CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal
This work addresses shadow removal in computer vision, which is an incremental improvement for image enhancement tasks.
The paper tackled the problem of shadow removal in images by proposing CNSNet, which uses a shadow mask to guide restoration from non-shadow regions, achieving superior performance on three benchmark datasets (ISTD, ISTD+, and SRD).
The key to shadow removal is recovering the contents of the shadow regions with the guidance of the non-shadow regions. Due to the inadequate long-range modeling, the CNN-based approaches cannot thoroughly investigate the information from the non-shadow regions. To solve this problem, we propose a novel cleanness-navigated-shadow network (CNSNet), with a shadow-oriented adaptive normalization (SOAN) module and a shadow-aware aggregation with transformer (SAAT) module based on the shadow mask. Under the guidance of the shadow mask, the SOAN module formulates the statistics from the non-shadow region and adaptively applies them to the shadow region for region-wise restoration. The SAAT module utilizes the shadow mask to precisely guide the restoration of each shadowed pixel by considering the highly relevant pixels from the shadow-free regions for global pixel-wise restoration. Extensive experiments on three benchmark datasets (ISTD, ISTD+, and SRD) show that our method achieves superior de-shadowing performance.