Abstract
Computational color constancy is an ill-posed problem since different combinations of objects and illumination may appear the same color in an image. Most methods produce a single illuminant estimate and cannot explain the potential ambiguity between possible illuminant solutions. In this work, a grouped convolutional color constancy (GC3) method is proposed. It approaches the computational color constancy as a grouped regression problem, dealing with ambiguity by generating multiple possible illuminant solutions. The GC3 consists of two components: fast feature extraction (FFE) and grouped illuminant regression (GIR). FFE uses small convolution kernels and rapid down-sampling scheme for quick feature extraction, while GIR employs grouped convolution layers for feature grouping regression, which estimates the illuminant solution and its probability for each group with two separate branches. The final illumination estimation is obtained by averaging all illuminant solutions, weighted by the normalized probability for each group. The proposed method yields competitive results on the ColorChecker dataset and achieves the state-of-the-art accuracy on the NUS and Cube+ datasets. Moreover, when compared with the state-of-the-art methods, our proposed approach exhibits superior time efficiency, making it the most efficient method available.
Published Version
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