  We propose a novel Retinex image-decomposition network that can be trained in
a self-supervised manner. The Retinex image-decomposition aims to decompose an
image into illumination-invariant and illumination-variant components, referred
to as "reflectance" and "shading," respectively. Although there are three
consistencies that the reflectance and shading should satisfy, most
conventional work considers only one or two of the consistencies. For this
reason, the three consistencies are considered in the proposed network. In
addition, by using generated pseudo-images for training, the proposed network
can be trained with self-supervised learning. Experimental results show that
our network can decompose images into reflectance and shading components.
Furthermore, it is shown that the proposed network can be used for
white-balance adjustment.
