19.4.3.6 Learning for Super Resolution

Chapter Contents (Back)
Super Resolution. Learning. Various learning issues, more details for:
See also Generative Adversarial Network, Neural Networks for Super Resolution.
See also Deep Neural Networks, Deep Learning for Super Resolution.
See also Lightweight Super Resolution.

Rajaram, S.[Shyamsundar], Gupta, M.D.[Mithun Das], Petrovic, N.[Nemanja], Huang, T.S.[Thomas S.],
Learning-Based Nonparametric Image Super-Resolution,
JASP(2006), 2006, pp. 1-11.
WWW Link. 0603
BibRef
Earlier: A2, A1, A3, A4:
Non-Parametric Image Super-Resolution Using Multiple Images,
ICIP05(II: 89-92).
IEEE DOI 0512
BibRef

Gupta, M.D.[Mithun Das], Rajaram, S.[Shyamsundar], Petrovic, N.[Nemanja], Huang, T.S.[Thomas S.],
Models for Patch-Based Image Restoration,
JIVP(2009), No. 2009, pp. xx-yy.
DOI Link 0903
BibRef
Earlier: BP06(17).
IEEE DOI 0609
BibRef
And:
Classifiers for Motion,
ICPR06(II: 593-596).
IEEE DOI 0609
BibRef

Dang, C., Aghagolzadeh, M.[Mohammad], Radha, H.[Hayder],
Image Super-Resolution via Local Self-Learning Manifold Approximation,
SPLetters(21), No. 10, October 2014, pp. 1245-1249.
IEEE DOI 1407
Approximation methods BibRef

Dang, C.[Chinh], Radha, H.[Hayder],
Fast image super-resolution via selective manifold learning of high-resolution patches,
ICIP15(1319-1323)
IEEE DOI 1512
Grassmann manifold distance BibRef

Tang, Y.[Yi], Yuan, Y.[Yuan],
Learning From Errors in Super-Resolution,
Cyber(44), No. 11, November 2014, pp. 2143-2154.
IEEE DOI 1411
image resolution BibRef

Mohaoui, S.[Souad], Hakim, A.[Abdelilah], Raghay, S.[Said],
Bi-dictionary learning model for medical image reconstruction from undersampled data,
IET-IPR(14), No. 10, August 2020, pp. 2130-2139.
DOI Link 2008
BibRef

Li, Y.B.[Yong-Bo], Dong, W.S.[Wei-Sheng], Xie, X.M.[Xue-Mei], Shi, G.M.[Guang-Ming], Wu, J.J.[Jin-Jian], Li, X.[Xin],
Image Super-Resolution With Parametric Sparse Model Learning,
IP(27), No. 9, September 2018, pp. 4638-4650.
IEEE DOI 1807
image reconstruction, image resolution, inverse problems, learning (artificial intelligence), HR images, LR/HR patch pairs, sparse representation BibRef

Lee, J.W.[Jae-Won], Lee, O.Y.[Oh-Young], Kim, J.O.[Jong-Ok],
Dual learning based compression noise reduction in the texture domain,
JVCIR(43), No. 1, 2017, pp. 98-107.
Elsevier DOI 1702
Compression noise BibRef

Lee, O.Y.[Oh-Young], Lee, J.W.[Jae-Won], Kim, J.O.[Jong-Ok],
Combining self-learning based super-resolution with denoising for noisy images,
JVCIR(48), No. 1, 2017, pp. 66-76.
Elsevier DOI 1708
Self-learning BibRef

Lee, O.Y.[Oh-Young], Lee, J.W.[Jae-Won], Lee, D.Y., Kim, J.O.[Jong-Ok],
Joint super-resolution and compression artifact reduction based on dual-learning,
VCIP16(1-4)
IEEE DOI 1701
Hafnium BibRef

Li, L.L.[Ling-Ling], Zhang, S.[Sibo], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Yang, S.Y.[Shu-Yuan], Tang, X.[Xu],
Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link 1911
BibRef

