  Shadow removal is a computer-vision task that aims to restore the image
content in shadow regions. While almost all recent shadow-removal methods
require shadow-free images for training, in ECCV 2020 Le and Samaras introduces
an innovative approach without this requirement by cropping patches with and
without shadows from shadow images as training samples. However, it is still
laborious and time-consuming to construct a large amount of such unpaired
patches. In this paper, we propose a new G2R-ShadowNet which leverages shadow
generation for weakly-supervised shadow removal by only using a set of shadow
images and their corresponding shadow masks for training. The proposed
G2R-ShadowNet consists of three sub-networks for shadow generation, shadow
removal and refinement, respectively and they are jointly trained in an
end-to-end fashion. In particular, the shadow generation sub-net stylises
non-shadow regions to be shadow ones, leading to paired data for training the
shadow-removal sub-net. Extensive experiments on the ISTD dataset and the Video
Shadow Removal dataset show that the proposed G2R-ShadowNet achieves
competitive performances against the current state of the arts and outperforms
Le and Samaras' patch-based shadow-removal method.
