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

27

Authors

141

  1. SL
    Sven Lončarić

    University of Zagreb, Croatia

    7 publications · 151 citations

    7

    h-index

  2. JN
    Jarno Nikkanen

    6 publications · 141 citations

    6

    h-index

  3. BF
    Brian Funt

    Simon Fraser University, Canada

    5 publications · 469 citations

    5

    h-index

  4. NB
    Nikola Banić

    University of Zagreb, Croatia

    5 publications · 113 citations

    5

    h-index

  5. YQ
    Yanlin Qian

    5 publications · 160 citations

    5

    h-index

1–5 of 141

Venues

  1. IEEE Transactions on Image Processing

    5 publications · 731 citations

    5

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 67 citations

    4

    h-index

  3. Journal of the Optical Society of America A

    3 publications · 571 citations

    3

    h-index

  4. IEEE Access

    2 publications · 40 citations

    2

    h-index

  5. 2

    h-index

1–5