Marivani, I.[Iman], Tsiligianni, E.[Evaggelia], Cornelis, B.[Bruno], Deligiannis, N.[Nikos],
Multimodal Deep Unfolding for Guided Image Super-Resolution,
IP(29), 2020, pp. 8443-8456.
IEEE DOI 2008
BibRef
And:
Joint Image Super-Resolution Via Recurrent Convolutional Neural Networks With Coupled Sparse Priors,
ICIP20(868-872)
IEEE DOI 2011
BibRef
Earlier:
Learned Multimodal Convolutional Sparse Coding for Guided Image Super-Resolution,
ICIP19(2891-2895)
IEEE DOI 1910
Image resolution, Machine learning, Convolutional codes, Image coding, Neural networks, Imaging, Image reconstruction, interpretable convolutional neural networks. Convolution, Signal resolution, Encoding, multimodal image fusion. Guided image super-resolution, convolutional sparse coding, multimodal deep neural networks BibRef

Sun, W.J.[Wan-Jie], Chen, Z.Z.[Zhen-Zhong],
Learning Discrete Representations From Reference Images for Large Scale Factor Image Super-Resolution,
IP(31), 2022, pp. 1490-1503.
IEEE DOI 2202
Vector quantization, Task analysis, Superresolution, Sun, Spatial resolution, Periodic structures, Neural networks, vector quantization BibRef

Li, S.[Shang], Zhang, G.X.[Gui-Xuan], Luo, Z.X.[Zheng-Xiong], Liu, J.[Jie], Zeng, Z.[Zhi], Zhang, S.W.[Shu-Wu],
From general to specific: Online updating for blind super-resolution,
PR(127), 2022, pp. 108613.
Elsevier DOI 2205
Blind super-resolution, Online updating, Internal learning, External learning BibRef

Ma, Q.[Qing], Jiang, J.J.[Jun-Jun], Liu, X.M.[Xian-Ming], Ma, J.Y.[Jia-Yi],
Multi-Task Interaction Learning for Spatiospectral Image Super-Resolution,
IP(31), 2022, pp. 2950-2961.
IEEE DOI 2205
Task analysis, Superresolution, Spatial resolution, Hyperspectral imaging, Correlation, Image reconstruction, feature interaction BibRef

Chen, H.G.[Hong-Gang], Dong, L.[Ling], Yang, H.[Hong], He, X.H.[Xiao-Hai], Zhu, C.[Ce],
Unsupervised Real-World Image Super-Resolution via Dual Synthetic-to-Realistic and Realistic-to-Synthetic Translations,
SPLetters(29), 2022, pp. 1282-1286.
IEEE DOI 2206
Training, Image resolution, Testing, Data models, Degradation, Toy manufacturing industry, Superresolution, Bilateral filtering, unsupervised learning BibRef

Son, S.[Sanghyun], Kim, J.[Jaeha], Lai, W.S.[Wei-Sheng], Yang, M.H.[Ming-Hsuan], Lee, K.M.[Kyoung Mu],
Toward Real-World Super-Resolution via Adaptive Downsampling Models,
PAMI(44), No. 11, November 2022, pp. 8657-8670.
IEEE DOI 2210
Kernel, Training, Superresolution, Image reconstruction, Unsupervised learning, Degradation, Adaptation models BibRef

Son, S.[Sanghyun], Lee, K.M.[Kyoung Mu],
SRWarp: Generalized Image Super-Resolution under Arbitrary Transformation,
CVPR21(7778-7787)
IEEE DOI 2111
Deformable models, Adaptation models, Visualization, Shape, Superresolution, Pattern recognition, Task analysis BibRef

Shi, Y.[Yukai], Li, H.[Hao], Zhang, S.[Sen], Yang, Z.J.[Zhi-Jing], Wang, X.[Xiao],
Criteria Comparative Learning for Real-Scene Image Super-Resolution,
CirSysVideo(32), No. 12, December 2022, pp. 8476-8485.
IEEE DOI 2212
Training data, Image restoration, Superresolution, Degradation, Feature extraction, Comparative Learning, criteria, image super-resolution BibRef

Li, H.[Hao], Qin, J.H.[Jing-Hui], Yang, Z.J.[Zhi-Jing], Wei, P.X.[Peng-Xu], Pan, J.S.[Jin-Shan], Lin, L.[Liang], Shi, Y.[Yukai],
Real-World Image Super-Resolution by Exclusionary Dual-Learning,
MultMed(25), 2023, pp. 4752-4763.
IEEE DOI 2311
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

