMI(38), No. 2, February 2016, pp. 295-307.
IEEE DOI
1601
BibRef
Earlier:
Learning a Deep Convolutional Network for Image Super-Resolution,
ECCV14(IV: 184-199).
Springer DOI
1408
Convolutional codes
See also Compression Artifacts Reduction by a Deep Convolutional Network.
BibRef
Dong, C.[Chao],
Loy, C.C.[Chen Change],
Tang, X.O.[Xiao-Ou],
Accelerating the Super-Resolution Convolutional Neural Network,
ECCV16(II: 391-407).
Springer DOI
1611
BibRef
Hui, T.W.[Tak-Wai],
Loy, C.C.[Chen Change],
Tang, X.O.[Xiao-Ou],
Depth Map Super-Resolution by Deep Multi-Scale Guidance,
ECCV16(III: 353-369).
Springer DOI
1611
BibRef
Wang, L.F.[Ling-Feng],
Huang, Z.[Zehao],
Gong, Y.C.[Yong-Chao],
Pan, C.H.[Chun-Hong],
Ensemble based deep networks for image super-resolution,
PR(68), No. 1, 2017, pp. 191-198.
Elsevier DOI
1704
Super-resolution
BibRef
Huang, Z.[Zehao],
Wang, L.F.[Ling-Feng],
Meng, G.,
Pan, C.H.[Chun-Hong],
Image Super-Resolution Via Deep Dilated Convolutional Networks,
ICIP17(953-957)
IEEE DOI
1803
Acceleration, Convolution, Image reconstruction, Image resolution,
Machine learning, Task analysis, Training, Acceleration,
Super-Resolution
BibRef
Cheong, J.Y.,
Park, I.K.,
Deep CNN-Based Super-Resolution Using External and Internal Examples,
SPLetters(24), No. 8, August 2017, pp. 1252-1256.
IEEE DOI
1708
convolution, image resolution, neural nets,
deep CNN-based SISR method, deep CNN-based superresolution,
deep convolutional neural network,
global residual network, internal example-based SISR methods,
Deep convolutional neural network (CNN), external example,
internal example, single, image, super-resolution, (SISR)
BibRef
Ren, C.,
He, X.,
Pu, Y.,
Nonlocal Similarity Modeling and Deep CNN Gradient Prior for Super
Resolution,
SPLetters(25), No. 7, July 2018, pp. 916-920.
IEEE DOI
1807
convolution, feedforward neural nets, gradient methods,
image resolution, AHNLTV, GA-GP approach, GSR,
super resolution
BibRef
Lin, G.M.[Gui-Min],
Wu, Q.X.[Qing-Xiang],
Chen, L.[Liang],
Qiu, L.[Lida],
Wang, X.[Xuan],
Liu, T.J.[Tian-Jian],
Chen, X.[Xiyao],
Deep Unsupervised Learning for Image Super-Resolution with Generative
Adversarial Network,
SP:IC(68), 2018, pp. 88-100.
Elsevier DOI
1810
Super-resolution, Deep unsupervised learning,
Sub-pixel convolution, Regularizer, Generative adversarial network
BibRef
Zheng, Y.[Yan],
Cao, X.[Xiang],
Xiao, Y.[Yi],
Zhu, X.[Xianyi],
Yuan, J.[Jin],
Joint residual pyramid for joint image super-resolution,
JVCIR(58), 2019, pp. 53-62.
Elsevier DOI
1901
Deep learning, Neural convolutional pyramid,
Joint super-resolution, Residual block
BibRef
Yang, W.,
Xia, S.,
Liu, J.,
Guo, Z.,
Reference-Guided Deep Super-Resolution via Manifold Localized
External Compensation,
CirSysVideo(29), No. 5, May 2019, pp. 1270-1283.
IEEE DOI
1905
Manifolds, Image resolution, Image reconstruction, Estimation,
Databases, Semantics, Face, Super-resolution, manifold localization,
internal structure inference
BibRef
Guo, D.[Dan],
Niu, Y.X.[Yan-Xiong],
Xie, P.Y.[Peng-Yan],
Speedy and accurate image super-resolution via deeply recursive CNN
with skip connection and network in network,
IET-IPR(13), No. 7, 30 May 2019, pp. 1201-1209.
DOI Link
1906
BibRef
He, Z.W.[Ze-Wei],
Tang, S.L.[Si-Liang],
Yang, J.X.[Jiang-Xin],
Cao, Y.L.[Yan-Long],
Yang, M.Y.[Michael Ying],
Cao, Y.P.[Yan-Peng],
Cascaded Deep Networks With Multiple Receptive Fields for Infrared
Image Super-Resolution,
CirSysVideo(29), No. 8, August 2019, pp. 2310-2322.
IEEE DOI
1908
Image reconstruction, Spatial resolution, Image restoration,
Dictionaries, Machine learning, Training, Infrared imaging,
receptive fields
BibRef
Guo, T.,
Seyed Mousavi, H.,
Monga, V.,
Adaptive Transform Domain Image Super-Resolution via Orthogonally
Regularized Deep Networks,
IP(28), No. 9, Sep. 2019, pp. 4685-4700.
