Learning-based Color Constancy
Learning-Based Color Constancy is a computational approach to color constancy that, as a subset of data-driven methods, employs machine learning models trained on labeled image data to estimate and correct scene illumination by leveraging statistical regularities in visual inputs rather than relying solely on predefined physical or geometric assumptions.
67
Publications
5.1K
Citations
141
Authors
72
Institutions
Publications per year
2017–2026
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67
A novel algorithm for color constancy
David Forsyth · International Journal of Computer Vision · 1990 · 724 citations
Jonathan T. Barron · 2015 · 224 citations
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6 publications · 141 citations
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5 publications · 160 citations
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Simon Fraser University
Burnaby, Canada
8 h-index
16 publications
1.4K citations

University of Zagreb
Zagreb, Croatia
7 h-index
18 publications
389 citations

Tampere University
Tampere, Finland
5 h-index
13 publications
335 citations

University of Amsterdam
Amsterdam, The Netherlands
4 h-index
9 publications
1.5K citations

University of Electronic Science and Technology of China
Chengdu, China
2 h-index
7 publications
279 citations

Czech Technical University in Prague
Prague, Czechia
2 h-index
2 publications
40 citations
University of Tennessee at Knoxville
Knoxville, United States
2 h-index
4 publications
377 citations
Kyungpook National University
Daegu, South Korea
2 h-index
4 publications
62 citations
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5 publications · 731 citations
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Repository · 4 publications · 67 citations
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3 publications · 571 citations
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2 publications · 40 citations
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2 publications · 49 citations
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