In this paper we present S3R-Net, the Self-Supervised Shadow Removal Network. The two-branch WGAN model achieves self-supervision relying on the unify-and-adaptphenomenon - it unifies the style of the output data and infers its characteristics from a database of unaligned shadow-free reference images. This approach stands in contrast to the large body of supervised frameworks. S3R-Net also differentiates itself from the few existing self-supervised models operating in a cycle-consistent manner, as it is a non-cyclic, unidirectional solution. The proposed framework achieves comparable numerical scores to recent selfsupervised shadow removal models while exhibiting superior qualitative performance and keeping the computational cost low.

翻译：本文提出了S3R-Net自监督阴影去除网络。该双分支WGAN模型利用"统一与适应"现象实现自监督——统一输出数据的风格，并从无对齐无阴影参考图像数据库中推断其特征。该方法与大量监督学习框架形成鲜明对比。S3R-Net也区别于现有少数采用循环一致性运行的自监督模型，它是一种非循环的单向解决方案。该框架在保持低计算成本的同时，与近期自监督阴影去除模型相比取得了可比的数值指标，并展现出更优的定性性能。