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Degradation, Training, Kernel, Superresolution, Sun, Learning systems,
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Convolution, Design methodology, Superresolution, Stacking,
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Superresolution, Feature extraction, Degradation, Task analysis,
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Image super-resolution, Convolutional neural network,
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Single Image Super-Resolution (SISR), Recursive networks,
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Single image super-resolution, Lightweight network,
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image reconstruction, image resolution, image restoration
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BSRAW: Improving Blind RAW Image Super-Resolution,
WACV24(8485-8495)
IEEE DOI
2404
Degradation, Training, Superresolution, Pipelines, Transforms, Cameras,
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ICIP23(1955-1959)
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NTIRE23(1692-1701)
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CVPR23(22378-22387)
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NTIRE23(1712-1721)
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NTIRE23(1746-1755)
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Gendy, G.[Garas],
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NTIRE23(1593-1602)
IEEE DOI
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NTIRE23(1484-1494)
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Dong, T.X.T.[Ting-Xing Tim],
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RenderSR: A Lightweight Super-Resolution Model for Mobile Gaming
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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],
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NTIRE22(1102-1111)
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Codes, Superresolution, Memory management, Transformers, Feature extraction
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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],
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ICIP20(573-577)
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Pattern recognition, Europe, Indexes,
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ICIP19(2826-2830)
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Super-resolution reconstruction, grouped convolution,
lightweight construction, residual
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Ahn, N.[Namhyuk],
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Image Super-Resolution via Progressive Cascading Residual Network,
Restoration18(904-9048)
IEEE DOI
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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 .