Human-Aware Motion Deblurring

• URL: http://arxiv.org/abs/2001.06816v1
• Date: Sun, 19 Jan 2020 12:16:39 GMT
• Title: Human-Aware Motion Deblurring
• Authors: Ziyi Shen, Wenguan Wang, Xiankai Lu, Jianbing Shen, Haibin Ling, Tingfa Xu, and Ling Shao
• Abstract summary: This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG) The proposed model is based on a triple-branch encoder-decoder architecture. The proposed model is further endowed with a supervised, human-aware attention mechanism in an end-to-end fashion.
• Score: 197.53076361425363
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a human-aware deblurring model that disentangles the motion blur between foreground (FG) humans and background (BG). The proposed model is based on a triple-branch encoder-decoder architecture. The first two branches are learned for sharpening FG humans and BG details, respectively; while the third one produces global, harmonious results by comprehensively fusing multi-scale deblurring information from the two domains. The proposed model is further endowed with a supervised, human-aware attention mechanism in an end-to-end fashion. It learns a soft mask that encodes FG human information and explicitly drives the FG/BG decoder-branches to focus on their specific domains. To further benefit the research towards Human-aware Image Deblurring, we introduce a large-scale dataset, named HIDE, which consists of 8,422 blurry and sharp image pairs with 65,784 densely annotated FG human bounding boxes. HIDE is specifically built to span a broad range of scenes, human object sizes, motion patterns, and background complexities. Extensive experiments on public benchmarks and our dataset demonstrate that our model performs favorably against the state-of-the-art motion deblurring methods, especially in capturing semantic details.

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