IEEE DOI
1908
convolutional neural nets, discrete cosine transforms,
image resolution, interpolation,
complexity constraint
BibRef
Lai, W.S.[Wei-Sheng],
Huang, J.B.[Jia-Bin],
Ahuja, N.[Narendra],
Yang, M.H.[Ming-Hsuan],
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid
Networks,
PAMI(41), No. 11, November 2019, pp. 2599-2613.
IEEE DOI
1910
BibRef
Earlier:
Deep Laplacian Pyramid Networks for Fast and Accurate
Super-Resolution,
CVPR17(5835-5843)
IEEE DOI
1711
Image reconstruction, Feature extraction, Convolution,
Spatial resolution, Laplace equations, Interpolation,
Laplacian pyramid.
Convolution, Image reconstruction, Training
BibRef
Haut, J.M.,
Fernandez-Beltran, R.,
Paoletti, M.E.,
Plaza, J.,
Plaza, A.,
Remote Sensing Image Superresolution Using Deep Residual Channel
Attention,
GeoRS(57), No. 11, November 2019, pp. 9277-9289.
IEEE DOI
1911
Remote sensing, Spatial resolution, Feature extraction,
Visualization, Earth, Training, Deep learning, remote sensing,
visual attention (VA)
BibRef
Deng, X.[Xin],
Dragotti, P.L.[Pier Luigi],
Deep Coupled ISTA Network for Multi-Modal Image Super-Resolution,
IP(29), No. 1, 2020, pp. 1683-1698.
IEEE DOI
1912
Machine learning, Dictionaries, Neural networks,
Thresholding (Imaging), Iterative algorithms,
deep neural network
BibRef
Deng, X.[Xin],
Dragotti, P.L.[Pier Luigi],
Deep Convolutional Neural Network for Multi-Modal Image Restoration
and Fusion,
PAMI(43), No. 10, October 2021, pp. 3333-3348.
IEEE DOI
2109
Image fusion, Task analysis, Image restoration,
Convolutional codes, Image reconstruction,
multi-modal convolutional sparse coding
BibRef
Xu, J.Y.[Jing-Yi],
Deng, X.[Xin],
Xu, M.[Mai],
Dragotti, P.L.[Pier Luigi],
CU-Net+: Deep Fully Interpretable Network for Multi-Modal Image
Restoration,
ICIP21(1674-1678)
IEEE DOI
2201
Convolutional codes, Image coding, Computational modeling,
Superresolution, Network architecture, Feature extraction,
multi-modal image restoration
BibRef
Deng, X.[Xin],
Zhang, Y.T.[Yu-Tong],
Xu, M.[Mai],
Gu, S.H.[Shu-Hang],
Duan, Y.P.[Yi-Ping],
Deep Coupled Feedback Network for Joint Exposure Fusion and Image
Super-Resolution,
IP(30), 2021, pp. 3098-3112.
IEEE DOI
2103
Superresolution, Image fusion, Task analysis, Feature extraction,
Dynamic range, Convolutional codes, Cameras, Exposure fusion,
deep learning
BibRef
Shamsolmoali, P.[Pourya],
Sadka, A.H.[Abdul Hamid],
Zhou, H.Y.[Hui-Yu],
Yang, W.K.[Wan-Kou],
Advanced deep learning for image super-resolution,
SP:IC(82), 2020, pp. 115732.
Elsevier DOI
2001
BibRef
Bordone Molini, A.,
Valsesia, D.,
Fracastoro, G.,
Magli, E.,
DeepSUM: Deep Neural Network for Super-Resolution of Unregistered
Multitemporal Images,
GeoRS(58), No. 5, May 2020, pp. 3644-3656.
IEEE DOI
2005
Convolutional neural networks (CNNs), dynamic filter networks,
multi-image super resolution (MISR), multitemporal images
BibRef
Zhang, S.[Shu],
Yuan, Q.Q.[Qiang-Qiang],
Li, J.[Jie],
Sun, J.[Jing],
Zhang, X.G.[Xu-Guo],
Scene-Adaptive Remote Sensing Image Super-Resolution Using a
Multiscale Attention Network,
GeoRS(58), No. 7, July 2020, pp. 4764-4779.
IEEE DOI
2006
Remote sensing, Feature extraction, Image reconstruction,
Convolution, Deep learning, Interpolation, Channel attention,
scene adaptive
BibRef
Yu, Y.L.[Yun-Long],
Li, X.Z.[Xian-Zhi],
Liu, F.X.[Fu-Xian],
E-DBPN: Enhanced Deep Back-Projection Networks for Remote Sensing
Scene Image Superresolution,
GeoRS(58), No. 8, August 2020, pp. 5503-5515.
