8 February 2017 Constrained low-rank gamut completion for robust illumination estimation
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Illumination estimation is an important component of color constancy and automatic white balancing. According to recent survey and evaluation work, the supervised methods with a learning phase are competitive for illumination estimation. However, the robustness and performance of any supervised algorithm suffer from an incomplete gamut in training image sets because of limited reflectance surfaces in a scene. In order to address this problem, we present a constrained low-rank gamut completion algorithm, which can replenish gamut from limited surfaces in an image, for robust illumination estimation. In the proposed algorithm, we first discuss why the gamut completion is actually a low-rank matrix completion problem. Then a constrained low-rank matrix completion framework is proposed by adding illumination similarities among the training images as an additional constraint. An optimization algorithm is also given out by extending the augmented Lagrange multipliers. Finally, the completed gamut based on the proposed algorithm is fed into the support vector regression (SVR)-based illumination estimation method to evaluate the effect of gamut completion. The experimental results on both synthetic and real-world image sets show that the proposed gamut completion model not only can effectively improve the performance of the original SVR method but is also robust to the surface insufficiency in training samples.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Jianshen Zhou, Jianshen Zhou, Jiazheng Yuan, Jiazheng Yuan, Hongzhe Liu, Hongzhe Liu, } "Constrained low-rank gamut completion for robust illumination estimation," Optical Engineering 56(2), 023102 (8 February 2017). https://doi.org/10.1117/1.OE.56.2.023102 . Submission: Received: 12 July 2016; Accepted: 5 January 2017
Received: 12 July 2016; Accepted: 5 January 2017; Published: 8 February 2017


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