18.4.3.3 Generative Adversarial Network, Neural Netowrks for Super Resolution

Chapter Contents (Back)
Super Resolution. Neural Networks. Generative Adversarial Networks.

Lu, Y.[Yao], Inamura, M.[Minoru], del Carmen Valdes, M.[Maria],
Super-resolution of the undersampled and subpixel shifted image sequence by a neural network,
IJIST(14), No. 1, 2004, pp. 8-15.
DOI Link 0406
BibRef

Miravet, C.[Carlos], Rodriguez, F.B.[Francisco B.],
A two-step neural-network based algorithm for fast image super-resolution,
IVC(25), No. 9, 1 September 2007, pp. 1449-1473.
Elsevier DOI 0707
Super-resolution; Multi-layer perceptron; Probabilistic neural network; Sequence processing; Image restoration BibRef

Miravet, C.[Carlos], Rodriguez, F.B.[Francisco B.],
A PCA-based super-resolution algorithm for short image sequences,
ICIP10(2025-2028).
IEEE DOI 1009
BibRef

Agrawal, D., Singhai, J.,
Multifocus image fusion using modified pulse coupled neural network for improved image quality,
IET-IPR(4), No. 6, December 2010, pp. 443-451.
DOI Link 1101
BibRef

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

Shi, Y.[Yukai], Wang, K.[Keze], Chen, C.Y.[Chong-Yu], Xu, L.[Li], Lin, L.[Liang],
Structure-Preserving Image Super-Resolution via Contextualized Multitask Learning,
MultMed(19), No. 12, December 2017, pp. 2804-2815.
IEEE DOI 1712
Context, Feature extraction, Image resolution, Image restoration, Interpolation, Neural networks, Training, Convolutional network, structure-preserving image super-resolution (SR) BibRef

Shi, Y.[Yukai], Zhong, H.Y.[Hao-Yu], Yang, Z.J.[Zhi-Jing], Yang, X.J.[Xiao-Jun], Lin, L.[Liang],
DDet: Dual-Path Dynamic Enhancement Network for Real-World Image Super-Resolution,
SPLetters(27), 2020, pp. 481-485.
IEEE DOI 2004
Kernel, Convolution, Feature extraction, Image resolution, Image restoration, Signal resolution, Mobile handsets, Neural Network BibRef

Song, Q., Xiong, R., Liu, D., Xiong, Z.W.[Zhi-Wei], Wu, F.[Feng], Gao, W.,
Fast Image Super-Resolution via Local Adaptive Gradient Field Sharpening Transform,
IP(27), No. 4, April 2018, pp. 1966-1980.
IEEE DOI 1802
gradient methods, image resolution, image restoration, HR image, LR image, blurry gradient field, gradient profile structure, upsampling BibRef

Zhang, H.C.[Hao-Chen], Liu, D.[Dong], Xiong, Z.W.[Zhi-Wei],
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 Recognition,
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

Yu, H.C.[Hai-Chao], Liu, D.[Ding], Shi, H.H.[Hong-Hui], Yu, H.C.[Han-Chao], Wang, Z.Y.[Zhang-Yang], 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

Li, Y.[Yue], Liu, D.[Dong], Li, H.Q.[Hou-Qiang], Li, L.[Li], Li, Z.[Zhu], Wu, F.[Feng],
Learning a Convolutional Neural Network for Image Compact-Resolution,
IP(28), No. 3, March 2019, pp. 1092-1107.
IEEE DOI 1812
data compression, image coding, image reconstruction, image resolution, image sampling, neural nets, video coding, up-sampling BibRef

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

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

Huang, Y., Shao, L., Frangi, A.F.,
Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning,
MedImg(37), No. 3, March 2018, pp. 815-827.
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

Pouliot, D.[Darren], Latifovic, R.[Rasim], Pasher, J.[Jon], Duffe, J.[Jason],
Landsat Super-Resolution Enhancement Using Convolution Neural Networks and Sentinel-2 for Training,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804
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

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 1807
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 Convolutional Network,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808
From High Temporal, Low Spatial resolution. BibRef

Wen, R.[Ran], Fu, K.[Kun], Sun, H.[Hao], 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

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

Arun, P.V.[Pattathal V.], Herrmann, I.[Ittai], Budhiraju, K.M.[Krishna M.], Karnieli, A.[Arnon],
Convolutional network architectures for super-resolution/sub-pixel mapping of drone-derived images,
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.[Huadong], Zhang, L.[Luming], Zhou, J.[Jie], Lu, Q.A.[Qi-Ang],
Hybrid image super-resolution using perceptual similarity from pre-trained network,
JVCIR(60), 2019, pp. 229-235.
Elsevier DOI 1903
Super-resolution, Hybrid method, Adaptive weight, Pre-trained VGG network 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

Xu, J., Li, M., Fan, J., Zhao, X., Chang, Z.,
Self-Learning Super-Resolution Using Convolutional Principal Component Analysis and Random Matching,
MultMed(21), No. 5, May 2019, pp. 1108-1121.
IEEE DOI 1905
convolution, feature extraction, image matching, image reconstruction, image resolution, principal component analysis 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

Jiang, K., Wang, Z., Yi, P., Wang, G., Lu, T., Jiang, J.,
Edge-Enhanced GAN for Remote Sensing Image Superresolution,
GeoRS(57), No. 8, August 2019, pp. 5799-5812.
IEEE DOI 1908
edge detection, feature extraction, gallium compounds, geophysical image processing, image enhancement, superresolution. 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

