A Two-stage Shadow Removal Algorithm Based on Recurrent Attention Network

Abstract

The presence of shadows in an image obscures some information, hindering the subsequent image-processing task. So correct identification of shadow regions will greatly upgrade the performance of shadow removal. Obviously, the more accurate the identification of shadow regions, the better the shadow removal performance will be. Based on this, this paper designs a two-stage shadow removal algorithm (TS-RAN) based on the recurrent attention network, including the shadow detection stage and shadow removal stage. Firstly, a single shadow image is fed into the shadow detection stage, which generates a rough attention map under the joint action of the recurrent attention network and luminance prediction module. Secondly, the results generated in the previous stage are transferred to the shadow removal stage and the luminance estimation module for fine shadow removal. In addition, we designed a residual estimation module to remove possible artifacts caused by shadow residuals. Experiments show that our method achieves significant performance on the LRSS, ISTD, and WSRD datasets.