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IEEE DOI
2102
Degradation, Training, Kernel, Superresolution, Sun, Learning systems,
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URNet: A U-Shaped Residual Network for Lightweight Image
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Elsevier DOI
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Super-resolution, Multi-scale, Attention mechanism, Lightweight
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IEEE DOI
2112
Superresolution, Cameras, Image resolution, Feature extraction,
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Explore Connection Pattern and Attention Mechanism for
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ICIP21(1799-1803)
IEEE DOI
2201
Convolution, Design methodology, Superresolution, Stacking,
Feature extraction, Convolutional neural networks,
Super-resolution
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Zhu, X.Y.[Xiang-Yuan],
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CirSysVideo(32), No. 3, March 2022, pp. 1273-1284.
IEEE DOI
2203
Superresolution, Feature extraction, Degradation, Task analysis,
Image reconstruction, Convolutional neural networks,
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CVPR23(22638-22647)
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2309
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Latticenet: Towards Lightweight Image Super-resolution with Lattice
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2011
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Xie, F.[Feng],
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Multi-scale convolutional attention network for lightweight image
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JVCIR(95), 2023, pp. 103889.
Elsevier DOI
2309
Image super-resolution, Convolutional neural network,
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Liu, F.Q.[Fei-Qiang],
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Elsevier DOI
2301
Single Image Super-Resolution (SISR), Recursive networks,
Multi-scale features, Progressive feature fusion
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2301
Single image super-resolution, Lightweight network,
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Efficient Multi-Scale Cosine Attention Transformer for Image
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IEEE DOI
2311
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MFFN: Multi-Path Feedback Fusion Network for Lightweight Image Super
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IET-IPR(17), No. 14, 2023, pp. 4190-4201.
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image reconstruction, image resolution, image restoration
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SP:IC(127), 2024, pp. 117148.
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2408
Super-resolution, Lightweight, Recursive gated convolution,
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Lightweight Prompt Learning Implicit Degradation Estimation Network
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IEEE DOI
2408
Degradation, Kernel, Wiener filters, Deconvolution, Superresolution,
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2312
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2409
Image super-resolution, Strip convolution, Attention mechanism,
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Conde, M.V.[Marcos V.],
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Timofte, R.[Radu],
BSRAW: Improving Blind RAW Image Super-Resolution,
WACV24(8485-8495)
IEEE DOI
2404
Degradation, Training, Superresolution, Pipelines, Transforms, Cameras,
Optics, Applications, Smartphones / end user devices,
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Sun, L.[Long],
Dong, J.X.[Jiang-Xin],
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2401
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ICCV23(12682-12691)
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ICIP23(1955-1959)
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2312
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Li, X.P.[Xin-Peng],
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DIPNet: Efficiency Distillation and Iterative Pruning for Image
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NTIRE23(1692-1701)
IEEE DOI
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IEEE DOI
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Mao, Y.[Yanyu],
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Wang, Q.[Qian],
Bai, B.[Bendu],
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NTIRE23(1522-1532)
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NTIRE23(1582-1592)
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CVPR23(2071-2081)
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Toward Stable, Interpretable, and Lightweight Hyperspectral
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CVPR23(22272-22281)
IEEE DOI
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Wang, X.H.[Xiao-Hang],
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CVPR23(1786-1795)
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Cao, J.Z.[Jie-Zhang],
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CiaoSR: Continuous Implicit Attention-in-Attention Network for
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IEEE DOI
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Wang, H.[Hang],
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CVPR23(22378-22387)
IEEE DOI
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Deng, W.J.[Wei-Jian],
Yuan, H.J.[Hong-Jie],
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NTIRE23(1712-1721)
IEEE DOI
2309
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Gankhuyag, G.[Ganzorig],
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Park, J.M.[Jin-Man],
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Min, K.[Kyoungwon],
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NTIRE23(1746-1755)
IEEE DOI
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Gendy, G.[Garas],
Sabor, N.[Nabil],
Hou, J.C.[Jing-Chao],
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Mixer-based Local Residual Network for Lightweight Image
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NTIRE23(1593-1602)
IEEE DOI
2309
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And:
A Simple Transformer-style Network for Lightweight Image
Super-resolution,
NTIRE23(1484-1494)
IEEE DOI
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Dong, T.X.T.[Ting-Xing Tim],
Yan, H.[Hao],
Parasar, M.[Mayank],
Krisch, R.[Raun],
RenderSR: A Lightweight Super-Resolution Model for Mobile Gaming
Upscaling,
MobileAI22(3086-3094)
IEEE DOI
2210
Image quality, Interpolation, Visualization, Image color analysis,
Superresolution, Buildings, Graphics processing units
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Fang, J.S.[Jin-Sheng],
Lin, H.J.[Han-Jiang],
Chen, X.Y.[Xin-Yu],
Zeng, K.[Kun],
A Hybrid Network of CNN and Transformer for Lightweight Image
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NTIRE22(1102-1111)
IEEE DOI
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Codes, Superresolution, Memory management, Transformers, Feature extraction
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Zong, Z.K.[Zhi-Kai],
Zha, L.[Lin],
Jiang, J.[Jiande],
Liu, X.X.[Xiao-Xiao],
Asymmetric Information Distillation Network for Lightweight Super
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NTIRE22(1248-1257)
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Multiplexing, Measurement, Image resolution,
Computational modeling, Feature extraction
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Li, W.[Wen],
Li, S.M.[Su-Mei],
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Lightweight Image Super-Resolution Reconstruction With Hierarchical
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ICIP20(573-577)
IEEE DOI
2011
Pattern recognition, Europe, Indexes,
Machine vision, Surface treatment, Super resolution,
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Shi, Y.L.[Yong-Lian],
Li, S.M.[Su-Mei],
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Fast and Lightweight Image Super-Resolution Based on Dense Residuals
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ICIP19(2826-2830)
IEEE DOI
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Super-resolution reconstruction, grouped convolution,
lightweight construction, residual
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Ahn, N.[Namhyuk],
Kang, B.[Byungkon],
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Springer DOI
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Image Super-Resolution via Progressive Cascading Residual Network,
Restoration18(904-9048)
IEEE DOI
1812
Training, Image resolution, Task analysis, Convolution,
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Chang, C.Y.[Chia-Yang],
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Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Challenges for Mosaic Generation, Super Resolution and Stabilization .