In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former unifies these steps into one, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection. Thus, SpA-former is adaptable to real image de-shadowing for shadows projected on different semantic regions. SpA-Former consists of transformer layer and a series of joint Fourier transform residual blocks and two-wheel joint spatial attention. The network in this paper is able to handle the task while achieving a very fast processing efficiency. Our code is relased on https://github.com/zhangbaijin/SpA-Former-shadow-removal
Cite
@article{arxiv.2206.10910,
title = {SpA-Former: Transformer image shadow detection and removal via spatial attention},
author = {Xiao Feng Zhang and Chao Chen Gu and Shan Ying Zhu},
journal= {arXiv preprint arXiv:2206.10910},
year = {2022}
}