  Residual images and illumination estimation have been proved very helpful in
image enhancement. In this paper, we propose a general and novel framework
RIS-GAN which explores residual and illumination with Generative Adversarial
Networks for shadow removal. Combined with the coarse shadow-removal image, the
estimated negative residual images and inverse illumination maps can be used to
generate indirect shadow-removal images to refine the coarse shadow-removal
result to the fine shadow-free image in a coarse-to-fine fashion. Three
discriminators are designed to distinguish whether the predicted negative
residual images, shadow-removal images, and the inverse illumination maps are
real or fake jointly compared with the corresponding ground-truth information.
To our best knowledge, we are the first one to explore residual and
illumination for shadow removal. We evaluate our proposed method on two
benchmark datasets, i.e., SRD and ISTD, and the extensive experiments
demonstrate that our proposed method achieves the superior performance to
state-of-the-arts, although we have no particular shadow-aware components
designed in our generators.
