CANet: A Context-Aware Network for Shadow Removal
This addresses the problem of improving image quality by removing shadows, which is important for computer vision applications, but it appears incremental as it builds on existing shadow removal methods.
The paper tackles shadow removal in images by proposing a two-stage context-aware network (CANet) that transfers contextual information from non-shadow to shadow regions, achieving superior performance on benchmark datasets and real-world images.
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.