19.4.3.7 Generative Adversarial Network, Neural Networks for Super Resolution

Chapter Contents (Back)
Super Resolution. Neural Networks. Learning. GAN. Generative Adversarial Networks.
See also Learning for Super Resolution.
See also Deep Neural Networks, Deep Learning for Super Resolution.
See also Lightweight Super Resolution.

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

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

Xiao, Z.[Zeyu], Fu, X.Y.[Xue-Yang], Huang, J.[Jie], Cheng, Z.[Zhen], Xiong, Z.W.[Zhi-Wei],
Space-Time Distillation for Video Super-Resolution,
CVPR21(2113-2122)
IEEE DOI 2111
Training, Performance evaluation, Knowledge engineering, Wearable computers, Superresolution, Network architecture, Pattern recognition 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

Zareapoor, M.[Masoumeh], Celebi, M.E.[M. Emre], Yang, J.[Jie],
Diverse Adversarial Network for Image Super-Resolution,
SP:IC(74), 2019, pp. 191-200.
Elsevier DOI 1904
Super-resolution, Adversarial network, Diverse GAN, Deep learning BibRef

Li, Y.[Yue], Liu, D.[Dong], 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

Weng, W.M.[Wen-Ming], Zhang, Y.[Yueyi], Xiong, Z.W.[Zhi-Wei],
Boosting Event Stream Super-Resolution with a Recurrent Neural Network,
ECCV22(VI:470-488).
Springer DOI 2211
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

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

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

Li, X.H.[Xing-Hua], Du, Z.S.[Zheng-Shun], Huang, Y.Y.[Yan-Yuan], Tan, Z.Y.[Zhen-Yu],
A deep translation (GAN) based change detection network for optical and SAR remote sensing images,
PandRS(179), 2021, pp. 14-34.
Elsevier DOI 2108
Change detection, Deep translation, Depthwise separable convolution, GAN, Multi-scale loss, Optical and SAR images BibRef

Tan, Z.Y.[Zhen-Yu], Yue, P.[Peng], Di, L.P.[Li-Ping], Tang, J.M.[Jun-Mei],
Deriving High Spatiotemporal Remote Sensing Images Using Deep 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

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.D.[Hua-Dong], 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

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

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, geophysical image processing, image enhancement, superresolution. 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

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

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

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

Dorr, F.[Francisco],
Satellite Image Multi-Frame Super Resolution Using 3D Wide-Activation Neural Networks,
RS(12), No. 22, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Gao, H.X.[Hong-Xia], Chen, Z.H.[Zhan-Hong], Huang, B.Y.[Bin-Yang], Chen, J.H.[Jia-He], Li, Z.F.[Zhi-Fu],
Image super-resolution based on conditional generative adversarial network,
IET-IPR(14), No. 13, November 2020, pp. 3006-3013.
DOI Link 2012
BibRef

Urazoe, K.[Kazuya], Kuroki, N.[Nobutaka], Kato, Y.[Yu], Ohtani, S.[Shinya], Hirose, T.[Tetsuya], Numa, M.[Masahiro],
Multi-Category Image Super-Resolution with Convolutional Neural Network and Multi-Task Learning,
IEICE(E104-D), No. 1, January 2021, pp. 183-193.
WWW Link. 2101
BibRef

Suryanarayana, G.[Gunnam], Rajesh, K.N.V.P.S.[Kandala N.V.P.S.], Yang, J.[Jie],
Super-Resolution Based on Residual Learning and Optimized Phase Stretch Transform,
IJIG(21), No. 1 2021, pp. 2150008.
DOI Link 2102
BibRef

Yang, J.M.[Jie-Ming], Ge, H.W.[Hong-Wei], Yang, J.L.[Jin-Long], Tong, Y.B.[Yu-Bing],
Image compact-resolution and reconstruction using reversible network,
IET-IPR(14), No. 16, 19 December 2020, pp. 4376-4384.
DOI Link 2103
BibRef

Lin, H.[Hong], Fan, J.[Jing], Zhang, Y.Y.[Yang-Yi], Peng, D.[Dewei],
Generative adversarial image super-resolution network for multiple degradations,
IET-IPR(14), No. 17, 24 December 2020, pp. 4520-4527.
DOI Link 2104
BibRef

Liu, Z.S.[Zhi-Song], Siu, W.C.[Wan-Chi], Chan, Y.L.,
Photo-Realistic Image Super-Resolution via Variational Autoencoders,
CirSysVideo(31), No. 4, April 2021, pp. 1351-1365.
IEEE DOI 2104
Generative adversarial networks, Distortion, Image reconstruction, Feature extraction, Distortion measurement, divergence BibRef

Liu, Z.S.[Zhi-Song], Siu, W.C.[Wan-Chi], Wang, L.W.[Li-Wen],
Variational AutoEncoder for Reference based Image Super-Resolution,
NTIRE21(516-525)
IEEE DOI 2109
Quantization (signal), Computational modeling, Superresolution, Space exploration, Pattern recognition BibRef

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

Tian, C.W.[Chun-Wei], Xu, Y.[Yong], Zuo, W.M.[Wang-Meng], Zhang, B.[Bob], Fei, L.[Lunke], Lin, C.W.[Chia-Wen],
Coarse-to-Fine CNN for Image Super-Resolution,
MultMed(23), 2021, pp. 1489-1502.
IEEE DOI 2106
Feature extraction, Training, Image reconstruction, Fuses, Visualization, Residual neural networks, Cascaded structure, Image super-resolution BibRef

Huang, Y.S.[Yong-Song], Jiang, Z.[Zetao], Lan, R.[Rushi], Zhang, S.Q.[Shao-Qin], Pi, K.[Kui],
Infrared Image Super-Resolution via Transfer Learning and PSRGAN,
SPLetters(28), 2021, pp. 982-986.
IEEE DOI 2106
Feature extraction, Transfer learning, Superresolution, Training, Generators, Task analysis, Neural networks, Super-resolution, image processing BibRef

Jiang, Z.[Zetao], Pi, K.[Kui], Huang, Y.S.[Yong-Song], Qian, Y.[Yi], Zhang, S.Q.[Shao-Qin],
Difference Value Network for Image Super-Resolution,
SPLetters(28), 2021, pp. 1070-1074.
IEEE DOI 2106
Image reconstruction, Feature extraction, Convolution, Superresolution, Training, Graphics, Correlation, difference value BibRef

Islam, S.R.[Sheikh Rafiul], Maity, S.P.[Santi P.], Ray, A.K.[Ajoy Kumar],
On learning based compressed sensing for high resolution image reconstruction,
IET-IPR(15), No. 2, 2021, pp. 393-404.
DOI Link 2106
BibRef

