S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal
<|reference_start|>S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal: 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.<|reference_end|>
@article{kubiak2024s3r-net:,
    title={S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal},
    author={Nikolina Kubiak, Armin Mustafa, Graeme Phillipson, Stephen Jolly,
  Simon Hadfield},
    journal={arXiv preprint arXiv:2404.12103},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12103},
    primaryClass={cs.CV cs.GR}
}