  Shadow removal is an important computer vision task aiming at the detection
and successful removal of the shadow produced by an occluded light source and a
photo-realistic restoration of the image contents. Decades of re-search
produced a multitude of hand-crafted restoration techniques and, more recently,
learned solutions from shad-owed and shadow-free training image pairs. In this
work,we propose an unsupervised single image shadow removal solution via
self-supervised learning by using a conditioned mask. In contrast to existing
literature, we do not require paired shadowed and shadow-free images, instead
we rely on self-supervision and jointly learn deep models to remove and add
shadows to images. We validate our approach on the recently introduced ISTD and
USR datasets. We largely improve quantitatively and qualitatively over the
compared methods and set a new state-of-the-art performance in single image
shadow removal.
