  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
