Single-image shadow removal using detail extraction and illumination estimation

Deep learning-based shadow removal methods are frequently hard to obtain a detail-rich and boundary-smoothing shadow removal result. In this work, we propose an illumination-sensitive filter and a multi-task generative adversarial networks architecture to tackle these problems. Firstly, we detect the shadow for the input shadow image and use the illumination-sensitive filter to extract the texture information for generating a coarse image with fewer texture details. Secondly, we conduct illumination estimation for this coarse shadow image to remove the shadow indirectly. Next, we restore the shadow boundary realistically inspired by the idea of image in painting. Finally, we recover the texture details for obtaining the final shadow removal result. Besides, we filter two large benchmark datasets, i.e., SRD and ISTD, to create a Low Error Synthesized Dataset (LESD). The extensive experiments demonstrate that our method can achieve superior performance to state of the arts.