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
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 .