  Shadow removal is still a challenging task due to its inherent
background-dependent and spatial-variant properties, leading to unknown and
diverse shadow patterns. Even powerful state-of-the-art deep neural networks
could hardly recover traceless shadow-removed background. This paper proposes a
new solution for this task by formulating it as an exposure fusion problem to
address the challenges. Intuitively, we can first estimate multiple
over-exposure images w.r.t. the input image to let the shadow regions in these
images have the same color with shadow-free areas in the input image. Then, we
fuse the original input with the over-exposure images to generate the final
shadow-free counterpart. Nevertheless, the spatial-variant property of the
shadow requires the fusion to be sufficiently `smart', that is, it should
automatically select proper over-exposure pixels from different images to make
the final output natural. To address this challenge, we propose the
shadow-aware FusionNet that takes the shadow image as input to generate fusion
weight maps across all the over-exposure images. Moreover, we propose the
boundary-aware RefineNet to eliminate the remaining shadow trace further. We
conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate
our method's effectiveness and show better performance in shadow regions and
comparable performance in non-shadow regions over the state-of-the-art methods.
We release the model and code in
https://github.com/tsingqguo/exposure-fusion-shadow-removal.
