Super-resolution
Super-Resolution is a raster data processing technique that enhances the spatial resolution of geospatial imagery by inferring and reconstructing fine-grained details from coarse-grained input through learned or algorithmic models.
6.9K
Publications
563.9K
Citations
21.7K
Authors
3.5K
Institutions
Publications per year
2017–2026
4.5K
6.9K
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza et al. · Communications of the ACM · 2020 · 13.6K citations · Full text
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Christian Ledig, Lucas Theis, Ferenc Huszár et al. · 2017 · 12.3K citations
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Kai Zhang, Wangmeng Zuo, Yunjin Chen et al. · IEEE Transactions on Image Processing · 2017 · 8.8K citations · Full text
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University of Illinois Urbana-Champaign, United States
39 publications · 14.2K citations
31
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29
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28
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3.5K

Chinese Academy of Sciences
Beijing, China
67 h-index
959 publications
55.4K citations

Centre National de la Recherche Scientifique
Paris, France
67 h-index
596 publications
50K citations
Wuhan University
Wuhan, China
58 h-index
489 publications
38.5K citations
University of Chinese Academy of Sciences
Beijing, China
53 h-index
374 publications
27K citations

Xidian University
Xi'an, China
53 h-index
515 publications
44.3K citations
Tsinghua University
Beijing, China
51 h-index
456 publications
37.2K citations
Harvard University
Cambridge, United States
50 h-index
273 publications
67.1K citations
Nanyang Technological University
Singapore, Singapore
47 h-index
211 publications
19.6K citations
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212 publications · 39.6K citations
88
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131 publications · 14.5K citations
67
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169 publications · 13.6K citations
66
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Repository · 263 publications · 21.2K citations
58
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142 publications · 7.6K citations
52
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