Wei, JinjiangLong, ChengjiangZou, HuaXiao, ChunxiaLee, Jehee and Theobalt, Christian and Wetzstein, Gordon2019-10-142019-10-1420191467-8659https://doi.org/10.1111/cgf.13845https://diglib.eg.org:443/handle/10.1111/cgf13845In this paper, we propose a two-stage top-down and bottom-up Generative Adversarial Networks (TBGANs) for shadow inpainting and removal which uses a novel top-down encoder and a bottom-up decoder with slice convolutions. These slice convolutions can effectively extract and restore the long-range spatial information for either down-sampling or up-sampling. Different from the previous shadow removal methods based on deep learning, we propose to inpaint shadow to handle the possible dark shadows to achieve a coarse shadow-removal image at the first stage, and then further recover the details and enhance the color and texture details with a non-local block to explore both local and global inter-dependencies of pixels at the second stage. With such a two-stage coarse-to-fine processing, the overall effect of shadow removal is greatly improved, and the effect of color retention in non-shaded areas is significant. By comparing with a variety of mainstream shadow removal methods, we demonstrate that our proposed method outperforms the state-of-the-art methods.Computing methodologiesShadow InpaintingShadow RemovalTopdownBottomupSlice ConvolutionNonlocal BlockGenerative Adversarial Networks10.1111/cgf.13845381-392