Zhang, D.Y.[Dong-Yang], Shao, J.[Jie], Liang, Z.W.[Zhen-Wen], Gao, L.L.[Lian-Li], Shen, H.T.[Heng Tao],
Large Factor Image Super-Resolution With Cascaded Convolutional Neural Networks,
MultMed(23), 2021, pp. 2172-2184.
IEEE DOI 2107
Convolution, Convolutional neural networks, Image reconstruction, Computer architecture, Computational efficiency, image super-resolution BibRef

Li, X.G.[Xiao-Guang], Dong, N.[Ning], Huang, J.L.[Jiang-Lu], Zhuo, L.[Li], Li, J.F.[Jia-Feng],
A discriminative self-attention cycle GAN for face super-resolution and recognition,
IET-IPR(15), No. 11, 2021, pp. 2614-2628.
DOI Link 2108
BibRef

Xi, S.[Si], Wei, J.[Jia], Zhang, W.D.[Wei-Dong],
Pixel-Guided Dual-Branch Attention Network for Joint Image Deblurring and Super-Resolution,
NTIRE21(532-540)
IEEE DOI 2109
Training, Superresolution, Feature extraction, Image restoration, Pattern recognition BibRef

Nachaoui, M., Afraites, L., Laghrib, A.,
A Regularization by Denoising super-resolution method based on genetic algorithms,
SP:IC(99), 2021, pp. 116505.
Elsevier DOI 2111
Super-resolution, Genetic algorithms, Nonlocal regularization BibRef

Castillo, A.[Angela], Escobar, M.[María], Pérez, J.C.[Juan C.], Romero, A.[Andrés], Timofte, R.[Radu], Van Gool, L.J.[Luc J.], Arbelaez, P.[Pablo],
Generalized Real-World Super-Resolution through Adversarial Robustness,
AIM21(1855-1865)
IEEE DOI 2112
Degradation, Training, Adaptation models, Computational modeling, Superresolution, Robustness BibRef

Liu, Y.H.[Yan-Hong], Li, S.[Sumei], Liu, A.[Anqi],
Two-Way Guided Super-Resolution Reconstruction Network Based on Gradient Prior,
ICIP21(1819-1823)
IEEE DOI 2201
Convolution, Aggregates, Superresolution, Benchmark testing, Feature extraction, Image restoration, Super-resolution, multi-scale BibRef

Yan, Y.T.[Yi-Tong], Liu, C.C.[Chuang-Chuang], Chen, C.Y.[Chang-You], Sun, X.F.[Xian-Fang], Jin, L.C.[Long-Cun], Peng, X.Y.[Xin-Yi], Zhou, X.[Xiang],
Fine-Grained Attention and Feature-Sharing Generative Adversarial Networks for Single Image Super-Resolution,
MultMed(24), 2022, pp. 1473-1487.
IEEE DOI 2204
Generators, Feature extraction, Superresolution, Generative adversarial networks, Image reconstruction, Standards, image super-resolution BibRef

Hu, Y.T.[Yan-Ting], Li, J.[Jie], Huang, Y.F.[Yuan-Fei], Gao, X.B.[Xin-Bo],
Image Super-Resolution With Self-Similarity Prior Guided Network and Sample-Discriminating Learning,
CirSysVideo(32), No. 4, April 2022, pp. 1966-1985.
IEEE DOI 2204
Feature extraction, Superresolution, Image reconstruction, Training, Optimization, Generative adversarial networks, single image super-resolution BibRef

Liu, Y.Q.[Yu-Qing], Wang, S.Q.[Shi-Qi], Zhang, J.[Jian], Wang, S.S.[Shan-She], Ma, S.W.[Si-Wei], Gao, W.[Wen],
Iterative Network for Image Super-Resolution,
MultMed(24), 2022, pp. 2259-2272.
IEEE DOI 2205
Degradation, Superresolution, Optimization, Image restoration, Visualization, Convolution, Training, feature normalization BibRef

Liu, Y.Q.[Yu-Qing], Jia, Q.[Qi], Fan, X.[Xin], Wang, S.S.[Shan-She], Ma, S.W.[Si-Wei], Gao, W.[Wen],
Cross-SRN: Structure-Preserving Super-Resolution Network With Cross Convolution,
CirSysVideo(32), No. 8, August 2022, pp. 4927-4939.
IEEE DOI 2208
Image edge detection, Convolution, Image restoration, Feature extraction, Information filters, Visualization, structure-preservation BibRef

Yin, G.H.[Guang-Hao], Wang, W.[Wei], Yuan, Z.H.[Ze-Huan], Ji, W.[Wei], Yu, D.D.[Dong-Dong], Sun, S.[Shouqian], Chua, T.S.[Tat-Seng], Wang, C.H.[Chang-Hu],
Conditional Hyper-Network for Blind Super-Resolution With Multiple Degradations,
IP(31), 2022, pp. 3949-3960.
IEEE DOI 2206
Degradation, Task analysis, Feature extraction, Adaptation models, Kernel, Training, Superresolution, Blind super-resolution, multi-degradation shift BibRef

Frizza, T.[Tristan], Dansereau, D.G.[Donald G.], Seresht, N.M.[Nagita Mehr], Bewley, M.[Michael],
Semantically accurate super-resolution Generative Adversarial Networks,
CVIU(221), 2022, pp. 103464.
Elsevier DOI 2206
Super-resolution, Semantic segmentation, Generative adversarial networks, Multi-modal learning BibRef

Tian, C.[Chunwei], Xu, Y.[Yong], Zuo, W.M.[Wang-Meng], Lin, C.W.[Chia-Wen], Zhang, D.[David],
Asymmetric CNN for Image Superresolution,
SMCS(52), No. 6, June 2022, pp. 3718-3730.
IEEE DOI 2206
Task analysis, Convolution, Training, Superresolution, Feature extraction, Kernel, Degradation, Asymmetric architecture, multiple degradation task BibRef

Kong, L.[Linhua], Wang, Y.M.[Yi-Ming], Chang, D.X.[Dong-Xia], Zhao, Y.[Yao],
Contour enhanced image super-resolution,
JVCIR(89), 2022, pp. 103659.
Elsevier DOI 2212
Contour, Attention mechanism, Deep convolution neural network BibRef

Kim, H.[Heewon], Hong, S.[Seokil], Han, B.H.[Bo-Hyung], Myeong, H.[Heesoo], Lee, K.M.[Kyoung Mu],
Fine-grained neural architecture search for image super-resolution,
JVCIR(89), 2022, pp. 103654.
Elsevier DOI 2212
Image super-resolution, Neural architecture search, Convolutional neural network BibRef

Bhasha, A.V.[A. Valli], Reddy, B.D.V.[B. D. Venkatramana],
Automated Image Super Resolution with the Aid of Activation Function Optimized Deep CNN and Adaptive Wavelet Lifting Approach,
IJIG(22), No. 5 2022, pp. 2250046.
DOI Link 2212
BibRef

