CANet: A Context-Aware Network for Shadow Removal

Zipei Chen, Chengjiang Long, Ling Zhang, Chunxia Xiao; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 4743-4752

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 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 experiment results strongly demonstrate the efficacy of our proposed CANet and exhibit superior performance to state-of-the-arts.