TSRFormer: Transformer Based Two-stage Refinement for Single Image Shadow Removal

Hua-En Chang, Chia-Hsuan Hsieh, Hao-Hsiang Yang, I-Hsiang Chen, Yi-Chung Chen, Yu-Chiang Frank Wang, Zhi-Kai Huang, Wei-Ting Chen, Sy-Yen Kuo

Abstract: Single-image shadow removal aims to remove undesired shadow information from captured images. With the development of deep convolutional neural networks, several methods have been proposed to achieve promising performance in shadow removal. However, they still struggle with limited performance due to the non-homogeneous intensity distribution of the shadow. To address this issue, we propose a two-stage shadow removal architecture based on the transformer called TSRFormer. The proposed architecture is divided into shadow removal and content refinement networks. These two stages adopt different transformer architectures and remove the shadow based on different information to achieve effective shadow removal. Experiments performed on challenging benchmark show that the proposed model achieves the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> highest SSIM in the NTIRE 2023 Image Shadow Removal Challenge. The source code will be public after the acceptance of this paper.