Choi, Y.J.[Young-Ju], Lee, Y.W.[Young-Woon], Kim, B.G.[Byung-Gyu],
Group-based bi-directional recurrent wavelet neural network for efficient video super-resolution (VSR),
PRL(164), 2022, pp. 246-253.
Elsevier DOI 2212
Attention mechanism, Discrete wavelet transform, Recurrent neural network, Video super-resolution BibRef

Chan, K.C.K.[Kelvin C.K.], Xu, X.Y.[Xiang-Yu], Wang, X.[Xintao], Gu, J.[Jinwei], Loy, C.C.[Chen Change],
GLEAN: Generative Latent Bank for Image Super-Resolution and Beyond,
PAMI(45), No. 3, March 2023, pp. 3154-3168.
IEEE DOI 2302
BibRef
Earlier: A1, A3, A2, A4, A5:
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution,
CVPR21(14240-14249)
IEEE DOI 2111
Image restoration, Generative adversarial networks, Task analysis, Superresolution, Generators, Faces, Optimization, generative prior. Runtime, Imaging, Switches, Generative adversarial networks BibRef

Ma, H.C.[Hai-Chuan], Liu, D.[Dong], Wu, F.[Feng],
Rectified Wasserstein Generative Adversarial Networks for Perceptual Image Restoration,
PAMI(45), No. 3, March 2023, pp. 3648-3663.
IEEE DOI 2302
Image restoration, Generative adversarial networks, Training, Generators, Task analysis, Measurement, Superresolution, Wasserstein GAN (WGAN) BibRef

Zhao, G.H.[Guang-Hui], Li, Q.X.[Qing-Xia], Chen, Z.W.[Zhi-Wei], Lei, Z.Y.[Zhen-Yu], Xiao, C.W.[Cheng-Wang], Huang, Y.H.[Yu-Hang],
Visibility Extension of 1-D Aperture Synthesis by a Residual CNN for Spatial Resolution Enhancement,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303
BibRef

Xu, N.J.[Nai-Jie], Chen, X.H.[Xiao-Hui], Cao, Y.L.[You-Long], Zhang, W.[Wenyi],
Hybrid Post-Training Quantization for Super-Resolution Neural Network Compression,
SPLetters(30), 2023, pp. 379-383.
IEEE DOI 2305
Quantization (signal), Neural networks, Distortion, Videos, Sensitivity, Superresolution, Optimization, super-resolution neural network BibRef

Cao, J.F.[Jian-Fang], Hu, X.H.[Xiao-Hui], Cui, H.Y.[Hong-Yan], Liang, Y.C.[Yun-Chuan], Chen, Z.[Zeyu],
A generative adversarial network model fused with a self-attention mechanism for the super-resolution reconstruction of ancient murals,
IET-IPR(17), No. 8, 2023, pp. 2336-2349.
DOI Link 2306
image processing, image reconstruction, image representation, image sampling BibRef

Yuan, N.Z.[Nian-Zeng], Sun, B.Y.[Bang-Yong], Zheng, X.T.[Xiang-Tao],
Unsupervised real image super-resolution via knowledge distillation network,
CVIU(234), 2023, pp. 103736.
Elsevier DOI 2307
Super-resolution, Knowledge distillation, Degradation module, Convolutional neural network BibRef

Altekruger, F.[Fabian], Hertrich, J.[Johannes],
WPPNets and WPPFlows: The Power of Wasserstein Patch Priors for Superresolution,
SIIMS(16), No. 3, 2023, pp. 1033-1067.
DOI Link 2309
BibRef

Wang, X.[Xuan], Sun, L.J.[Li-Jun], Chehri, A.[Abdellah], Song, Y.C.[Yong-Chao],
A Review of GAN-Based Super-Resolution Reconstruction for Optical Remote Sensing Images,
RS(15), No. 20, 2023, pp. 5062.
DOI Link 2310
BibRef

Pang, B.[Boyu], Zhao, S.W.[Si-Wei], Liu, Y.[Yinnian],
The Use of a Stable Super-Resolution Generative Adversarial Network (SSRGAN) on Remote Sensing Images,
RS(15), No. 20, 2023, pp. 5064.
DOI Link 2310
BibRef

Guo, X.X.[Xiao-Xin], Tu, Z.C.[Zhen-Chuan], Zhang, H.R.[Hao-Ran], Dong, H.L.[Hong-Liang],
Super-resolution reconstruction based on generative adversarial networks with dual branch half instance normalization,
IET-IPR(18), No. 6, 2024, pp. 1434-1446.
DOI Link 2405
image reconstruction, image resolution BibRef

Tang, N.[Ni], Zhang, D.X.[Dong-Xiao], Gao, J.[Juhao], Qu, Y.[Yanyun],
FSRDiff: A fast diffusion-based super-resolution method using GAN,
JVCIR(101), 2024, pp. 104164.
Elsevier DOI 2406
Diffusion model, GAN, Super-resolution, Sampling speed BibRef


Lee, H.[Hwayoon], Kang, K.[Kyoungkook], Lee, H.[Hyeongmin], Baek, S.H.[Seung-Hwan], Cho, S.H.[Sung-Hyun],
UGPNet: Universal Generative Prior for Image Restoration,
WACV24(1587-1597)
IEEE DOI 2404
Uncertainty, Computational modeling, Superresolution, Noise reduction, Merging, Measurement uncertainty, Algorithms, image and video synthesis BibRef

Ma, W.[Wen], Lou, Q.W.[Qiu-Wen], Kazemi, A.[Arman], Faraone, J.[Julian], Afzal, T.[Tariq],
Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution,
VAQuality24(460-468)
IEEE DOI 2404
Training, Performance evaluation, Interpolation, Superresolution, Neural networks, Bit rate, Hardware BibRef

Park, J.K.[Joon-Kyu], Son, S.[Sanghyun], Lee, K.M.[Kyoung Mu],
Content-Aware Local GAN for Photo-Realistic Super-Resolution,
ICCV23(10551-10560)
IEEE DOI Code:
WWW Link. 2401
BibRef

Rempakos, P.[Pantelis], Vrigkas, M.[Michalis], Plissiti, M.E.[Marina E.], Nikou, C.[Christophoros],
Spatial Transformer Generative Adversarial Network for Image Super-resolution,
CIAP23(I:399-411).
Springer DOI 2312
BibRef

Pan, P.C.[Pin-Chi], Hsu, T.H.[Tzu-Hao], Wei, W.L.[Wen-Li], Lin, J.C.[Jen-Chun],
Global-Local Awareness Network for Image Super-Resolution,
ICIP23(1150-1154)
IEEE DOI 2312
BibRef

Kim, D.[Dayeon], Kim, M.C.[Mun-Churl],
SGSR: A Saliency-Guided Image Super-Resolution Network,
ICIP23(980-984)
IEEE DOI 2312
BibRef

Wei, M.[Min], Zhang, X.S.[Xue-Song],
Super-Resolution Neural Operator,
CVPR23(18247-18256)
IEEE DOI 2309
BibRef

