论文标题
CNSNET：一个清洁效果的阴影网络，用于删除阴影
CNSNet: A Cleanness-Navigated-Shadow Network for Shadow Removal
论文作者
论文摘要
去除阴影的关键是在非阴影地区的指导下恢复了影子区域的内容。由于远程建模不足，基于CNN的方法无法彻底研究非阴影地区的信息。为了解决这个问题，我们提出了一个新颖的清洁效果图网络（CNSNET），并具有面向阴影的自适应标准化（SOAN）模块，并根据影子蒙版提供带有变压器（SAAT）模块的阴影吸引的聚合。在影子面罩的指导下，Soan模块制定了非阴影区域的统计数据，并将其适用于阴影区域以进行区域恢复。 SAAT模块利用阴影面具来精确指导每个阴影像素的修复，通过考虑来自无阴影区域的高度相关像素以进行全球像素恢复。在三个基准数据集（ISTD，ISTD+和SRD）上进行了广泛的实验表明，我们的方法可实现出色的脱落性能。
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.