IEEE DOI
2007
Remote sensing, Generators, Task analysis,
Generative adversarial networks, Training, Deep learning,
single image superresolution (SISR)
BibRef
Salvetti, F.[Francesco],
Mazzia, V.[Vittorio],
Khaliq, A.[Aleem],
Chiaberge, M.[Marcello],
Multi-Image Super Resolution of Remotely Sensed Images Using Residual
Attention Deep Neural Networks,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link
2007
BibRef
Anwar, S.[Saeed],
Khan, S.[Salman],
Barnes, N.M.[Nick M.],
A Deep Journey into Super-Resolution: A Survey,
Surveys(53), No. 3, May 2020, pp. xx-yy.
DOI Link
2007
Survey, Super-Resolution. deep learning, survey, Super-resolution (SR),
generative adversarial networks (GANs), high-resolution (HR),
convolutional neural networks (CNNs)
BibRef
Anwar, S.[Saeed],
Barnes, N.M.[Nick M.],
Densely Residual Laplacian Super-Resolution,
PAMI(44), No. 3, March 2022, pp. 1192-1204.
IEEE DOI
2202
Laplace equations, Feature extraction, Computer architecture,
Convolutional neural networks, Image restoration,
deep convolutional neural network
BibRef
Zhang, Y.B.[Yong-Bing],
Liu, S.Y.[Si-Yuan],
Dong, C.[Chao],
Zhang, X.F.[Xin-Feng],
Yuan, Y.[Yuan],
Multiple Cycle-in-Cycle Generative Adversarial Networks for
Unsupervised Image Super-Resolution,
IP(29), 2020, pp. 1101-1112.
IEEE DOI
1911
Training, Kernel, Degradation, Interpolation, Deep learning,
Super resolution, unsupervised learning, generative adversarial networks
BibRef
Yuan, Y.[Yuan],
Liu, S.Y.[Si-Yuan],
Zhang, J.W.[Jia-Wei],
Zhang, Y.B.[Yong-Bing],
Dong, C.[Chao],
Lin, L.[Liang],
Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative
Adversarial Networks,
Restoration18(814-81409)
IEEE DOI
1812
Image resolution, Kernel, Training, Generators,
Degradation, Unsupervised learning
BibRef
Jiang, J.,
Yu, Y.,
Wang, Z.,
Tang, S.,
Hu, R.,
Ma, J.,
Ensemble Super-Resolution With a Reference Dataset,
Cyber(50), No. 11, November 2020, pp. 4694-4708.
IEEE DOI
2011
Image reconstruction, Image resolution, Learning systems,
Convolutional codes, Deep learning, Estimation, Task analysis,
super-resolution (SR)
BibRef
Yang, X.[Xin],
Li, X.C.[Xiao-Chuan],
Li, Z.Q.[Zhi-Qiang],
Zhou, D.[Dake],
Image super-resolution based on deep neural network of multiple
attention mechanism,
JVCIR(75), 2021, pp. 103019.
Elsevier DOI
2103
Super-resolution, CNN, Attention mechanism, Channel attention, Spatial attention
BibRef
Liu, Z.S.[Zhi-Song],
Siu, W.C.[Wan-Chi],
Chan, Y.L.,
Features Guided Face Super-Resolution via Hybrid Model of Deep
Learning and Random Forests,
IP(30), 2021, pp. 4157-4170.
IEEE DOI
2104
Random forests, Superresolution, Face recognition, Faces,
Image reconstruction, Image segmentation, Facial features,
facial features
BibRef
Wang, Q.Q.[Qian-Qian],
Gao, Q.X.[Quan-Xue],
Wu, L.L.[Lin-Lu],
Sun, G.[Gan],
Jiao, L.C.[Li-Cheng],
Adversarial Multi-Path Residual Network for Image Super-Resolution,
IP(30), 2021, pp. 6648-6658.
IEEE DOI
2108
Feature extraction, Residual neural networks, Superresolution,
Generative adversarial networks, Image reconstruction,
deep convolutional neural network
BibRef
Chen, P.L.[Pei-Lin],
Yang, W.H.[Wen-Han],
Wang, M.[Meng],
Sun, L.[Long],
Hu, K.K.[Kang-Kang],
Wang, S.Q.[Shi-Qi],
Compressed Domain Deep Video Super-Resolution,
IP(30), 2021, pp. 7156-7169.
IEEE DOI
2108
Image coding, Encoding, Decoding, Convolutional neural networks,
Superresolution, Computational modeling, Video coding,
soft alignment
BibRef
Esmaeilzehi, A.[Alireza],
Ahmad, M.O.[M. Omair],
Swamy, M.N.S.,
SRNHARB: A deep light-weight image super resolution network using
hybrid activation residual blocks,
SP:IC(99), 2021, pp. 116509.
Elsevier DOI
2111
Image super resolution, Deep learning, Residual learning
BibRef
Esmaeilzehi, A.[Alireza],
Zaredar, H.[Hossein],
Hatzinakos, D.[Dimitrios],
Ahmad, M.O.[M. Omair],
DPAN: A Deep Light-Weight Attention-Based Image Super Resolution
Network Using Multi-Dimensional Filter Design Technique,
SPLetters(30), 2023, pp. 1637-1641.
