S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal

arXiv:2404.12103v1 Announce Type: new
Abstract: 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 …

abstract arxiv contrast cs.cv cs.gr data database free images network paper reference shadow stage style supervision type wgan