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.

本文提出了一种使用生成对抗网络来探索残留图像和亮度估计的 RIS-GAN 框架，以粗到细的方式生成间接阴影去除图像，其通过三个鉴别器来检测负残留图像、阴影去除图像和反向亮度图的真假，实验表明该方法在阴影去除上表现优异而且无需特定的阴影感知组件。