Wei, P.X.[Peng-Xu], Xie, Z.W.[Zi-Wei], Li, G.B.[Guan-Bin], Lin, L.[Liang],
Taylor Neural Network for Real-World Image Super-Resolution,
IP(32), 2023, pp. 1942-1951.
IEEE DOI 2303
Neural networks, Image reconstruction, Superresolution, Task analysis, Degradation, Taylor series, Deep learning, Taylor skip connection BibRef

Wei, P.X.[Peng-Xu], Xie, Z.W.[Zi-Wei], Lu, H.[Hannan], Zhan, Z.Y.[Zong-Yuan], Ye, Q.X.[Qi-Xiang], Zuo, W.M.[Wang-Meng], Lin, L.[Liang],
Component Divide-and-conquer for Real-world Image Super-resolution,
ECCV20(VIII:101-117).
Springer DOI 2011
BibRef

Xu, X.Q.[Xiao-Qian], Wei, P.X.[Peng-Xu], Chen, W.[Weikai], Liu, Y.[Yang], Mao, M.Z.[Ming-Zhi], Lin, L.[Liang], Li, G.B.[Guan-Bin],
Dual Adversarial Adaptation for Cross-Device Real-World Image Super-Resolution,
CVPR22(5657-5666)
IEEE DOI 2210
Training, Degradation, Adaptation models, Superresolution, Cameras, Feature extraction, Low-level vision BibRef

Xiang, X.J.[Xi-Jie], Zhu, L.[Lin], Li, J.N.[Jia-Ning], Wang, Y.X.[Yi-Xuan], Huang, T.J.[Tie-Jun], Tian, Y.H.[Yong-Hong],
Learning Super-Resolution Reconstruction for High Temporal Resolution Spike Stream,
CirSysVideo(33), No. 1, January 2023, pp. 16-29.
IEEE DOI 2301
Image reconstruction, Superresolution, Cameras, Streaming media, Spatial resolution, Brightness, Spatiotemporal phenomena, spike-based iterative projection BibRef

Huang, Y.F.[Yuan-Fei], Li, J.[Jie], Hu, Y.T.[Yan-Ting], Gao, X.B.[Xin-Bo], Huang, H.[Hua],
Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution,
PAMI(45), No. 5, May 2023, pp. 6495-6510.
IEEE DOI 2304
Degradation, Superresolution, Additives, Learning systems, Optimization, Kernel, Estimation, Blind super-resolution, degradation representation BibRef

Pal, D.[Debabrata], Bose, S.[Shirsha], More, D.[Deeptej], Jha, A.[Ankit], Banerjee, B.[Biplab], Jeppu, Y.[Yogananda],
MAML-SR: Self-adaptive super-resolution networks via multi-scale optimized attention-aware meta-learning,
PRL(173), 2023, pp. 101-107.
Elsevier DOI 2310
Image super-resolution, Meta-learning, Attention learning, Multi-scale optimization BibRef

Liu, Y.H.[Yi-Hao], Zhao, H.Y.[Heng-Yuan], Gu, J.J.[Jin-Jin], Qiao, Y.[Yu], Dong, C.[Chao],
Evaluating the Generalization Ability of Super-Resolution Networks,
PAMI(45), No. 12, December 2023, pp. 14497-14513.
IEEE DOI 2311
BibRef

Chen, X.Y.[Xiang-Yu], Wang, X.[Xintao], Zhou, J.T.[Jian-Tao], Qiao, Y.[Yu], Dong, C.[Chao],
Activating More Pixels in Image Super-Resolution Transformer,
CVPR23(22367-22377)
IEEE DOI 2309
BibRef

Yan, Q.S.[Qing-Sen], Niu, A.[Axi], Wang, C.Q.[Chao-Qun], Dong, W.[Wei], Wozniak, M.[Marcin], Zhang, Y.N.[Yan-Ning],
KGSR: A kernel guided network for real-world blind super-resolution,
PR(147), 2024, pp. 110095.
Elsevier DOI 2312
Blind super-resolution, Kernel estimation, Discriminator, Unsupervised learning, Non-ideal degradation BibRef

