用软掩码更精准地去除阴影边界伪影，提升真实感。
SoftShadow: Leveraging Soft Masks for Penumbra-Aware Shadow Removal
- 引入物理启发的软掩码，替代传统二值掩码。
- 在多个数据集上实现最优去阴影效果，边界恢复更自然。
- 适合需要高保真阴影处理的图像修复与增强任务。
深度学习在图像去阴影任务中取得显著进展，但现有方法多依赖预生成的二值阴影掩码，易在阴影与非阴影交界处产生伪影。受阴影形成物理模型启发，本文提出SoftShadow框架，设计专用于去阴影的软掩码。该框架结合预训练SAM模型的先验知识与物理约束，通过联合优化SAM与后续去阴影网络，引入半影形成损失、掩码重建损失及去阴影损失。该方法能准确预测半影（部分遮蔽）和全影（完全遮蔽）区域，并实现端到端去阴影。在多个主流数据集上的大量实验表明，基于软掩码的SoftShadow框架能更好恢复边界伪影，达到当前最优性能，并展现更强泛化能力。
原文摘要 · Abstract (English)
Recent advancements in deep learning have yielded promising results for the image shadow removal task. However, most existing methods rely on binary pre-generated shadow masks. The binary nature of such masks could potentially lead to artifacts near the boundary between shadow and non-shadow areas. In view of this, inspired by the physical model of shadow formation, we introduce novel soft shadow masks specifically designed for shadow removal. To achieve such soft masks, we propose a SoftShadow framework by leveraging the prior knowledge of pretrained SAM and integrating physical constraints. Specifically, we jointly tune the SAM and the subsequent shadow removal network using penumbra formation constraint loss, mask reconstruction loss, and shadow removal loss. This framework enables accurate predictions of penumbra (partially shaded) and umbra (fully shaded) areas while simultaneously facilitating end-to-end shadow removal. Through extensive experiments on popular datasets, we found that our SoftShadow framework, which generates soft masks, can better restore boundary artifacts, achieve state-of-the-art performance, and demonstrate superior generalizability.
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