In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces. At Stage-I, we propose a contextual patch matching (CPM) module to generate a set of potential matching pairs of shadow and non-shadow patches. Combined with the potential contextual relationships between shadow and non-shadow regions, our well-designed contextual feature transfer (CFT) mechanism can transfer contextual information from non-shadow to shadow regions at different scales. With the reconstructed feature maps, we remove shadows at L and A/B channels separately. At Stage-II, we use an encoder-decoder to refine current results and generate the final shadow removal results. We evaluate our proposed CANet on two benchmark datasets and some real-world shadow images with complex scenes. Extensive experimental results strongly demonstrate the efficacy of our proposed CANet and exhibit superior performance to state-of-the-arts.
@article{arxiv.2108.09894,
title = {CANet: A Context-Aware Network for Shadow Removal},
author = {Zipei Chen and Chengjiang Long and Ling Zhang and Chunxia Xiao},
journal= {arXiv preprint arXiv:2108.09894},
year = {2021}
}
Comments
This paper was accepted to the IEEE International Conference on Computer Vision (ICCV), Montreal, Canada, Oct 11-17, 2021