Deep learning-based shadow removal methods are frequently hard to obtain a detail-rich and boundary-smoothing shadow removal result. In this work, we propose an illumination-sensitive filter and a multi-task generative adversarial networks architecture to tackle these problems. Firstly, we detect the shadow for the input shadow image and use the illumination-sensitive filter to extract the texture information for generating a coarse image with fewer texture details. Secondly, we conduct illumination estimation for this coarse shadow image to remove the shadow indirectly. Next, we restore the shadow boundary realistically inspired by the idea of image in painting. Finally, we recover the texture details for obtaining the final shadow removal result. Besides, we filter two large benchmark datasets, i.e., SRD and ISTD, to create a Low Error Synthesized Dataset (LESD). The extensive experiments demonstrate that our method can achieve superior performance to state of the arts.

基于深度学习的阴影去除方法通常很难获得细节丰富且边界平滑的阴影去除结果。在这项工作中，我们提出了一个光照敏感的滤波器和一个多任务生成对抗网络架构来解决这些问题。首先，我们检测输入阴影图像的阴影，并使用光敏滤镜提取纹理信息以生成具有较少纹理细节的粗糙图像。其次，我们对该粗糙阴影图像进行照明估计，以间接去除阴影。接下来，我们将还原绘画中图像概念所启发的阴影边界。最后，我们恢复纹理细节以获得最终的阴影去除结果。此外，我们过滤了两个大型基准数据集SRD和ISTD，创建低错误综合数据集（LESD）。广泛的实验表明，我们的方法可以实现比现有技术更好的性能。