Liu, A.[Anqi], Li, S.[Sumei], Chang, Y.L.[Yong-Li], Hou, Y.H.[Yong-Hong],
Multi-Scale Visual Perception Based Progressive Feature Interaction Network for Stereo Image Super-Resolution,
CirSysVideo(34), No. 3, March 2024, pp. 1615-1626.
IEEE DOI 2403
Feature extraction, Image reconstruction, Superresolution, Transformers, Fuses, Visual perception, Streaming media, perceptual texture matching BibRef

Li, G.P.[Guo-Ping], Zhou, Z.[Zhenting], Wang, G.Z.[Guo-Zhong],
A joint image super-resolution network for multiple degradations removal via complementary transformer and convolutional neural network,
IET-IPR(18), No. 5, 2024, pp. 1344-1357.
DOI Link 2404
image enhancement, image resolution, image restoration BibRef

Liu, Q.G.[Qing-Guo], Gao, P.[Pan], Han, K.[Kang], Liu, N.Z.[Ning-Zhong], Xiang, W.[Wei],
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution,
MultMed(26), 2024, pp. 7516-7528.
IEEE DOI 2405
Degradation, Transformers, Feature extraction, Image reconstruction, Kernel, Self-supervised learning, contrastive learning BibRef

Jiang, J.Q.[Jia-Qin], Li, L.[Li], Tan, B.[Bin], Duan, L.[Lunhao], Yao, J.[Jian],
A multi-view references image super-resolution framework for generating the large-FOV and high-resolution image,
JVCIR(100), 2024, pp. 104123.
Elsevier DOI 2405
Large-FOV HR images, Reference-based super-resolution, Adaptive feature fusion, Structural information BibRef


Weng, S.Y.[Shao-Yu], Yuan, H.[Hsuan], Xu, Y.S.[Yu-Syuan], Huang, C.C.[Ching-Chun], Chiu, W.C.[Wei-Chen],
Best of Both Worlds: Learning Arbitrary-scale Blind Super-Resolution via Dual Degradation Representations and Cycle-Consistency,
WACV24(1536-1545)
IEEE DOI 2404
Degradation, Training, Scalability, Superresolution, Artificial neural networks, Task analysis, Algorithms, Visualization BibRef

Lee, R.[Royson], Li, R.[Rui], Venieris, S.[Stylianos], Hospedales, T.[Timothy], Huszár, F.[Ferenc], Lane, N.D.[Nicholas D.],
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation,
WACV24(1485-1494)
IEEE DOI 2404
Metalearning, Degradation, Adaptation models, Costs, Superresolution, Estimation, Generators, Algorithms, Low-level and physics-based vision BibRef

Berral-Soler, R.[Rafael], Madrid-Cuevas, F.J.[Francisco J.], Ventura, S.[Sebastián], Muñoz-Salinas, R.[Rafael], Marín-Jiménez, M.J.[Manuel J.],
A Comparison of Neural Network-based Super-resolution Models on 3d Rendered Images,
CAIP23(I:45-55).
Springer DOI 2312
BibRef

Yin, J.[Jun], Peng, S.[Shuang], Lin, J.[Jucai], Jiang, D.[Dong], Fang, C.[Cheng],
An Effective CNN-based Super Resolution Method for Video Coding,
CIAP23(I:124-134).
Springer DOI 2312
BibRef

Zhu, Q.[Qiang], Li, P.F.[Peng-Fei], Li, Q.H.[Qian-Hui],
Attention Retractable Frequency Fusion Transformer for Image Super Resolution,
NTIRE23(1756-1763)
IEEE DOI 2309
BibRef

Lee, S.[Sangyun], Ahn, S.[Sewoong], Yoon, K.[Kwangjin],
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution,
LLID22(88-100).
Springer DOI 2304
BibRef