Panaetov, A.[Alexander], Daou, K.E.[Karim Elhadji], Samenko, I.[Igor], Tetin, E.[Evgeny], Ivanov, I.[Ilya],
RDRN: Recursively Defined Residual Network for Image Super-resolution,
ACCV22(II:629-645).
Springer DOI 2307
BibRef

Zhang, X.D.[Xin-Dong], Zeng, H.[Hui], Zhang, L.[Lei],
Efficient Hardware-aware Neural Architecture Search for Image Super-resolution on Mobile Devices,
ACCV22(III:409-426).
Springer DOI 2307
BibRef

Chira, D.[Darius], Haralampiev, I.[Ilian], Winther, O.[Ole], Dittadi, A.[Andrea], Liévin, V.[Valentin],
Image Super-resolution with Deep Variational Autoencoders,
AIM22(395-411).
Springer DOI 2304
BibRef

Yoo, J.[Jinsu], Kim, T.[Taehoon], Lee, S.[Sihaeng], Kim, S.H.[Seung Hwan], Lee, H.L.[Hong-Lak], Kim, T.H.[Tae Hyun],
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution,
WACV23(4945-4954)
IEEE DOI 2302
Aggregates, Superresolution, Benchmark testing, Transformers, Cognition, Algorithms: Computational photography, Low-level and physics-based vision BibRef

Kansy, M.[Manuel], Balletshofer, J.[Julian], Naruniec, J.[Jacek], Schroers, C.[Christopher], Mignone, G.[Graziana], Gross, M.[Markus], Weber, R.M.[Romann M.],
Self-Supervised Effective Resolution Estimation with Adversarial Augmentations,
VAQuality23(573-582)
IEEE DOI 2302
Training, Image quality, Superresolution, Neural networks, Estimation, Training data, Self-supervised learning BibRef

Xue, B.X.[Bo-Xiang], Zhou, Z.H.[Zheng-Hua],
Multi-scale Visual Aggregation Residual Network for Super-Resolution,
ICIVC22(682-687)
IEEE DOI 2301
Visualization, Convolution, Superresolution, Neural networks, MIMICs, Feature extraction, Kernel, Multi-scale Residual Network, Super-Resolution BibRef

Wang, S.[Sen], Zheng, J.[Jin],
Multi-Scale Detail Enhancement Network for Image Super-Resolution,
ICPR22(161-167)
IEEE DOI 2212
Visualization, Fuses, Superresolution, Feature extraction, Image restoration, Data mining, Convolutional neural networks BibRef

Wang, Y.[Yan],
Edge-enhanced Feature Distillation Network for Efficient Super-Resolution,
NTIRE22(776-784)
IEEE DOI 2210
Training, Convolution, Image edge detection, Superresolution, Neural networks BibRef

Gu, J.J.[Jin-Jin], Cai, H.M.[Hao-Ming], Dong, C.Y.[Chen-Yu], Zhang, R.F.[Ruo-Fan], Zhang, Y.[Yulun], Yang, W.M.[Wen-Ming], Yuan, C.[Chun],
Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images,
ECCV22(XIX:583-598).
Springer DOI 2211
BibRef

Ji, Y.T.[Yan-Tao], Jiang, P.L.[Pei-Lin], Shi, J.G.[Jin-Gang], Guo, Y.[Yu], Zhang, R.[Ruiteng], Wang, F.[Fei],
Information-Growth Swin Transformer Network for Image Super-Resolution,
ICIP22(3993-3997)
IEEE DOI 2211
Adaptation models, Current transformers, Superresolution, Benchmark testing, Feature extraction, Data mining, Transformer BibRef

Vasileiou, C.[Christos], Smith, J.[Josiah], Thiagarajan, S.[Shiva], Nigh, M.[Matthew], Makris, Y.[Yiorgos], Torlak, M.[Murat],
Efficient CNN-Based Super Resolution Algorithms for MMwave Mobile Radar Imaging,
ICIP22(3803-3807)
IEEE DOI 2211
Laser radar, Superresolution, Radar imaging, Apertures, Radar polarimetry, Convolutional neural networks, Task analysis, Depth-wise Convolution BibRef

Korkmaz, C.[Cansu], Tekalp, A.M.[A. Murat], Dogan, Z.[Zafer],
MMSR: Multiple-Model Learned Image Super-Resolution Benefiting from Class-Specific Image Priors,
ICIP22(2816-2820)
IEEE DOI 2211
Training, Measurement, Degradation, Fuses, Superresolution, image super-resolution, multiple learned models, zero-shot learning BibRef

Montanaro, A.[Antonio], Valsesia, D.[Diego], Magli, E.[Enrico],
Exploring the Solution Space of Linear Inverse Problems with GAN Latent Geometry,
ICIP22(1381-1385)
IEEE DOI 2211
Geometry, Inverse problems, Atmospheric measurements, Superresolution, Semantics, Optimization methods, GANs BibRef

Li, J.[Junyi], Zhang, Z.[Zhilu], Liu, X.Y.[Xiao-Yu], Feng, C.[Chaoyu], Wang, X.T.[Xiao-Tao], Lei, L.[Lei], Zuo, W.M.[Wang-Meng],
Spatially Adaptive Self-Supervised Learning for Real-World Image Denoising,
CVPR23(9914-9924)
IEEE DOI 2309
BibRef

Zhang, Z.[Zhilu], Wang, R.[Ruohao], Zhang, H.Z.[Hong-Zhi], Chen, Y.J.[Yun-Jin], Zuo, W.M.[Wang-Meng],
Self-supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations,
ECCV22(XVIII:610-627).
Springer DOI 2211
BibRef

Li, J.C.[Jia-Cheng], Chen, C.[Chang], Cheng, Z.[Zhen], Xiong, Z.W.[Zhi-Wei],
MuLUT: Cooperating Multiple Look-Up Tables for Efficient Image Super-Resolution,
ECCV22(XVIII:238-256).
Springer DOI 2211
BibRef

Ma, C.[Cheng], Zhang, J.Y.[Jing-Yi], Zhou, J.[Jie], Lu, J.W.[Ji-Wen],
Learning Series-Parallel Lookup Tables for Efficient Image Super-Resolution,
ECCV22(XVII:305-321).
Springer DOI 2211
BibRef

Zhuang, K.[Kai], Yuan, Y.[Yuan], Wang, Q.[Qi],
DTransGAN: Deblurring Transformer Based on Generative Adversarial Network,
ICIP22(701-705)
IEEE DOI 2211
Training, Image registration, Surveillance, Semantics, Transformers, Generative adversarial networks, Cameras, Motion deblurring, skip connection BibRef

Wang, W.[Wei], Zhang, H.C.[Hao-Chen], Yuan, Z.H.[Ze-Huan], Wang, C.H.[Chang-Hu],
Unsupervised Real-World Super-Resolution: A Domain Adaptation Perspective,
ICCV21(4298-4307)
IEEE DOI 2203
Training, Convolution, Superresolution, Neural networks, Force, Generative adversarial networks, Decoding, BibRef

