  This paper focuses on the limitations of current over-parameterized shadow
removal models. We present a novel lightweight deep neural network that
processes shadow images in the LAB color space. The proposed network termed
"LAB-Net", is motivated by the following three observations: First, the LAB
color space can well separate the luminance information and color properties.
Second, sequentially-stacked convolutional layers fail to take full use of
features from different receptive fields. Third, non-shadow regions are
important prior knowledge to diminish the drastic color difference between
shadow and non-shadow regions. Consequently, we design our LAB-Net by involving
a two-branch structure: L and AB branches. Thus the shadow-related luminance
information can well be processed in the L branch, while the color property is
well retained in the AB branch. In addition, each branch is composed of several
Basic Blocks, local spatial attention modules (LSA), and convolutional filters.
Each Basic Block consists of multiple parallelized dilated convolutions of
divergent dilation rates to receive different receptive fields that are
operated with distinct network widths to save model parameters and
computational costs. Then, an enhanced channel attention module (ECA) is
constructed to aggregate features from different receptive fields for better
shadow removal. Finally, the LSA modules are further developed to fully use the
prior information in non-shadow regions to cleanse the shadow regions. We
perform extensive experiments on the both ISTD and SRD datasets. Experimental
results show that our LAB-Net well outperforms state-of-the-art methods. Also,
our model's parameters and computational costs are reduced by several orders of
magnitude. Our code is available at https://github.com/ngrxmu/LAB-Net.
