ShadowRefiner: Towards Mask-Free Shadow Removal via Fast Fourier Transformer

Abstract

Shadow-affected images often exhibit pronounced spatial discrepancies in color and illumination, consequently degrading various vision applications including object detection and segmentation systems. To effectively eliminate shadows in real-world images while preserving intricate details and producing visually compelling outcomes, we introduce a mask-free Shadow Removal and Refinement network (ShadowRefiner) via Fast Fourier Transformer. Specifically, the Shadow Removal module in our method aims to establish effective mappings between shadow-affected and shadow-free images via spatial and frequency representation learning. To mitigate the pixel misalignment and further improve the image quality, we propose a novel Fast-Fourier Attention based Transformer (FFAT) architecture, where an innovative attention mechanism is designed for meticulous refinement. Our method wins the championship in the Perceptual Track and achieves the second best performance in the Fidelity Track of NTIRE 2024 Image Shadow Removal Challenge. Besides, comprehensive experiment result also demonstrate the compelling effectiveness of our proposed method. The code is publicly available: https://github.com/movingforward100/Shadow_R.