TY - JOUR AU - Zhang, Ling AU - Long, Chengjiang AU - Zhang, Xiaolong AU - Xiao, Chunxia PY - 2020/04/03 Y2 - 2024/03/29 TI - RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 07 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v34i07.6979 UR - https://ojs.aaai.org/index.php/AAAI/article/view/6979 SP - 12829-12836 AB - <p>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, <em>i.e.</em>, 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.</p> ER -