Denoising Reconstruction is a computational framework within reconstruction theory that specifically aims to recover an underlying true signal or structure by removing noise-induced distortions from observed, corrupted data.
319
Publications
29.5K
Citations
1K
Authors
465
Institutions
Publications per year
2017–2026
161
Parents
Publications
319
De-noising by soft-thresholding
David L. Donoho · IEEE Transactions on Information Theory · 1995 · 9.6K citations
Noise2Noise: Learning Image Restoration without Clean Data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren et al. · arXiv (Cornell University) · 2018 · 988 citations · Full text
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Authors
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- YC
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- AFAlessandro Foi
6 publications · 1.8K citations
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- KEKaren Egiazarian
6 publications · 1.8K citations
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- VKVladimir Katkovnik
5 publications · 1.8K citations
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- TW
4
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Institutions
465
Centre National de la Recherche Scientifique
Paris, France
12 h-index
31 publications
2.1K citations
Stanford University
Stanford, United States
11 h-index
26 publications
10.3K citations
Zhejiang University
Hangzhou, China
8 h-index
17 publications
634 citations
China University of Petroleum, Beijing
Beijing, China
7 h-index
23 publications
2.2K citations
Massachusetts Institute of Technology
Cambridge, United States
6 h-index
7 publications
1.7K citations
Tsinghua University
Beijing, China
5 h-index
11 publications
564 citations
Technion – Israel Institute of Technology
Haifa, Israel
5 h-index
7 publications
228 citations
Inserm
Paris, France
5 h-index
9 publications
779 citations
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Venues
- IEEE Transactions on Image Processing
12 publications · 3.9K citations
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- Signal Processing
7 publications · 428 citations
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- Biomedical Signal Processing and Control
7 publications · 280 citations
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- Sensors
7 publications · 310 citations
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- Geophysics
7 publications · 545 citations
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