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The assumption of a uniform light color distribution is no longer applicablein scenes that have multiple light colors. Most color constancy methods aredesigned to deal with a single light color, and thus are erroneous when appliedto multiple light colors. The spatial variability in multiple light colorscauses the color constancy problem to be more challenging and requires theextraction of local surface/light information. Motivated by this, we introducea multi-task learning method to discount multiple light colors in a singleinput image. To have better cues of the local surface/light colors undermultiple light color conditions, we design a novel multi-task learningframework. Our framework includes auxiliary tasks of achromatic-pixel detectionand surface-color similarity prediction, providing better cues for local lightand surface colors, respectively. Moreover, to ensure that our model maintainsthe constancy of surface colors regardless of the variations of light colors, anovel local surface color feature preservation scheme is developed. Wedemonstrate that our model achieves 47.1% improvement (from 4.69 mean angularerror to 2.48) compared to a state-of-the-art multi-illuminant color constancymethod on a multi-illuminant dataset (LSMI).

MIMT: Multi-Illuminant Color Constancy via Multi-Task Local Surface and Light Color Learning