IEEE DOI
2311
BibRef
Wu, H.L.[Han-Lin],
Zhang, L.B.[Li-Bao],
Ma, J.[Jie],
Remote Sensing Image Super-Resolution via Saliency-Guided Feedback
GANs,
GeoRS(60), 2022, pp. 1-16.
IEEE DOI
2112
Visualization, Image reconstruction,
Generative adversarial networks, Distortion,
super-resolution (SR)
BibRef
Ma, J.[Jie],
Wu, H.L.[Han-Lin],
Zhang, J.,
Zhang, L.[Libao],
SD-FB-GAN: Saliency-Driven Feedback GAN for Remote Sensing Image
Super-Resolution Reconstruction,
ICIP20(528-532)
IEEE DOI
2011
Indexes, Economic indicators, Zirconium, Image reconstruction,
super-resolution, deep learning, GAN, saliency analysis
BibRef
Wang, S.Y.[Shi-Yan],
Zhang, J.S.[Jiang-Shan],
Yu, X.[Xiang],
Shi, F.[Fan],
SVDN: A spatially variant degradation network for blind image
super-resolution,
PRL(153), 2022, pp. 214-221.
Elsevier DOI
2201
Super-resolution, Spatially variant degradation,
Kernel estimation, Deep learning
BibRef
Ahn, N.[Namhyuk],
Kang, B.[Byungkon],
Sohn, K.A.[Kyung-Ah],
Efficient deep neural network for photo-realistic image
super-resolution,
PR(127), 2022, pp. 108649.
Elsevier DOI
2205
Super-resolution, Photo-realistic,
Convolutional neural network, Efficient model, Multi-scale approach
BibRef
Guo, Y.H.[Yan-Hui],
Wu, X.L.[Xiao-Lin],
Shu, X.[Xiao],
Data Acquisition and Preparation for Dual-Reference Deep Learning of
Image Super-Resolution,
IP(31), 2022, pp. 4393-4404.
IEEE DOI
2207
Cameras, Training, Superresolution, Lenses, Training data,
Deep learning, Task analysis, Image super-resolution,
dual-reference deep learning
BibRef
Li, Z.[Zhen],
Kuang, Z.S.[Zeng-Sheng],
Zhu, Z.L.[Zuo-Liang],
Wang, H.P.[Hong-Peng],
Shao, X.L.[Xiu-Li],
Wavelet-Based Texture Reformation Network for Image Super-Resolution,
IP(31), 2022, pp. 2647-2660.
IEEE DOI
2204
Wavelet transforms, Feature extraction, Superresolution,
Correlation, Image reconstruction, Deep learning, Visualization,
adversarial loss
BibRef
Li, Z.[Zhen],
Zhang, W.J.[Wen-Juan],
Pan, J.[Jie],
Sun, R.Q.[Rui-Qi],
Sha, L.Y.[Ling-Yu],
A Super-Resolution Algorithm Based on Hybrid Network for
Multi-Channel Remote Sensing Images,
RS(15), No. 14, 2023, pp. 3693.
DOI Link
2307
BibRef
Luo, X.T.[Xiao-Tong],
Qu, Y.Y.[Yan-Yun],
Xie, Y.[Yuan],
Zhang, Y.L.[Yu-Lun],
Li, C.H.[Cui-Hua],
Fu, Y.[Yun],
Lattice Network for Lightweight Image Restoration,
PAMI(45), No. 4, April 2023, pp. 4826-4842.
IEEE DOI
2303
Task analysis, Adaptation models, Lattices, Computational modeling,
Image restoration, Image denoising, Superresolution,
Contrastive Learning
BibRef
Zhang, Y.L.[Yu-Lun],
Tian, Y.,
Kong, Y.,
Zhong, B.N.[Bi-Neng],
Fu, Y.[Yun],
Residual Dense Network for Image Super-Resolution,
CVPR18(2472-2481)
IEEE DOI
1812
Feature extraction, Convolution, Image resolution,
Training, Buildings, Fuses
BibRef
Zhang, Y.L.[Yu-Lun],
Li, K.P.[Kun-Peng],
Li, K.[Kai],
Wang, L.C.[Li-Chen],
Zhong, B.N.[Bi-Neng],
Fu, Y.[Yun],
Image Super-Resolution Using Very Deep Residual Channel Attention
Networks,
ECCV18(VII: 294-310).
Springer DOI
1810
BibRef
Rajaei, B.[Boshra],
Rajaei, S.[Sara],
Damavandi, H.[Hossein],
An Analysis of Multi-stage Progressive Image Restoration Network
(MPRNet),
IPOL(13), 2023, pp. 140-152.
DOI Link
2305
three-stage CNN (convolutional neural network) for image restoration.
See also MPRNet: Multi-Path Residual Network for Lightweight Image Super Resolution.
BibRef
Ates, H.F.[Hasan F.],
Yildirim, S.[Suleyman],
Gunturk, B.K.[Bahadir K.],
Deep learning-based blind image super-resolution with iterative
kernel reconstruction and noise estimation,
CVIU(233), 2023, pp. 103718.
