RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal

Authors

  • Ling Zhang Wuhan University of Science and Technology
  • Chengjiang Long Kitware Inc.
  • Xiaolong Zhang Wuhan University of Science and Technology
  • Chunxia Xiao Wuhan University

DOI:

https://doi.org/10.1609/aaai.v34i07.6979

Abstract

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.

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Published

2020-04-03

How to Cite

Zhang, L., Long, C., Zhang, X., & Xiao, C. (2020). RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 12829-12836. https://doi.org/10.1609/aaai.v34i07.6979

Issue

Section

AAAI Technical Track: Vision