The challenges surrounding the application of image shadow removal to
real-world images and not just constrained datasets like ISTD/SRD have
highlighted an urgent need for zero-shot learning in this field. In this study,
we innovatively adapted the SAM (Segment anything model) for shadow removal by
introducing SAM-helps-Shadow, effectively integrating shadow detection and
removal into a single stage. Our approach utilized the model's detection
results as a potent prior for facilitating shadow detection, followed by shadow
removal using a second-order deep unfolding network. The source code of
SAM-helps-Shadow can be obtained from
this https URL

本文介绍了一种创新的方法 SAM-helps-Shadow，将分割模型 SAM 用于影子去除，利用零样本学习提高模型在实际场景中的适用性，使用二阶深度展开网络实现影子去除，提高了阴影检测的准确性和效率。

SAM 帮助 Shadow：当分割模型遇上阴影去除

SAM-helps-Shadow:When Segment Anything Model meet shadow removal

Recent deep learning methods have achieved promising results in image shadow
removal. However, most of the existing approaches focus on working locally
within shadow and non-shadow regions, resulting in severe artifacts around the
shadow boundaries as well as inconsistent illumination between shadow and
non-shadow regions. It is still challenging for the deep shadow removal model
to exploit the global contextual correlation between shadow and non-shadow
regions. In this work, we first propose a Retinex-based shadow model, from
which we derive a novel transformer-based network, dubbed ShandowFormer, to
exploit non-shadow regions to help shadow region restoration. A multi-scale
channel attention framework is employed to hierarchically capture the global
information. Based on that, we propose a Shadow-Interaction Module (SIM) with
Shadow-Interaction Attention (SIA) in the bottleneck stage to effectively model
the context correlation between shadow and non-shadow regions. We conduct
extensive experiments on three popular public datasets, including ISTD, ISTD+,
and SRD, to evaluate the proposed method. Our method achieves state-of-the-art
performance by using up to 150X fewer model parameters.

该研究提出了一种 Retinex-based 阴影模型和 ShadowFormer 转换器网络，通过多尺度通道的注意机制和 Shadow-Interaction Module 中的 Shadow-Interaction Attention 对阴影和非阴影区域之间的全局联系进行建模。经验证，该方法在三个常用的公共数据集中实现了最先进的性能。