  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.
