论文标题
使用目标一致性生成对抗网络无监督的阴影去除
Unsupervised Shadow Removal Using Target Consistency Generative Adversarial Network
论文作者
论文摘要
无监督的阴影去除旨在学习非线性功能，以在没有配对的阴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，我们以无监督的方式为阴影去除任务开发了一个简单而有效的目标抗性生成对抗网络（TC-GAN）。与基于循环一致性GAN的阴影去除方法中的双向映射相比，TC-GAN试图学习一个单方面的映射，以将阴影图像投入到无阴影图像中。借助提出的目标一致性约束，严格限制了阴影图像与无阴影图像之间的相关性。广泛的比较实验结果表明，TC-GAN的表现优于最新的无监督影子去除方法，就FID而言，TC-GAN的表现为14.9％，而对于KID而言，TC-GAN的表现优于31.5％。 TC-GAN通过监督的阴影去除方法实现可比的性能是相当值得注意的。
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.
