Image shadow removal is a typical low-level vision problem, where the presence of shadows leads to abrupt changes in brightness in certain regions, affecting the accuracy of upstream tasks. Current shadow removal methods still face challenges such as residual boundary artifacts, and capturing feature information at shadow boundaries is crucial for removing shadows and eliminating residual boundary artifacts. Recently, Mamba has achieved remarkable success in computer vision by globally modeling long-sequence information with linear complexity. However, when applied to image shadow removal, the original Mamba scanning method overlooks the semantic continuity of shadow boundaries as well as the continuity of semantics within the same region. Based on the unique characteristics of shadow images, this paper proposes a novel selective scanning method called boundary-region selective scanning. This method scans boundary regions, shadow regions, and non-shadow regions independently, bringing pixels of the same region type closer together in the long sequence, especially focusing on the local information at the boundaries, which is crucial for shadow removal. This method combines with global scanning and channel scanning to jointly accomplish the shadow removal. We name our model ShadowMamba, the first Mamba-based model for shadow removal. Extensive experimental results show that our method outperforms current state-of-the-art models across most metrics on multiple datasets. The code for ShadowMamba is available at (Code will be released upon acceptance).

翻译：图像阴影去除是一个典型的低层视觉问题，其中阴影的存在导致某些区域亮度发生突变，影响上游任务的准确性。当前的阴影去除方法仍面临残留边界伪影等挑战，而捕捉阴影边界的特征信息对于去除阴影和消除残留边界伪影至关重要。近年来，Mamba通过以线性复杂度对长序列信息进行全局建模，在计算机视觉领域取得了显著成功。然而，当应用于图像阴影去除时，原始的Mamba扫描方法忽略了阴影边界的语义连续性以及同一区域内语义的连续性。基于阴影图像的独特特性，本文提出了一种新颖的选择性扫描方法，称为边界区域选择性扫描。该方法独立扫描边界区域、阴影区域和非阴影区域，使相同区域类型的像素在长序列中更紧密地聚集，尤其关注边界的局部信息，这对于阴影去除至关重要。该方法结合全局扫描和通道扫描共同完成阴影去除任务。我们将我们的模型命名为ShadowMamba，这是首个基于Mamba的阴影去除模型。大量实验结果表明，我们的方法在多个数据集上的大多数指标上均优于当前最先进的模型。ShadowMamba的代码可在（代码将在论文录用后发布）获取。