Removing shadows requires an understanding of both lighting conditions and object textures in a scene. Existing methods typically learn pixel-level color mappings between shadow and non-shadow images, in which the joint modeling of lighting and object textures is implicit and inadequate. We observe that in a shadow region, the degradation degree of object textures depends on the local illumination, while simply enhancing the local illumination cannot fully recover the attenuated textures. Based on this observation, we propose to condition the restoration of attenuated textures on the corrected local lighting in the shadow region. Specifically, We first design a shadow-aware decomposition network to estimate the illumination and reflectance layers of shadow regions explicitly. We then propose a novel bilateral correction network to recast the lighting of shadow regions in the illumination layer via a novel local lighting correction module, and to restore the textures conditioned on the corrected illumination layer via a novel illumination-guided texture restoration module. We further annotate pixel-wise shadow masks for the public SRD dataset, which originally contains only image pairs. Experiments on three benchmarks show that our method outperforms existing state-of-the-art shadow removal methods.

翻译：去除阴影需要理解场景中的光照条件和物体纹理。现有方法通常学习阴影区域与无阴影图像之间的像素级颜色映射，其中光照与物体纹理的联合建模隐式且不充分。我们观察到，在阴影区域中，物体纹理的退化程度取决于局部光照，而单纯增强局部光照无法完全恢复衰减的纹理。基于这一观察，我们提出将衰减纹理的恢复条件化于阴影区域中校正后的局部光照。具体而言，我们首先设计一个阴影感知分解网络，显式估计阴影区域的照明层和反射层。随后提出一种新型双边校正网络，通过新颖的局部光照校正模块重构照明层中的阴影区域光照，并通过光照引导的纹理恢复模块基于校正后的照明层恢复纹理。我们还为公开SRD数据集逐像素标注了阴影掩码（该数据集原本仅包含图像对）。在三个基准数据集上的实验表明，我们的方法优于现有最先进的阴影去除方法。