Yao, G.Q.[Geng-Qi], Li, Z.[Zhan], Bhanu, B.[Bir], Kang, Z.Q.[Zhi-Qing], Zhong, Z.[Ziyi], Zhang, Q.F.[Qing-Feng],
MTKDSR: Multi-Teacher Knowledge Distillation for Super Resolution Image Reconstruction,
ICPR22(352-358)
IEEE DOI 2212
Knowledge engineering, Deep learning, Computational modeling, Superresolution, Neural networks, Computational efficiency BibRef

Fang, Z.X.[Zhen-Xuan], Dong, W.S.[Wei-Sheng], Li, X.[Xin], Wu, J.J.[Jin-Jian], Li, L.[Leida], Shi, G.M.[Guang-Ming],
Uncertainty Learning in Kernel Estimation for Multi-stage Blind Image Super-Resolution,
ECCV22(XVIII:144-161).
Springer DOI 2211
BibRef

Tang, C.Z.[Cheng-Zhou], Yang, Y.Q.[Yu-Qiang], Zeng, B.[Bing], Tan, P.[Ping], Liu, S.C.[Shuai-Cheng],
Learning to Zoom Inside Camera Imaging Pipeline,
CVPR22(17531-17540)
IEEE DOI 2210
Degradation, Design methodology, Pipelines, Superresolution, Subspace constraints, Signal processing, Cameras, Low-level vision, Image and video synthesis and generation BibRef

Oh, J.H.[Jung-Hun], Kim, H.[Heewon], Nah, S.[Seungjun], Hong, C.[Cheeun], Choi, J.H.[Jong-Hyun], Lee, K.M.[Kyoung Mu],
Attentive Fine-Grained Structured Sparsity for Image Restoration,
CVPR22(17652-17661)
IEEE DOI 2210
Photography, Computational modeling, Superresolution, Image restoration, Computational efficiency, Pattern recognition, Efficient learning and inferences BibRef

Romero, A.[Andrés], Van Gool, L.J.[Luc J.], Timofte, R.[Radu],
Unpaired Real-World Super-Resolution with Pseudo Controllable Restoration,
NTIRE22(797-806)
IEEE DOI 2210
Training, Degradation, Superresolution, Image restoration, Pattern recognition BibRef

Wang, K.[Kai], Sun, Q.G.[Qi-Gong], Wang, Y.C.[Yi-Cheng], Wei, H.Y.[Hui-Yuan], Lv, C.H.[Chong-Hua], Tian, X.L.[Xiao-Lin], Liu, X.[Xu],
CIPPSRNet: A Camera Internal Parameters Perception Network Based Contrastive Learning for Thermal Image Super-Resolution,
PBVS22(341-348)
IEEE DOI 2210
Training, Degradation, Visualization, Superresolution, Cameras, Robustness, Pattern recognition BibRef

Dong, X.Y.[Xiao-Yu], Xu, Q.Y.[Qing-Yu], Yang, J.G.[Jun-Gang], An, W.[Wei], Guo, Y.L.[Yu-Lan],
Unsupervised Degradation Representation Learning for Blind Super-Resolution,
CVPR21(10576-10585)
IEEE DOI 2111
Degradation, Estimation error, Codes, Superresolution, Data mining, Task analysis BibRef

Cheng, X.[Xi], Fu, Z.Y.[Zhen-Yong], Yang, J.[Jian],
Zero-shot Image Super-resolution with Depth Guided Internal Degradation Learning,
ECCV20(XVII:265-280).
Springer DOI 2011
BibRef

Chen, S.J.[Shuai-Jun], Han, Z.[Zhen], Dai, E.[Enyan], Jia, X.[Xu], Liu, Z.L.[Zi-Luan], Liu, X.[Xing], Zou, X.Y.[Xue-Yi], Xu, C.J.[Chun-Jing], Liu, J.Z.[Jian-Zhuang], Tian, Q.[Qi],
Unsupervised Image Super-Resolution with an Indirect Supervised Path,
NTIRE20(1924-1933)
IEEE DOI 2008
Image resolution, Degradation, Training, Task analysis, Pipelines, Image reconstruction, Machine learning BibRef