Yoon, K.J.[Kwang-Jin],
Simple and Efficient Unpaired Real-world Super-Resolution using Image Statistics,
AIM21(1983-1990)
IEEE DOI 2112
Measurement, Training, Degradation, Superresolution, Generative adversarial networks BibRef

Wang, L.G.[Long-Guang], Wang, Y.Q.[Ying-Qian], Lin, Z.P.[Zai-Ping], Yang, J.G.[Jun-Gang], An, W.[Wei], Guo, Y.L.[Yu-Lan],
Learning A Single Network for Scale-Arbitrary Super-Resolution,
ICCV21(4781-4790)
IEEE DOI 2203
Adaptation models, Costs, Convolution, Computational modeling, Superresolution, Task analysis, Vision applications and systems BibRef

Pesavento, M.[Marco], Volino, M.[Marco], Hilton, A.[Adrian],
Attention-based Multi-Reference Learning for Image Super-Resolution,
ICCV21(14677-14686)
IEEE DOI 2203
Superresolution, Memory management, Spatial coherence, Graphics processing units, Benchmark testing, Datasets and evaluation BibRef

Tu, Z.J.[Zhi-Jun], Hu, J.[Jie], Chen, H.T.[Han-Ting], Wang, Y.H.[Yun-He],
Toward Accurate Post-Training Quantization for Image Super Resolution,
CVPR23(5856-5865)
IEEE DOI 2309
BibRef

Xie, W.B.[Wen-Bin], Song, D.H.[De-Hua], Xu, C.[Chang], Xu, C.J.[Chun-Jing], Zhang, H.[Hui], Wang, Y.H.[Yun-He],
Learning Frequency-aware Dynamic Network for Efficient Super-Resolution,
ICCV21(4288-4297)
IEEE DOI 2203
Training, Visualization, Frequency-domain analysis, Computational modeling, Superresolution, Computer architecture, Machine learning architectures and formulations BibRef

Duong, V.V.[Vinh Van], Huu, T.N.[Thuc Nguyen], Yim, J.[Jonghoon], Jeon, B.W.[Byeung-Woo],
A Fast and Efficient Super-Resolution Network Using Hierarchical Dense Residual Learning,
ICIP21(1809-1813)
IEEE DOI 2201
Training, Performance evaluation, Superresolution, Computational efficiency, Convolutional neural networks, hierarchical dense residual learning BibRef

Gao, T.X.[Tian-Xiao], Xiong, R.Q.[Rui-Qin], Zhao, R.[Rui], Zhang, J.[Jian], Zhu, S.Y.[Shu-Yuan], Huang, T.J.[Tie-Jun],
Recover the Residual of Residual: Recurrent Residual Refinement Network for Image Super-Resolution,
ICIP21(1804-1808)
IEEE DOI 2201
Superresolution, Optimization methods, Transforms, Convolutional neural networks, Image reconstruction, recurrent convolutional neural network BibRef

Michelini, P.N.[Pablo Navarrete], Liu, H.[Hanwen], Lu, Y.[Yunhua], Jiang, X.Q.[Xing-Qun],
Back-Projection Pipeline,
ICIP21(1949-1953)
IEEE DOI 2201
Image resolution, Heuristic algorithms, Pipelines, Superresolution, Iterative algorithms, Task analysis, Super-resolution, causality BibRef

Huang, Q.[Qiu], Zhang, Y.X.[Yu-Xin], Hu, H.J.[Hao-Ji], Zhu, Y.D.[Yong-Dong], Zhao, Z.F.[Zhi-Feng],
Binarizing Super-Resolution Networks by Pixel-Correlation Knowledge Distillation,
ICIP21(1814-1818)
IEEE DOI 2201
Knowledge engineering, Quantization (signal), Computational modeling, Superresolution, Knowledge Distillation BibRef

Keles, O.[Onur], Tekalp, A.M.[A. Murat], Malik, J.[Junaid], Kiranyaz, S.[Serkan],
Self-Organized Residual Blocks for Image Super-Resolution,
ICIP21(589-593)
IEEE DOI 2201
Training, Superresolution, Neurons, Computer architecture, Network architecture, Taylor series, Task analysis, super-resolution BibRef

Song, D.H.[De-Hua], Wang, Y.H.[Yun-He], Chen, H.T.[Han-Ting], Xu, C.[Chang], Xu, C.J.[Chun-Jing], Tao, D.C.[Da-Cheng],
AdderSR: Towards Energy Efficient Image Super-Resolution,
CVPR21(15643-15652)
IEEE DOI 2111
Energy consumption, Visualization, Computational modeling, Superresolution, Refining, Neural networks, Pattern recognition BibRef

Wei, Y.X.[Yun-Xuan], Gu, S.H.[Shu-Hang], Li, Y.[Yawei], Timofte, R.[Radu], Jin, L.[Longcun], Song, H.J.[Heng-Jie],
Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training,
CVPR21(13380-13389)
IEEE DOI 2111
Training, Philosophical considerations, Codes, Superresolution, Training data, Pattern recognition BibRef

Kong, X.T.[Xiang-Tao], Zhao, H.[Hengyuan], Qiao, Y.[Yu], Dong, C.[Chao],
ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic,
CVPR21(12011-12020)
IEEE DOI 2111
Training, Superresolution, Pipelines, Containers, Image restoration, Pattern recognition BibRef

Wu, H.P.[Hai-Ping], Wang, X.L.[Xiao-Long],
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency,
ICCV21(10129-10139)
IEEE DOI 2203
Representation learning, Visualization, Image recognition, Semantics, Image representation, Object tracking, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Chen, Y.[Yinbo], Liu, S.F.[Si-Fei], Wang, X.L.[Xiao-Long],
Learning Continuous Image Representation with Local Implicit Image Function,
CVPR21(8624-8634)
IEEE DOI 2111
Training, Bridges, Visualization, Superresolution, Image representation BibRef

Zhang, Y.[Yiman], Chen, H.[Hanting], Chen, X.[Xinghao], Deng, Y.P.[Yi-Ping], Xu, C.J.[Chun-Jing], Wang, Y.H.[Yun-He],
Data-Free Knowledge Distillation For Image Super-Resolution,
CVPR21(7848-7857)
IEEE DOI 2111
Training, Knowledge engineering, Image coding, Superresolution, Training data, Smart cameras, Generators BibRef

Xing, W.Z.[Wen-Zhu], Egiazarian, K.[Karen],
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution,
CVPR21(3506-3515)
IEEE DOI 2111
Training, Superresolution, Noise reduction, Switches, Image restoration, Pattern recognition, Convolutional neural networks BibRef

