Recasting Regional Lighting for Shadow Removal（重铸区域光照以实现阴影去除）
Recasting Regional Lighting for Shadow Removal
AI总结：
针对现有阴影去除方法对光照与纹理联合建模不足的问题，提出基于阴影感知分解与双边校正网络的方法，以校正光照为条件恢复纹理，在三个基准上性能超越现有SOTA，还为SRD数据集补充了像素级阴影掩膜。
摘要：
去除阴影需要同时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图像与非阴影图像之间的像素级颜色映射，这类方法对光照与物体纹理的联合建模是隐式且不充分的。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，而仅简单增强局部光照无法完全恢复被衰减的纹理。基于这一观察，我们提出将衰减纹理的恢复以阴影区域校正后的局部光照为条件。具体而言，我们首先设计了一个阴影感知分解网络（shadow-aware decomposition network），用于显式估计阴影区域的光照层和反射率层。随后我们提出了一种新颖的双边校正网络（bilateral correction network），通过新型局部光照校正模块重铸光照层中阴影区域的光照，并通过新型光照引导纹理恢复模块，以校正后的光照层为条件恢复纹理。我们还为原本仅包含图像对的公开SRD数据集标注了像素级阴影掩膜。在三个基准上的实验表明，我们的方法优于现有的最先进阴影去除方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
