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

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...

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Bibliographic Details
Published in:arXiv.org 2022-10
Main Authors: Xiao Feng Zhang, Chao Chen Gu, Zhu, Shan Ying
Format: Article
Language:English
Subjects:
Fourier series
Fourier transforms
Shadows
Online Access:Get full text
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Summary: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
ISSN:2331-8422