Elsevier DOI
2307
BibRef
Earlier: A2, A1, A3:
Iterative Kernel Reconstruction for Deep Learning-Based Blind Image
Super-Resolution,
ICIP22(3251-3255)
IEEE DOI
2211
Super-resolution, Blind, Iterative, Deep network.
Deep learning, Superresolution, Estimation, Iterative methods,
Kernel, Task analysis, Image reconstruction, Super-resolution
BibRef
Song, C.X.[Chong-Xing],
Lang, Z.Q.[Zhi-Qiang],
Wei, W.[Wei],
Zhang, L.[Lei],
E2FIF: Push the Limit of Binarized Deep Imagery Super-Resolution
Using End-to-End Full-Precision Information Flow,
IP(32), 2023, pp. 5379-5393.
IEEE DOI
2310
BibRef
Zhang, Y.[Yan],
Zhang, L.[Lifu],
Song, R.X.[Ruo-Xi],
Tong, Q.X.[Qing-Xi],
A General Deep Learning Point-Surface Fusion Framework for RGB Image
Super-Resolution,
RS(16), No. 1, 2024, pp. xx-yy.
DOI Link
2401
BibRef
Su, H.[Hu],
Li, Y.[Ying],
Xu, Y.F.[Yi-Fan],
Fu, X.[Xiang],
Liu, S.[Song],
A review of deep-learning-based super-resolution:
From methods to applications,
PR(157), 2025, pp. 110935.
Elsevier DOI
2409
Deep learning, Super-resolution, Single image super-resolution,
Multiple image super-resolution, Degradation model
BibRef
Wang, H.[Hang],
Ding, Z.Y.[Zhen-Yu],
Cheng, C.[Cheng],
Li, Y.[Yuhai],
Sun, H.B.[Hong-Bin],
MDC-Net: Multi-domain constrained kernel estimation network for blind
image super resolution,
CVIU(238), 2024, pp. 103865.
Elsevier DOI
2312
Blind super-resolution, Deep learning,
Degradation kernel estimation, Multi-domain constraints
BibRef
Xiong, Q.M.[Qi-Ming],
Gao, Z.R.[Zhi-Rong],
Ma, J.Y.[Jia-Yi],
Ma, Y.[Yong],
Multi-image super-resolution based low complexity deep network for
image compressive sensing reconstruction,
JVCIR(99), 2024, pp. 104071.
Elsevier DOI
2403
Compressive sensing reconstruction, Deep learning,
Grouping initial reconstruction, Image super-resolution, Low complexity
BibRef
Chaouai, Z.[Zakariya],
Tamaazousti, M.[Mohamed],
Universal Robustness via Median Randomized Smoothing for Real-World
Super-Resolution,
CVPR24(9059-9068)
IEEE DOI
2410
Measurement, Deep learning, Adaptation models, Smoothing methods,
Gaussian noise, Superresolution
BibRef
Luo, Z.W.[Zi-Wei],
Huang, H.B.[Hai-Bin],
Yu, L.[Lei],
Li, Y.[Youwei],
Fan, H.Q.[Hao-Qiang],
Liu, S.C.[Shuai-Cheng],
Deep Constrained Least Squares for Blind Image Super-Resolution,
CVPR22(17621-17631)
IEEE DOI
2210
Degradation, Photography, Visualization, Image resolution,
Superresolution, Estimation, Nonlinear filters, Low-level vision,
Image and video synthesis and generation
BibRef
Zhang, H.Y.[Hai-Yu],
Zhu, Y.[Yu],
Sun, J.Q.[Jin-Qiu],
Zhang, Y.N.[Yan-Ning],
Real-World Image Super-Resolution Via Kernel Augmentation And
Stochastic Variation,
ICIP22(2506-2510)
IEEE DOI
2211
Deep learning, Degradation, Visualization, Superresolution,
Stochastic processes, Generative adversarial networks,
stochastic variation (SV)
BibRef
Hu, X.T.[Xiao-Tao],
Xu, J.[Jun],
Gu, S.H.[Shu-Hang],
Cheng, M.M.[Ming-Ming],
Liu, L.[Li],
Restore Globally, Refine Locally:
A Mask-Guided Scheme to Accelerate Super-Resolution Networks,
ECCV22(XIX:74-91).
Springer DOI
2211
BibRef
Maeda, S.[Shunta],
Image Super-Resolution with Deep Dictionary,
ECCV22(XIX:464-480).
Springer DOI
2211
BibRef
Zhong, Y.S.[Yun-Shan],
Lin, M.B.[Ming-Bao],
Li, X.C.[Xun-Chao],
Li, K.[Ke],
Shen, Y.H.[Yun-Hang],
Chao, F.[Fei],
Wu, Y.J.[Yong-Jian],
Ji, R.R.[Rong-Rong],
Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution
Networks,
ECCV22(XVIII:1-18).
