Shadow removal is a challenging task as it requires us to recover common penumbra at the shadow boundary and trys not to alter the illumination of the non-shadow regions. In this work, we propose a novel strategy to achieve these goals based on Generative Adversarial Networks and design a dual-module architecture (DM-GAN) consisting of a mask generator, a matte generator, an umbra module, a penumbra module, and a boundary generator. The mask generator and matte generator first produce a shadow alpha mask and a shadow matte for input shadow image. Combined with this mask and matte, we employ the umbra module and penumbra module to generate an umbra removal image and a mask of the penumbra. Finally, we recover the penumbra based on the idea of image inpainting to obtain a boundary-smoothing result with less alteration at non-shadow regions. Besides, we construct a Low Error Shadow Dataset (LESD) with less error and more scenes to maintain the global illumination consistency between shadow and non-shadow regions. Extensive experiments demonstrate that our proposed DM-GAN can outperform other state-of-the-art methods.

阴影去除是一项具有挑战性的任务，因为它要求我们在阴影边界处恢复常见的半影，并尝试不改变非阴影区域的照明。在这项工作中，我们提出了一种基于生成对抗网络的新颖策略来实现这些目标，并设计了一种双模块架构（DM-GAN），该架构由遮罩生成器，遮罩生成器，本影模块，半影模块和边界生成器。遮罩生成器和遮罩生成器首先为输入的阴影图像生成阴影alpha遮罩和阴影遮罩。结合使用此蒙版和遮罩，我们使用本影模块和半影模块来生成本影去除图像和半影的蒙版。最后，我们基于图像修复的思想恢复了半影，以获得边界平滑的结果，在非阴影区域的变化较小。除了，我们构建了一个具有更少错误和更多场景的低错误阴影数据集（LESD），以保持阴影和非阴影区域之间的全局照明一致性。大量实验表明，我们提出的DM-GAN可以胜过其他最新技术。