Zhang, X.Y.[Xin-Yi], Dong, H.[Hang], Hu, Z.[Zhe], Lai, W.S.[Wei-Sheng], Wang, F.[Fei], Yang, M.H.[Ming-Hsuan],
Gated Fusion Network for Degraded Image Super Resolution,
IJCV(128), No. 6, June 2020, pp. 1699-1721.
Springer DOI 2006
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

Qiao, J.J.[Jiao-Jiao], Song, H.H.[Hui-Hui], Zhang, K.H.[Kai-Hua], Zhang, X.L.[Xiao-Lu], Liu, Q.S.[Qing-Shan],
Image super-resolution using conditional generative adversarial network,
IET-IPR(13), No. 14, 12 December 2019, pp. 2673-2679.
DOI Link 1912
BibRef

Deng, X., Dragotti, P.L.,
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

Mustafa, A., Khan, S.H., Hayat, M., Shen, J., Shao, L.,
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

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

Xue, S.K.[Sheng-Ke], Qiu, W.Y.[Wen-Yuan], Liu, F.[Fan], Jin, X.Y.[Xin-Yu],
Faster image super-resolution by improved frequency-domain neural networks,
SIViP(14), No. 2, March 2020, pp. 257-265.
WWW Link. 2003
BibRef

Xiong, Y.F.[Ying-Fei], Guo, S.X.[Shan-Xin], Chen, J.S.[Jin-Song], Deng, X.P.[Xin-Ping], Sun, L.Y.[Lu-Yi], Zheng, X.R.[Xiao-Rou], Xu, W.[Wenna],
Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004
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., Yuan, Q., Li, J., Sun, J., Zhang, X.,
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

Amaranageswarao, G.[Gadipudi], Deivalakshmi, S., Ko, S.B.[Seok-Bum],
Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network,
JVCIR(70), 2020, pp. 102819.
Elsevier DOI 2007
Grouped convolution, Low-dose X-ray CT, Residual-in-dense, Super resolution, Wavelet sub-bands BibRef

Kim, J., Jung, C., Kim, C.,
Dual Back-Projection-Based Internal Learning for Blind Super-Resolution,
SPLetters(27), 2020, pp. 1190-1194.
IEEE DOI 2007
Super-resolution, blind super-resolution, internal learning 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.[Nick],
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

Romero, L.S.[Luis Salgueiro], Marcello, J.[Javier], Vilaplaan, V.[Verónica],
Super-Resolution of Sentinel-2 Imagery Using Generative Adversarial Networks,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008
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

Ma, J.B.[Jia-Bo], Yu, J.Y.[Jing-Ya], Liu, S.B.[Si-Bo], Chen, L.[Li], Li, X.[Xu], Feng, J.[Jie], Chen, Z.X.[Zhi-Xing], Zeng, S.Q.[Shao-Qun], Liu, X.L.[Xiu-Li], Cheng, S.H.[Sheng-Hua],
PathSRGAN: Multi-Supervised Super-Resolution for Cytopathological Images Using Generative Adversarial Network,
MedImg(39), No. 9, September 2020, pp. 2920-2930.
IEEE DOI 2009
Generators, Image reconstruction, Cervical cancer, Microscopy, Cervical cancer, cytopathological images, super resolution 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., Tao, L., Zhou, L., Yang, D., Zhang, X., Guo, Z.,
Low-rate Image Compression with Super-resolution Learning,
CLIC20(607-610)
IEEE DOI 2008
Image coding, Image resolution, Bit rate, Training, Codecs, Pattern recognition, Conferences 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, Computer vision 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.[Hanseok],
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

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

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

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

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., Cao, Y., Tai, Y., Wang, C., Li, J., Huang, F.,
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

Liu, Z., Siu, W., Wang, L., Li, C., Cani, M., Chan, Y.,
Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder,
NTIRE20(1788-1797)
IEEE DOI 2008
Noise reduction, Training, Mathematical model, Spatial resolution, Decoding, Distortion 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.[Hanseok],
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

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

Ren, H.[Haoyu], Kheradmand, A.[Amin], El-Khamy, M.[Mostafa], Wang, S.Q.[Shuang-Quan], Bai, D.[Dongwoon], 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

Li, J., Yuan, Y., Mei, K., Fang, F.,
Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution,
CLI19(3814-3823)
IEEE DOI 2004
Code, Super Resolution.
WWW Link. convolutional neural nets, fractals, image resolution, learning (artificial intelligence), topology, CNN BibRef

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., Wang, L., Li, C., Siu, W., Chan, Y.,
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., Sun, X., Lu, W., Li, J., Gao, X.,
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, computer vision 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., Zhang, H., Kim, J., Hsieh, C., Lee, J.,
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

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

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

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

Liu, J.[Jie], Jung, C.[Cheolkon],
Multiple Connected Residual Network for Image Enhancement on Smartphones,
PerceptualRest18(V:182-196).
Springer DOI 1905
BibRef

Hui, Z.[Zheng], Wang, X.[Xiumei], Deng, L.[Lirui], Gao, X.[Xinbo],
Perception-Preserving Convolutional Networks for Image Enhancement on Smartphones,
PerceptualRest18(V:197-213).
Springer DOI 1905
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

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, Computer vision, Signal resolution 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

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

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, Computer vision, 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

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

Liling, Z., Zelin, Z., Quansen, S.,
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

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

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

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

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

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

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

Ren, H.[Haoyu], 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

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

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
Registration for Super Resolution .


Last update:Sep 24, 2020 at 19:44:22