Unsupervised shadow removal aims to learn a non-linear function to map the original image from shadow domain to non-shadow domain in the absence of paired shadow and non-shadow data. In this paper, we develop a simple yet efficient target-consistency generative adversarial network (TC-GAN) for the shadow removal task in the unsupervised manner. Compared with the bidirectional mapping in cycle-consistency GAN based methods for shadow removal, TC-GAN tries to learn a one-sided mapping to cast shadow images into shadow-free ones. With the proposed target-consistency constraint, the correlations between shadow images and the output shadow-free image are strictly confined. Extensive comparison experiments results show that TC-GAN outperforms the state-of-the-art unsupervised shadow removal methods by 14.9% in terms of FID and 31.5% in terms of KID. It is rather remarkable that TC-GAN achieves comparable performance with supervised shadow removal methods.

翻译：无人监督的阴影清除旨在学习一个非线性功能,在没有对影和非阴影数据的情况下,将原始图像从阴影域映射到非阴影域。 在本文中,我们开发了一个简单而有效的目标一致基因对抗网络(TC-GAN),以不受监督的方式执行阴影清除任务。与以循环一致的GAN法为基础的双向绘图相比,TC-GAN试图学习一面映射,将影子图像投向无阴影域。由于拟议的目标一致性限制,阴影图像与输出无阴影图像之间的关系受到严格限制。广泛的比较实验结果显示,TC-GAN在FID方面超越了14.9%的状态,在KID方面超过了31.5%的状态不受监督的影子清除方法。非常值得注意的是,TC-GAN在监督的影子清除方法下取得了可比较的业绩。