Springer DOI
2211
BibRef
Zou, W.B.[Wen-Bin],
Ye, T.[Tian],
Zheng, W.X.[Wei-Xin],
Zhang, Y.C.[Yun-Chen],
Chen, L.[Liang],
Wu, Y.[Yi],
Self-Calibrated Efficient Transformer for Lightweight
Super-Resolution,
NTIRE22(929-938)
IEEE DOI
2210
Deep learning, Visualization, Superresolution,
Computer architecture, Transformers
BibRef
Ayazoglu, M.[Mustafa],
IMDeception: Grouped Information Distilling Super-Resolution Network,
NTIRE22(755-764)
IEEE DOI
2210
Deep learning, Superresolution, Real-time systems, Hardware, Timing
BibRef
Kong, F.Y.[Fang-Yuan],
Li, M.X.[Ming-Xi],
Liu, S.G.[Son-Gwei],
Liu, D.[Ding],
He, J.W.[Jing-Wen],
Bai, Y.[Yang],
Chen, F.M.[Fang-Min],
Fu, L.[Lean],
Residual Local Feature Network for Efficient Super-Resolution,
NTIRE22(765-775)
IEEE DOI
2210
Training, Representation learning, Performance evaluation,
Deep learning, Convolutional codes, Runtime, Computational modeling
BibRef
Sinha, A.K.[Abhishek Kumar],
Moorthi, S.M.[S. Manthira],
Dhar, D.[Debajyoti],
NL-FFC: Non-Local Fast Fourier Convolution for Image Super Resolution,
NTIRE22(466-475)
IEEE DOI
2210
Deep learning, Convolution, Fuses, Superresolution, Neural networks,
Performance gain
BibRef
Guo, B.S.[Bai-Song],
Zhang, X.Y.[Xiao-Yun],
Wu, H.N.[Hao-Ning],
Wang, Y.[Yu],
Zhang, Y.[Ya],
Wang, Y.F.[Yan-Feng],
LAR-SR: A Local Autoregressive Model for Image Super-Resolution,
CVPR22(1899-1908)
IEEE DOI
2210
Measurement, Adaptation models, Computational modeling,
Superresolution, Image restoration,
Deep learning architectures and techniques
BibRef
de Lutio, R.[Riccardo],
Becker, A.[Alexander],
d'Aronco, S.[Stefano],
Russo, S.[Stefania],
Wegner, J.D.[Jan D.],
Schindler, K.[Konrad],
Learning Graph Regularisation for Guided Super-Resolution,
CVPR22(1969-1978)
IEEE DOI
2210
Deep learning, Training, Superresolution, Optimization methods,
Lattices, Computer architecture, Feature extraction,
RGBD sensors and analytics
BibRef
Xue, M.[Mowen],
Greenslade, T.[Theo],
Mirmehdi, M.[Majid],
Burghardt, T.[Tilo],
Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data
for Aerial Animal Surveillance,
RWSurvil22(509-519)
IEEE DOI
2202
Visualization, Systematics, Animals, Surveillance,
Superresolution, Pipelines
BibRef
Michelini, P.N.[Pablo Navarrete],
Lu, Y.[Yunhua],
Jiang, X.Q.[Xing-Qun],
edge-SR: Super-Resolution For The Masses,
WACV22(4019-4028)
IEEE DOI
2202
Deep learning, Image quality, Visualization, Runtime,
Image edge detection, Superresolution, Computer architecture,
Privacy and Ethics in Vision
BibRef
Durand, T.[Thibault],
Rabin, J.[Julien],
Tschumperlé, D.[David],
Shallow Multi-Scale Network for Stylized Super-Resolution,
ICIP21(2758-2762)
IEEE DOI
2201
Deep learning, Superresolution, Memory management,
Memory architecture, Graphics processing units, Software,
Interactive Computation
BibRef
Tarasiewicz, T.[Tomasz],
Nalepa, J.[Jakub],
Kawulok, M.[Michal],
A Graph Neural Network for Multiple-Image Super-Resolution,
ICIP21(1824-1828)
IEEE DOI
2201
Deep learning, Magnetic resonance imaging, Superresolution,
Feature extraction, Graph neural networks, Image reconstruction,
deep learning
BibRef
Bhattacharya, P.[Purbaditya],
Zölzer, U.[Udo],
Attentive Inception Module based Convolutional Neural Network for
Image Enhancement,
DICTA20(1-8)
IEEE DOI
2201
Image coding, Digital images, Superresolution, Redundancy,
Convolutional neural networks, Task analysis, Image enhancement,
deep learning
BibRef
Gu, J.J.[Jin-Jin],
Dong, C.[Chao],
Interpreting Super-Resolution Networks with Local Attribution Maps,
CVPR21(9195-9204)
IEEE DOI
2111
Deep learning, Visualization, Technological innovation,
Gradient methods, Superresolution, Semantics
BibRef
Yamac, M.[Mehmet],
Nawaz, A.[Aakif],
Ataman, B.[Baran],
Reference-Based Blind Super-Resolution Kernel Estimation,
ICIP22(4123-4127)
IEEE DOI
2211
BibRef
Earlier: A1, A3, A2:
KernelNet: A Blind Super-Resolution Kernel Estimation Network,
NTIRE21(453-462)
IEEE DOI
2109
Superresolution, Estimation, Cameras, Real-time systems, Kernel,
Image reconstruction, Super-resolution, SR kernel estimation,
Real-world SR.
