CANet: A Context-Aware Network for Shadow Removal

• Main Authors: Chen, Zipei, Long, Chengjiang, Zhang, Ling, Xiao, Chunxia
• Format: Conference Proceeding
• Language: English
• Subjects: Benchmark testing; Codes; Computer vision; Context-aware services; Image and video manipulation detection and integrity methods; Image reconstruction; Low-level and physics-based vision; Scene analysis and understanding; Task analysis
• ISSN: 2380-7504
• DOI: 10.1109/ICCV48922.2021.00470

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. Our source code is available at https://github.com/Zipei-Chen/CANet.