Multi illumination color constancy based on multi-scale supervision and single-scale estimation cascade convolution neural network

Abstract

Color constancy methods are generally based on a simplifying assumption that the spectral distribution of a light source is uniform across scenes. However, in reality, this assumption is often violated because of the presence of multiple light sources, that is, more than two illuminations. In this paper, we propose a unique cascade network of deep multi-scale supervision and single-scale estimation (CN-DMS4) to estimate multi-illumination. The network parameters are supervised and learned from coarse to fine in the training process and estimate only the final thinnest level illumination map in the illumination estimation process. Furthermore, to reduce the influence of the color channel on the Euclidean distance or the pixel-level angle error, a new loss function with a channel penalty term is designed to optimize the network parameters. Extensive experiments are conducted on single and multi-illumination benchmark datasets. In comparison with previous multi-illumination estimation methods, our proposed method displays a partial improvement in terms of quantitative data and visual effect, which provides the future research direction in end-to-end multi-illumination estimation.