Measurement, Deep learning, Neural networks, Mobile handsets
BibRef
Jo, Y.[Younghyun],
Yang, S.[Sejong],
Kim, S.J.[Seon Joo],
SRFlow-DA: Super-Resolution Using Normalizing Flow with Deep
Convolutional Block,
NTIRE21(364-372)
IEEE DOI
2109
Couplings, Training, Manifolds,
Superresolution, Stacking
BibRef
Emad, M.[Mohammad],
Peemen, M.[Maurice],
Corporaal, H.[Henk],
MoESR: Blind Super-Resolution using Kernel-Aware Mixture of Experts,
WACV22(4009-4018)
IEEE DOI
2202
BibRef
Earlier:
DualSR: Zero-Shot Dual Learning for Real-World Super-Resolution,
WACV21(1629-1638)
IEEE DOI
2106
Degradation, Training, Visualization, Surveillance, Microscopy,
Superresolution, Prediction methods,
Image Processing -> Image Restoration Super-resolution.
Deep learning, Interpolation, Training data
BibRef
Behjati, P.[Parichehr],
Rodríguez, P.[Pau],
Mehri, A.[Armin],
Hupont, I.[Isabelle],
Tena, C.F.[Carles Fernández],
Gonzŕlez, J.[Jordi],
OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling
Network,
WACV21(2693-2702)
IEEE DOI
2106
Training, Computational modeling, Superresolution,
Memory management, Noise reduction, Feature extraction, Data mining
BibRef
Mehri, A.[Armin],
Ardakani, P.B.[Parichehr B.],
Sappa, A.D.[Angel D.],
MPRNet: Multi-Path Residual Network for Lightweight Image Super
Resolution,
WACV21(2703-2712)
IEEE DOI
2106
BibRef
And:
LiNet: A Lightweight Network for Image Super Resolution,
ICPR21(7196-7202)
IEEE DOI
2105
Deep learning, Adaptive systems,
Computer architecture, Feature extraction, Computational efficiency.
Performance evaluation, Image resolution, Computational modeling,
Benchmark testing, Data mining
See also Analysis of Multi-stage Progressive Image Restoration Network (MPRNet), An.
BibRef
Li, H.W.[Hong-Wei],
Dai, T.[Tao],
Li, Y.M.[Yi-Ming],
Zou, X.[Xueyi],
Xia, S.T.[Shu-Tao],
Adaptive Local Implicit Image Function for Arbitrary-Scale
Super-Resolution,
ICIP22(4033-4037)
IEEE DOI
2211
Adaptation models, Visualization, Codes, Image edge detection,
Superresolution, Image representation, Distortion, deep learning
BibRef
Bhat, G.[Goutam],
Danelljan, M.[Martin],
Yu, F.[Fisher],
Van Gool, L.J.[Luc J.],
Timofte, R.[Radu],
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising,
ICCV21(2440-2450)
IEEE DOI
2203
Deep learning, Superresolution, Noise reduction, Transforms,
Predictive models, Extraterrestrial measurements,
BibRef
Zhang, K.,
Van Gool, L.J.,
Timofte, R.,
Deep Unfolding Network for Image Super-Resolution,
CVPR20(3214-3223)
IEEE DOI
2008
Degradation, Kernel, Image resolution, Learning systems, Noise level,
Computational modeling, Inference algorithms
BibRef
Arefin, M.R.[M. Rifat],
Michalski, V.,
St-Charles, P.,
Kalaitzis, A.,
Kim, S.,
Kahou, S.E.,
Bengio, Y.,
Multi-Image Super-Resolution for Remote Sensing using Deep Recurrent
Networks,
EarthVision20(816-825)
IEEE DOI
2008
Image resolution, Image reconstruction, Satellites,
Machine learning, Decoding, Remote sensing, Earth
BibRef
Umer, R.M.[R. Muhammad],
Foresti, G.L.[G. Luca],
Micheloni, C.,
Deep Generative Adversarial Residual Convolutional Networks for
Real-World Super-Resolution,
NTIRE20(1769-1777)
IEEE DOI
2008
Image resolution, Degradation, Cameras, Linear programming,
Optimization, Training, Machine learning
BibRef
Umer, R.M.[Rao M.],
Foresti, G.L.,
Micheloni, C.,
Deep Iterative Residual Convolutional Network for Single Image
Super-Resolution,
ICPR21(1852-1858)
IEEE DOI
2105
Training, Visualization, Solid modeling, Superresolution,
Memory management, Training data
BibRef
Kim, S.Y.,
Oh, J.,
Kim, M.,
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse
Tone-Mapping for 4K UHD HDR Applications,
ICCV19(3116-3125)
IEEE DOI
2004
high definition video, image colour analysis, image enhancement,
image resolution, image restoration,
Multimedia communication
BibRef
Choi, J.H.[Jun-Ho],
Zhang, H.[Huan],
Kim, J.H.[Jun-Hyuk],
Hsieh, C.J.[Cho-Jui],
Lee, J.S.[Jong-Seok],
Adversarially Robust Deep Image Super-resolution Using Entropy
Regularization,
ACCV20(IV:301-317).
