Exploiting Residual and Illumination with GANs for Shadow Detection and Shadow Removal
Ling Zhang, Chengjiang Long, Xiaolong Zhang, Chunxia Xiao
Abstract
Residual image and illumination estimation have been proven to be helpful for image enhancement. In this article, we propose a general framework, called RI-GAN, that exploits residual and illumination using generative adversarial networks (GANs). The proposed framework detects and removes shadows in a coarse-to-fine fashion. At the coarse stage, we employ three generators to produce a coarse shadow-removal result, a residual image, and an inverse illumination map. We also incorporate two indirect shadow-removal images via the residual image and the inverse illumination map. With the residual image, the illumination map, and the two indirect shadow-removal images as auxiliary information, the refinement stage estimates a shadow mask to identify shadow regions in the image, and then refines the coarse shadow-removal result to the fine shadow-free image. We introduce a cross-encoding module to the refinement generator, in which the use of feature-crossing can provide additional details to promote the shadow mask and the high-quality shadow-removal result. In addition, we apply data augmentation to the discriminator to reduce the dependence between representations of the discriminator and the quality of the predicted image. Experiments for shadow detection and shadow removal demonstrate that our method outperforms state-of-the-art methods. Furthermore, RI-GAN exhibits good performance in terms of image dehazing, rain removal, and highlight removal, demonstrating the effectiveness and flexibility of the proposed framework.