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Illumination estimation via nonnegative matrix factorization

[+] Author Affiliations
Lilong Shi, Brian Funt

Simon Fraser University, School of Computing Science, Burnaby, British Columbia Canada V5A 1S6

Weihua Xiong

Omnivision Corporation California, 4275 Burton Drive, Santa Clara, California 95054

J. Electron. Imaging. 21(3), 033022 (Sep 12, 2012). doi:10.1117/1.JEI.21.3.033022
History: Received August 16, 2011; Revised June 19, 2012; Accepted July 19, 2012
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Abstract.  The problem of illumination estimation for color constancy and automatic white balancing of digital color imagery can be viewed as the separation of the image into illumination and reflectance components. We propose using nonnegative matrix factorization with sparseness constraints to separate these components. Since illumination and reflectance are combined multiplicatively, the first step is to move to the logarithm domain so that the components are additive. The image data is then organized as a matrix to be factored into nonnegative components. Sparseness constraints imposed on the resulting factors help distinguish illumination from reflectance. The proposed approach provides a pixel-wise estimate of the illumination chromaticity throughout the entire image. This approach and its variations can also be used to provide an estimate of the overall scene illumination chromaticity.

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Citation

Lilong Shi ; Brian Funt and Weihua Xiong
"Illumination estimation via nonnegative matrix factorization", J. Electron. Imaging. 21(3), 033022 (Sep 12, 2012). ; http://dx.doi.org/10.1117/1.JEI.21.3.033022


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