CANet: A Context-Aware Network for Shadow Removal

• URL: http://arxiv.org/abs/2108.09894v1
• Date: Mon, 23 Aug 2021 02:05:52 GMT
• Title: CANet: A Context-Aware Network for Shadow Removal
• Authors: Zipei Chen, Chengjiang Long, Ling Zhang, Chunxia Xiao
• Abstract summary: We propose a novel context-aware network named CANet for shadow removal. The contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces. We evaluate our proposed CANet on two benchmark datasets and some real-world shadow images with complex scenes.
• Score: 27.73332935893127
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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