Color constancy and color illusion perception are two phenomena occurring in
the human visual system, which can help us reveal unknown mechanisms of human
perception. For decades computer vision scientists have developed numerous
color constancy methods, which estimate the reflectance of the surface by
discounting the illuminant. However, color illusions have not been analyzed in
detail in the field of computational color constancy, which we find surprising
since the relationship they share is significant and may let us design more
robust systems. We argue that any model that can reproduce our sensation on
color illusions should also be able to provide pixel-wise estimates of the
light source. In other words, we suggest that the analysis of color illusions
helps us to improve the performance of the existing global color constancy
methods, and enable them to provide pixel-wise estimates for scenes illuminated
by multiple light sources. In this study, we share the outcomes of our
investigation in which we take several color constancy methods and modify them
to reproduce the behavior of the human visual system on color illusions. Also,
we show that parameters purely extracted from illusions are able to improve the
performance of color constancy methods. A noteworthy outcome is that our
strategy based on the investigation of color illusions outperforms the
state-of-the-art methods that are specifically designed to transform global
color constancy algorithms into multi-illuminant algorithms.

通过分析色彩视觉系统中的色彩恒常性和色彩幻觉知觉现象，我们修改了现有的色彩恒常性方法，使其能够更好地模拟人类视觉系统在色彩幻觉上的行为，并显示出从幻觉中提取的参数能够改善色彩恒常性方法的性能，从而超越现有的多光源算法设计。