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In this paper, we propose an end-to-end SpA-Former to recover a shadow-freeimage from a single shaded image. Unlike traditional methods that require twosteps for shadow detection and then shadow removal, the SpA-Former unifiesthese steps into one, which is a one-stage network capable of directly learningthe mapping function between shadows and no shadows, it does not require aseparate shadow detection. Thus, SpA-former is adaptable to real imagede-shadowing for shadows projected on different semantic regions. SpA-Formerconsists of transformer layer and a series of joint Fourier transform residualblocks and two-wheel joint spatial attention. The network in this paper is ableto handle the task while achieving a very fast processing efficiency.  Our code is relased onhttps://github.com/zhangbaijin/SpA-Former-shadow-removal

SpA-Former: Transformer image shadow detection and removal via spatial attention