19.4.3.7.1 Deep Neural Networks, Deep Learning for Super Resolution

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

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, Pattern recognition 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, Pattern recognition, 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], Gonzàlez, 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, Pattern recognition 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 .