  Existing deep learning-based shadow removal methods still produce images with
shadow remnants. These shadow remnants typically exist in homogeneous regions
with low-intensity values, making them untraceable in the existing
image-to-image mapping paradigm. We observe that shadows mainly degrade images
at the image-structure level (in which humans perceive object shapes and
continuous colors). Hence, in this paper, we propose to remove shadows at the
image structure level. Based on this idea, we propose a novel
structure-informed shadow removal network (StructNet) to leverage the
image-structure information to address the shadow remnant problem.
Specifically, StructNet first reconstructs the structure information of the
input image without shadows and then uses the restored shadow-free structure
prior to guiding the image-level shadow removal. StructNet contains two main
novel modules: (1) a mask-guided shadow-free extraction (MSFE) module to
extract image structural features in a non-shadow-to-shadow directional manner,
and (2) a multi-scale feature & residual aggregation (MFRA) module to leverage
the shadow-free structure information to regularize feature consistency. In
addition, we also propose to extend StructNet to exploit multi-level structure
information (MStructNet), to further boost the shadow removal performance with
minimum computational overheads. Extensive experiments on three shadow removal
benchmarks demonstrate that our method outperforms existing shadow removal
methods, and our StructNet can be integrated with existing methods to improve
them further.
