  Remote sensing shadow removal, which aims to recover contaminated surface
information, is tricky since shadows typically display overwhelmingly low
illumination intensities. In contrast, the infrared image is robust toward
significant light changes, providing visual clues complementary to the visible
image. Nevertheless, the existing methods ignore the collaboration between
heterogeneous modalities, leading to undesired quality degradation. To fill
this gap, we propose a weakly supervised shadow removal network with a
spherical feature space, dubbed S2-ShadowNet, to explore the best of both
worlds for visible and infrared modalities. Specifically, we employ a modal
translation (visible-to-infrared) model to learn the cross-domain mapping, thus
generating realistic infrared samples. Then, Swin Transformer is utilized to
extract strong representational visible/infrared features. Simultaneously, the
extracted features are mapped to the smooth spherical manifold, which
alleviates the domain shift through regularization. Well-designed similarity
loss and orthogonality loss are embedded into the spherical space, prompting
the separation of private visible/infrared features and the alignment of shared
visible/infrared features through constraints on both representation content
and orientation. Such a manner encourages implicit reciprocity between
modalities, thus providing a novel insight into shadow removal. Notably, ground
truth is not available in practice, thus S2-ShadowNet is trained by cropping
shadow and shadow-free patches from the shadow image itself, avoiding
stereotypical and strict pair data acquisition. More importantly, we contribute
a large-scale weakly supervised shadow removal benchmark, including 4000 shadow
images with corresponding shadow masks.
