S3R-Net: A Single-Stage Approach to Self-Supervised Shadow Removal
Nikolina Kubiak Armin Mustafa Graeme Phillipson Stephen Jolly Simon Hadfield
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.