Kim, Y.[Younggeun], Son, D.[Donghee],
Noise Conditional Flow Model for Learning the Super-Resolution Space,
NTIRE21(424-432)
IEEE DOI 2109
Training, Degradation, Visualization, Computational modeling, Superresolution BibRef

Cho, W.Y.[Woo-Yeong], Son, S.[Sanghyeok], Kim, D.S.[Dae-Shik],
Weighted Multi-Kernel Prediction Network for Burst Image Super-Resolution,
NTIRE21(404-413)
IEEE DOI 2109
Visualization, Computational modeling, Superresolution, Prediction methods, Motion compensation, Image restoration, Pattern recognition BibRef

Chen, L.[Liang], Zhang, J.W.[Jia-Wei], Pan, J.S.[Jin-Shan], Lin, S.N.[Song-Nan], Fang, F.[Faming], Ren, J.S.[Jimmy S.],
Learning a Non-blind Deblurring Network for Night Blurry Images,
CVPR21(10537-10545)
IEEE DOI 2111
Deconvolution, Convolution, Noise reduction, Estimation, Image restoration, Pattern recognition BibRef

Bai, H.R.[Hao-Ran], Cheng, S.S.[Song-Sheng], Tang, J.H.[Jin-Hui], Pan, J.S.[Jin-Shan],
Learning A Cascaded Non-Local Residual Network for Super-resolving Blurry Images,
NTIRE21(223-232)
IEEE DOI 2109
Training, Image edge detection, Superresolution, Benchmark testing, Image restoration BibRef

Rad, M.S.[Mohammad Saeed], Yu, T.[Thomas], Musat, C.[Claudiu], Ekenel, H.K.[Hazim Kemal], Bozorgtabar, B.[Behzad], Thiran, J.P.[Jean-Philippe],
Benefiting from Bicubically Down-Sampled Images for Learning Real-World Image Super-Resolution,
WACV21(1589-1598)
IEEE DOI 2106
Degradation, Training, Analytical models, Computational modeling, Superresolution BibRef

Roziere, B.[Baptiste], Rakotonirina, N.C.[Nathanaël Carraz], Hosu, V.[Vlad], Rasoanaivo, A.[Andry], Lin, H.[Hanhe], Couprie, C.[Camille], Teytaud, O.[Olivier],
Tarsier: Evolving Noise Injection in Super-Resolution GANs,
ICPR21(7028-7035)
IEEE DOI 2105
Training, Image quality, Gaussian noise, Superresolution, Quality assessment, Pattern recognition, Standards BibRef

Lee, W.[Wonkyung], Lee, J.[Junghyup], Kim, D.[Dohyung], Ham, B.[Bumsub],
Learning with Privileged Information for Efficient Image Super-resolution,
ECCV20(XXIV:465-482).
Springer DOI 2012
BibRef

Park, S.[Seobin], Yoo, J.[Jinsu], Cho, D.[Donghyeon], Kim, J.[Jiwon], Kim, T.H.[Tae Hyun],
Fast Adaptation to Super-resolution Networks via Meta-learning,
ECCV20(XXVII:754-769).
Springer DOI 2011
BibRef

Mousavi, S., Lee, D., Griffin, T., Steadman, D., Mockus, A.,
Collaborative Learning Of Semi-Supervised Clustering And Classification For Labeling Uncurated Data,
ICIP20(1716-1720)
IEEE DOI 2011
Labeling, Manuals, Data models, Training data, Training, Image analysis, Analytical models, image labeling, CNNs BibRef

He, Z., Dai, T., Lu, J., Jiang, Y., Xia, S.T.,
FAKD: Feature-Affinity Based Knowledge Distillation for Efficient Image Super-Resolution,
ICIP20(518-522)
IEEE DOI 2011
Knowledge engineering, Computational modeling, Correlation, Image resolution, Task analysis, Feature extraction, Convolutional neural networks BibRef

Lee, R.[Royson], Dudziak, L.[Lukasz], Abdelfattah, M.[Mohamed], Venieris, S.I.[Stylianos I.], Kim, H.[Hyeji], Wen, H.K.[Hong-Kai], Lane, N.D.[Nicholas D.],
Journey Towards Tiny Perceptual Super-Resolution,
ECCV20(XXVI:85-102).
Springer DOI 2011
Embedded super-resolution. BibRef

Xie, Y.C.[Yan-Chun], Xiao, J.M.[Ji-Min], Sun, M.J.[Ming-Jie], Yao, C.[Chao], Huang, K.Z.[Kai-Zhu],
Feature Representation Matters: End-to-end Learning for Reference-based Image Super-resolution,
ECCV20(IV:230-245).
Springer DOI 2011
BibRef

Lugmayr, A.[Andreas], Danelljan, M.[Martin], Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
Srflow: Learning the Super-resolution Space with Normalizing Flow,
ECCV20(V:715-732).
Springer DOI 2011
BibRef

Li, S., Zhu, H., Zha, K., Li, W.,
Super-Resolution Reconstruction Algorithm of Target Image Based on Learning Background,
ICIVC20(133-138)
IEEE DOI 2009
Image reconstruction, Dictionaries, Image restoration, Spatial resolution, Training, Video surveillance, super-resolution, interesting target BibRef

Gao, W.[Wei], Tao, L.F.[Lv-Fang], Zhou, L.J.[Lin-Jie], Yang, D.H.[Ding-Hao], Zhang, X.Y.[Xiao-Yu], Guo, Z.X.[Zi-Xuan],
Low-rate Image Compression with Super-resolution Learning,
CLIC20(607-610)
IEEE DOI 2008
Image coding, Image resolution, Bit rate, Training, Codecs, Pattern recognition BibRef

Xu, Y., Tseng, S.R., Tseng, Y., Kuo, H., Tsai, Y.,
Unified Dynamic Convolutional Network for Super-Resolution With Variational Degradations,
CVPR20(12493-12502)
IEEE DOI 2008
Convolution, Degradation, Kernel, Feature extraction, Training, Spatial resolution BibRef

Yang, F., Yang, H., Fu, J., Lu, H., Guo, B.,
Learning Texture Transformer Network for Image Super-Resolution,
CVPR20(5790-5799)
IEEE DOI 2008
Feature extraction, Image resolution, Task analysis, Machine learning, Indexes BibRef

Ahn, N., Yoo, J., Sohn, K.,
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution,
NTIRE20(1953-1961)
IEEE DOI 2008
Image resolution, Runtime, Training, Task analysis, Adaptation models, Degradation, Kernel BibRef

Lee, J.Y.[Jun-Yeop], Park, J.[Jaihyun], Lee, K.[Kanghyu], Min, J.[Jeongki], Kim, G.[Gwantae], Lee, B.[Bokyeung], Ku, B.[Bonhwa], Han, D.K.[David K.], Ko, H.S.[Han-Seok],
FBRNN: Feedback Recurrent Neural Network for Extreme Image Super-Resolution,
NTIRE20(2021-2028)
IEEE DOI 2008
Image resolution, Image reconstruction, Training, Feature extraction, Computational modeling, Image restoration BibRef

