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BibRef
Earlier:
BP06(17).
IEEE DOI
0609
BibRef
And:
Classifiers for Motion,
ICPR06(II: 593-596).
IEEE DOI
0609
BibRef
Dang, C.,
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SPLetters(21), No. 10, October 2014, pp. 1245-1249.
IEEE DOI
1407
Approximation methods
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Dang, C.[Chinh],
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Fast image super-resolution via selective manifold learning of
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ICIP15(1319-1323)
IEEE DOI
1512
Grassmann manifold distance
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Learning From Errors in Super-Resolution,
Cyber(44), No. 11, November 2014, pp. 2143-2154.
IEEE DOI
1411
image resolution
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Bi-dictionary learning model for medical image reconstruction from
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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
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Lee, J.W.[Jae-Won],
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1702
Compression noise
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JVCIR(48), No. 1, 2017, pp. 66-76.
Elsevier DOI
1708
Self-learning
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Lee, O.Y.[Oh-Young],
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Joint super-resolution and compression artifact reduction based on
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VCIP16(1-4)
IEEE DOI
1701
Hafnium
BibRef
Li, L.L.[Ling-Ling],
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Jiao, L.C.[Li-Cheng],
Liu, F.[Fang],
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Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution,
RS(11), No. 21, 2019, pp. xx-yy.
DOI Link
1911
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Zhang, L.[Lei],
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Springer DOI
2002
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Sun, W.,
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Learning to Zoom-In via Learning to Zoom-Out: Real-World
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IP(30), 2021, pp. 2947-2962.
IEEE DOI
2102
Degradation, Training, Kernel, Superresolution, Sun, Learning systems,
Cameras, Super resolution (SR), domain adaptation, unpaired learning
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Marivani, I.[Iman],
Tsiligianni, E.[Evaggelia],
Cornelis, B.[Bruno],
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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
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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
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Li, S.[Shang],
Zhang, G.[Guixuan],
Luo, Z.X.[Zheng-Xiong],
Liu, J.[Jie],
Zeng, Z.[Zhi],
Zhang, S.[Shuwu],
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
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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
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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],
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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
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Xiang, X.[Xijie],
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
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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.[Yuqiang],
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 .