Wang, J.Q.[Jia-Qi], Chen, K.[Kai], Xu, R.[Rui], Liu, Z.W.[Zi-Wei], Loy, C.C.[Chen Change], Lin, D.[Dahua],
CARAFE: Content-Aware ReAssembly of FEatures,
ICCV19(3007-3016)
IEEE DOI 2004
Code, Convolutional Networks.
WWW Link. Feature upsampling. convolutional neural nets, image segmentation, interpolation, learning (artificial intelligence), object detection, CARAFE, Image segmentation BibRef

Richard, A., Cherabier, I., Oswald, M.R., Tsiminaki, V., Pollefeys, M., Schindler, K.,
Learned Multi-View Texture Super-Resolution,
3DV19(533-543)
IEEE DOI 1911
Computational modeling, Surface reconstruction, Geometry, Image reconstruction, Texture, Variational Methods BibRef

Aadil, M., Rahim, R., Hussain, S.U.,
Improving Super Resolution Methods Via Incremental Residual Learning,
ICIP19(2836-2840)
IEEE DOI 1910
Image Reconstruction, Convolutional Neural Networks, Residual Learning, Super Resolution BibRef

Wang, L., Qiu, L., Sui, W., Pan, C.,
Reconstructed Densenets for Image Super-Resolution,
ICIP18(3558-3562)
IEEE DOI 1809
Image reconstruction, Computational modeling, Image resolution, Convolution, Computational complexity, Training, Residual Learning BibRef

Hu, Y.Y.[Yue-Yu], Liu, J.Y.[Jia-Ying], Yang, W.H.[Wen-Han], Deng, S.H.[Shi-Hong], Zhang, L.Y.[Lu-Yao], Guo, Z.M.[Zong-Ming],
Real-time deep image super-resolution via global context aggregation and local queue jumping,
VCIP17(1-4)
IEEE DOI 1804
image resolution, learning (artificial intelligence), GLNet, deep learning, deep network, first-layer feature map, Real-Time Image Super-Resolution BibRef

Liu, H., Xiong, R., Song, Q., Wu, F., Gao, W.,
Image super-resolution based on adaptive joint distribution modeling,
VCIP17(1-4)
IEEE DOI 1804
gradient methods, image reconstruction, image resolution, learning (artificial intelligence), HR image reconstruction, non-local similarity BibRef

Tong, T.[Tong], Li, G.[Gen], Liu, X.J.[Xie-Jie], Gao, Q.Q.[Qin-Quan],
Image Super-Resolution Using Dense Skip Connections,
ICCV17(4809-4817)
IEEE DOI 1802
image reconstruction, image resolution, image sampling, learning (artificial intelligence), neural nets, Training BibRef

Liang, Y.D.[Yu-Dong], Wang, J.J.[Jin-Jun], Zhang, S.Z.[Shi-Zhou], Gong, Y.H.[Yi-Hong],
Incorporating image degeneration modeling with multitask learning for image super-resolution,
ICIP15(2110-2114)
IEEE DOI 1512
autoencoder, degeneration modeling, multitask learning, super-resolution BibRef

Kang, C.[Chulmoo], Hong, M.[Minui], Yoo, S.I.[Suk I.],
Learning Texture Image Prior for Super Resolution Using Restricted Boltzmann Machine,
CIAP15(I:215-224).
Springer DOI 1511
BibRef

Zuckerman, L.P.[Liad Pollak], Naor, E.[Eyal], Pisha, G.[George], Bagon, S.[Shai], Irani, M.[Michal],
Across Scales and Across Dimensions: Temporal Super-Resolution Using Deep Internal Learning,
ECCV20(VII:52-68).
Springer DOI 2011
BibRef

Shocher, A., Cohen, N., Irani, M.,
Zero-Shot Super-Resolution Using Deep Internal Learning,
CVPR18(3118-3126)
IEEE DOI 1812
Kernel, Training, Image resolution, Databases, Entropy, Data mining BibRef

Kim, C.H.[Chang-Hyun], Choi, K.[Kyuha], Lee, H.Y.[Ho-Young], Hwang, K.Y.[Kyu-Young], Ra, J.B.[Jong Beom],
Robust learning-based super-resolution,
ICIP10(2017-2020).
IEEE DOI 1009
BibRef

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


Last update:May 6, 2024 at 15:50:14