Yoo, J., Ahn, N., Sohn, K.,
Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy,
CVPR20(8372-8381)
IEEE DOI 2008
Task analysis, Spatial resolution, Training, Image restoration, Data models, Adaptation models BibRef

Liu, J.[Jie], Zhang, W.J.[Wen-Jie], Tang, Y.T.[Yu-Ting], Tang, J.[Jie], Wu, G.S.[Gang-Shan],
Residual Feature Aggregation Network for Image Super-Resolution,
CVPR20(2356-2365)
IEEE DOI 2008
Feature extraction, Convolution, Spatial resolution, Image reconstruction, Training, Task analysis BibRef

Maeda, S.[Shunta],
Unpaired Image Super-Resolution Using Pseudo-Supervision,
CVPR20(288-297)
IEEE DOI 2008
Training, Generators, Degradation, Image resolution, Image reconstruction, Kernel BibRef

Rout, L., Shah, S., Moorthi, S.M., Dhar, D.,
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution,
EarthVision20(757-767)
IEEE DOI 2008
Image resolution, Remote sensing, Learning (artificial intelligence), Mathematical model, Feature extraction BibRef

Soh, J.W., Cho, S., Cho, N.I.,
Meta-Transfer Learning for Zero-Shot Super-Resolution,
CVPR20(3513-3522)
IEEE DOI 2008
Task analysis, Kernel, Image resolution, Training, Adaptation models, Degradation, Optimization BibRef

Ji, X.Z.[Xiao-Zhong], Cao, Y.[Yun], Tai, Y.[Ying], Wang, C.J.[Cheng-Jie], Li, J.L.[Ji-Lin], Huang, F.Y.[Fei-Yue],
Real-World Super-Resolution via Kernel Estimation and Noise Injection,
NTIRE20(1914-1923)
IEEE DOI 2008
Kernel, Degradation, Data models, Training, Estimation, Spatial resolution BibRef

Cai, J., Meng, Z., Ho, C.M.,
Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction,
NTIRE20(1852-1861)
IEEE DOI 2008
Feature extraction, Spatial resolution, Signal resolution, Convolution, Generators BibRef

Kim, G.[Gwantae], Park, J.[Jaihyun], Lee, K.[Kanghyu], Lee, J.Y.[Jun-Yeop], Min, J.[Jeongki], Lee, B.[Bokyeung], Han, D.K.[David K.], Ko, H.S.[Han-Seok],
Unsupervised Real-World Super Resolution with Cycle Generative Adversarial Network and Domain Discriminator,
NTIRE20(1862-1871)
IEEE DOI 2008
Image resolution, Image color analysis, Generators, Training, Task analysis, Colored noise BibRef

Ren, H.Y.[Hao-Yu], Kheradmand, A.[Amin], El-Khamy, M.[Mostafa], Wang, S.Q.[Shuang-Quan], Bai, D.W.[Dong-Woon], Lee, J.W.[Jung-Won],
Real-World Super-Resolution using Generative Adversarial Networks,
NTIRE20(1760-1768)
IEEE DOI 2008
Image resolution, Degradation, Training, Kernel, Generative adversarial networks BibRef

Wang, L., Kim, T., Yoon, K.,
EventSR: From Asynchronous Events to Image Reconstruction, Restoration, and Super-Resolution via End-to-End Adversarial Learning,
CVPR20(8312-8322)
IEEE DOI 2008
Image reconstruction, Cameras, Streaming media, Spatial resolution, Image restoration, Training BibRef

Kim, Y.[Yongwoo], Choi, J.S.[Jae-Seok], Lee, J.[Jaehyup], Kim, M.C.[Mun-Churl],
A CNN-based Multi-scale Super-resolution Architecture on FPGA for 4k/8k Uhd Applications,
MMMod20(II:739-744).
Springer DOI 2003
BibRef

Noh, J., Bae, W., Lee, W., Seo, J., Kim, G.,
Better to Follow, Follow to Be Better: Towards Precise Supervision of Feature Super-Resolution for Small Object Detection,
ICCV19(9724-9733)
IEEE DOI 2004
convolutional neural nets, image resolution, object detection, proposal-based detectors, feature pooling, Detectors BibRef

Liu, Z.S.[Zhi-Song], Wang, L.W.[Li-Wen], Li, C.T.[Chu-Tak], Siu, W.C.[Wan-Chi], Chan, Y.L.[Yui-Lam],
Image Super-Resolution via Attention Based Back Projection Networks,
AIM19(3517-3525)
IEEE DOI 2004
Big Data, feature extraction, image reconstruction, image resolution, image sampling, iterative methods, back projection BibRef

Tariq, T., Gonzalez Bello, J.L., Kim, M.,
A HVS-Inspired Attention to Improve Loss Metrics for CNN-Based Perception-Oriented Super-Resolution,
CLI19(3904-3912)
IEEE DOI 2004
convolutional neural nets, feature extraction, image resolution, image restoration, visual perception, Visual Perception BibRef

Zhou, R., Süsstrunk, S.,
Kernel Modeling Super-Resolution on Real Low-Resolution Images,
ICCV19(2433-2443)
IEEE DOI 2004
cameras, convolutional neural nets, image resolution, image restoration, image sampling, interpolation, Generative adversarial networks BibRef

Xiong, D., Huang, K., Chen, S., Li, B., Jiang, H., Xu, W.,
NoUCSR: Efficient Super-Resolution Network without Upsampling Convolution,
AIM19(3378-3387)
IEEE DOI 2004
convolutional neural nets, image resolution, learning (artificial intelligence), NoUCSR, inference runtime, efficient neural networks BibRef

Huang, Y.F.[Yuan-Fei], Sun, X.P.[Xiao-Peng], Lu, W.[Wen], Li, J.[Jie], Gao, X.B.[Xin-Bo],
Un-Paired Real World Super-Resolution with Degradation Consistency,
AIM19(3458-3466)
IEEE DOI 2004
convolutional neural nets, image coding, image representation, image resolution, learning (artificial intelligence), degradation consistency BibRef

Fuoli, D., Gu, S., Timofte, R.,
Efficient Video Super-Resolution through Recurrent Latent Space Propagation,
AIM19(3476-3485)
IEEE DOI 2004
image resolution, motion compensation, motion estimation, recurrent neural nets, video signal processing BibRef

Chen, B.X.[Bo-Xun], Liu, T.J.[Tsung-Jung], Liu, K.H.[Kuan-Hsien], Liu, H.H.[Hsin-Hua], Pei, S.C.[Soo-Chang],
Image Super-Resolution Using Complex Dense Block on Generative Adversarial Networks,
ICIP19(2866-2870)
IEEE DOI 1910
Super-resolution (SR), dense block, generative adversarial network (GAN), visual quality. BibRef