Springer DOI
2103
BibRef
Earlier:
Evaluating Robustness of Deep Image Super-Resolution Against
Adversarial Attacks,
ICCV19(303-311)
IEEE DOI
2004
image resolution, learning (artificial intelligence),
neural nets, low-resolution image, adversarial attacks,
Interpolation
BibRef
Schirrmacher, F.,
Lorch, B.,
Stimpel, B.,
Köhler, T.,
Riess, C.,
SR2: Super-Resolution With Structure-Aware Reconstruction,
ICIP20(533-537)
IEEE DOI
2011
Image resolution, Task analysis, Training, Image reconstruction,
Noise measurement, Degradation, Training data, Deep learning,
Classification
BibRef
Lugmayr, A.,
Danelljan, M.,
Timofte, R.,
Unsupervised Learning for Real-World Super-Resolution,
AIM19(3408-3416)
IEEE DOI
2004
image resolution, image restoration, image sampling,
unsupervised learning, real-world images, deep learning
BibRef
Muqeet, A.[Abdul],
Bae, S.H.[Sung-Ho],
Effective Utilization of Hybrid Residual Modules in Deep Neural
Networks for Super Resolution,
MMMod20(II:745-750).
Springer DOI
2003
BibRef
Fritsche, M.,
Gu, S.,
Timofte, R.,
Frequency Separation for Real-World Super-Resolution,
AIM19(3599-3608)
IEEE DOI
2004
image resolution, image sampling, real-world super-resolution,
image super-resolution, low resolution image,
deep learning
BibRef
Kim, J.,
Lee, J.,
Deep Residual Network with Enhanced Upscaling Module for
Super-Resolution,
Restoration18(913-9138)
IEEE DOI
1812
Convolution, Image resolution, Feature extraction, Training,
Image reconstruction, Signal resolution
BibRef
Gao, H.,
Chen, Z.,
Ma, G.,
Xie, W.,
Li, Z.,
Deep Pixel Probabilistic Model for Super Resolution Based on Human
Visual Saliency Mechanism,
ICPR18(2747-2752)
IEEE DOI
1812
Training, Image resolution, Probabilistic logic, Visualization,
Interpolation, Computational modeling, Optimization,
image quality assessment
BibRef
Zhao, L.L.[Li-Ling],
Zhang, Z.L.[Ze-Lin],
Sun, Q.S.[Quan-Sen],
Deep Learning Based Super Resolution Using Significant and General
Regions,
ICIP18(2516-2520)
IEEE DOI
1809
Training, Image resolution, Image reconstruction,
Feature extraction, Roads, Machine learning, Big Data, deep learning,
significant regions
BibRef
Liu, Y.,
Chen, Q.,
Wassell, I.,
Deep network for image super-resolution with a dictionary learning
layer,
ICIP17(967-971)
IEEE DOI
1803
Computer architecture, DH-HEMTs, Dictionaries, Encoding,
Image reconstruction, Image resolution, Machine learning, Super-resolution
BibRef
Fan, R.,
Li, S.,
Lei, G.,
Yue, G.,
Shallow and deep convolutional networks for image super-resolution,
ICIP17(1847-1851)
IEEE DOI
1803
Convolution, Feature extraction, Image resolution,
Image restoration, Interpolation, Training, Videos, Super-resolution,
multi-scale manner
BibRef
Ren, H.Y.[Hao-Yu],
El-Khamy, M.[Mostafa],
Lee, J.W.[Jung-Won],
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural
Networks for Image Super Resolution,
WACV18(1423-1431)
IEEE DOI
1806
BibRef
Earlier:
Image Super Resolution Based on Fusing Multiple Convolution Neural
Networks,
NTIRE17(1050-1057)
IEEE DOI
1709
feedforward neural nets, image resolution,
learning (artificial intelligence), CT-SRCNN, SR efficiency, Tuning.
Convolution, Kernel, Neural networks, Training.
BibRef
Sharma, M.[Manoj],
Chaudhury, S.[Santanu],
Lall, B.[Brejesh],
Space-Time Super-Resolution Using Deep Learning Based Framework,
PReMI17(582-590).
Springer DOI
1711
BibRef
Cui, Z.[Zhen],
Chang, H.[Hong],
Shan, S.G.[Shi-Guang],
Zhong, B.[Bineng],
Chen, X.L.[Xi-Lin],
Deep Network Cascade for Image Super-resolution,
ECCV14(V: 49-64).
Springer DOI
1408
BibRef
Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Alignment, Registration for Super Resolution .