Multi-illuminant estimation with conditional random fields

Shida Beigpour, Christian Riess, Joost Van De Weijer, Elli Angelopoulou

Research output: Contribution to journalArticleResearchpeer-review

62 Citations (Scopus)


Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach. © 1992-2012 IEEE.
Original languageEnglish
Article number6637091
Pages (from-to)83-96
JournalIEEE Transactions on Image Processing
Issue number1
Publication statusPublished - 1 Jan 2014


  • Color constancy
  • CRF
  • multi-illuminant


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