Niu, Z.H.[Zhong-Han], Zhou, Y.H.[Yang-Hao], Yang, Y.B.[Yu-Bin], Fan, J.C.[Jian-Cong],
A Novel Attention Enhanced Dense Network for Image Super-resolution,
MMMod20(I:568-580).
Springer DOI 2003
BibRef

Navarrete Michelini, P.[Pablo], Chen, W.B.[Wen-Bin], Liu, H.W.[Han-Wen], Zhu, D.[Dan],
MGBPv2: Scaling Up Multi-Grid Back-Projection Networks,
AIM19(3399-3407)
IEEE DOI 2004
image reconstruction, image resolution, iterative methods, MGBPv2, multigrid back-projection networks, perceptual quality, adversarial BibRef

Kim, S.Y.[Soo Ye], Kim, M.C.[Mun-Churl],
A Multi-purpose Convolutional Neural Network for Simultaneous Super-Resolution and High Dynamic Range Image Reconstruction,
ACCV18(III:379-394).
Springer DOI 1906
BibRef

Zhao, Z.Q.[Zhong-Qiu], Hu, J.[Jian], Tian, W.D.[Wei-Dong], Ling, N.[Ning],
Cooperative Adversarial Network for Accurate Super Resolution,
ACCV18(II:98-114).
Springer DOI 1906
BibRef

Cheon, M.[Manri], Kim, J.H.[Jun-Hyuk], Choi, J.H.[Jun-Ho], Lee, J.S.[Jong-Seok],
Generative Adversarial Network-Based Image Super-Resolution Using Perceptual Content Losses,
PerceptualRest18(V:51-62).
Springer DOI 1905
BibRef

Tan, W., Yan, B., Bare, B.,
Feature Super-Resolution: Make Machine See More Clearly,
CVPR18(3994-4002)
IEEE DOI 1812
Image resolution, Feature extraction, Generative adversarial networks, Data models, Image recognition, Euclidean distance BibRef

Gao, P., Xue, J., Lu, K., Yan, Y.,
A fast Cascade Shape Regression Method based on CNN-based Initialization,
ICPR18(3037-3042)
IEEE DOI 1812
Shape, Face, Training, Interpolation, Convolution, Neural networks, Splines (mathematics) BibRef

Jiang, T., Wu, X., Yu, Z., Shui, W., Lu, G., Guo, S., Fei, H., Zhang, Q.,
Recursive Inception Network for Super-Resolution,
ICPR18(2759-2764)
IEEE DOI 1812
Feature extraction, Training, Image reconstruction, Image resolution, Periodic structures, Interpolation, Convolution BibRef

Kasem, H.M., Hung, K., Jiang, J.,
Revised Spatial Transformer Network towards Improved Image Super-resolutions,
ICPR18(2688-2692)
IEEE DOI 1812
Spatial resolution, Signal resolution, Image reconstruction, Training, Transforms, Interpolation BibRef

Bulat, A.[Adrian], Yang, J.[Jing], Tzimiropoulos, G.[Georgios],
To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image Degradation First,
ECCV18(VI: 187-202).
Springer DOI 1810
BibRef

Liu, Z.S.[Zhi-Song], Wan-Chi, S.[Siu],
Cascaded Random Forests for Fast Image Super-Resolution,
ICIP18(2531-2535)
IEEE DOI 1809
Image resolution, Decision trees, Mathematical model, Feature extraction, Image reconstruction, Signal resolution, machine learning BibRef

Sugawara, Y., Shiota, S., Kiya, H.,
Super-Resolution Using Convolutional Neural Networks Without Any Checkerboard Artifacts,
ICIP18(66-70)
IEEE DOI 1809
Convolution, Deconvolution, Training, Linear systems, Steady-state, Image resolution, Kernel, Super-Resolution, Checkerboard Artifacts BibRef

Zhang, K.H.[Kai-Hao], Li, D.X.[Dong-Xu], Luo, W.H.[Wen-Han], Ren, W.Q.[Wen-Qi], Stenger, B.[Björn], Liu, W.[Wei], Li, H.D.[Hong-Dong], Yang, M.H.[Ming-Hsuan],
Benchmarking Ultra-High-Definition Image Super-resolution,
ICCV21(14749-14758)
IEEE DOI 2203
Training, Performance evaluation, Correlation, Computational modeling, Superresolution, BibRef

Xu, J., Chae, Y., Stenger, B., Datta, A.,
Dense Bynet: Residual Dense Network for Image Super Resolution,
ICIP18(71-75)
IEEE DOI 1809
Convolutional codes, Training, Spatial resolution, Task analysis, Benchmark testing, Network architecture, image super resolution, image enhancement BibRef

Wang, Q.A.[Qi-Ang], Fan, H.J.[Hui-Jie], Cong, Y.[Yang], Tang, Y.D.[Yan-Dong],
Large receptive field convolutional neural network for image super-resolution,
ICIP17(958-962)
IEEE DOI 1803
Convolution, Convolutional neural networks, Feature extraction, Kernel, Spatial resolution, Training, Convolutional neural network, Super resolution BibRef

Xu, J., Chae, Y., Stenger, B.,
BYNET-SR: Image super resolution with a bypass connection network,
ICIP17(4053-4057)
IEEE DOI 1803
Convergence, Convolution, Image resolution, Mathematical model, Signal resolution, Task analysis, Training, super resolution BibRef

Bao, W.B.[Wen-Bo], Zhang, X.Y.[Xiao-Yun], Yan, S.P.[Shang-Peng], Gao, Z.Y.[Zhi-Yong],
Iterative convolutional neural network for noisy image super-resolution,
ICIP17(4038-4042)
IEEE DOI 1803
Convolutional neural networks, Image reconstruction, Noise measurement, Noise reduction, Spatial resolution, Training, super-resolution BibRef

Mu, Y.Y.[Yan-Yan], Dimitrakopoulos, R.[Roussos], Ferrie, F.P.[Frank P.],
Generalizing Generative Models: Application to Image Super-Resolution,
CRV16(8-15)
IEEE DOI 1612
BibRef
Earlier: A1, A3, A2:
Sparse image reconstruction by two phase RBM learning: Application to mine planning,
MVA15(316-320)
IEEE DOI 1507
Computer vision. Location of ore bodies. Boltzmann machine. BibRef

Abolhassani, A.A.H., Dimitrakopoulos, R.[Roussos], Ferrie, F.P.[Frank P.],
Anisotropic Interpolation of Sparse Images,
CRV16(440-447)
IEEE DOI 1612
anisotropic interpolation BibRef

Ma, L.[Lin], Zhang, Y.H.[Yong-Hua], Lu, Y.[Yan], Wu, F.[Feng], Zhao, D.B.[De-Bin],
Three-tiered network model for image hallucination,
ICIP08(357-360).
IEEE DOI 0810
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Deep Neural Networks, Deep Learning for Super Resolution .


Last update:Jul 13, 2024 at 15:27:21