Learning Parallax for Stereo Event-based Motion Deblurring

• URL: http://arxiv.org/abs/2309.09513v1
• Date: Mon, 18 Sep 2023 06:51:41 GMT
• Title: Learning Parallax for Stereo Event-based Motion Deblurring
• Authors: Mingyuan Lin, Chi Zhang, Chu He, Lei Yu
• Abstract summary: Existing approaches rely on the perfect pixel-wise alignment between intensity images and events, which is not always fulfilled in the real world. We propose a novel coarse-to-fine framework, named NETwork of Event-based motion Deblurring with STereo event and intensity cameras (St-EDNet) We build a new dataset with STereo Event and Intensity Cameras (StEIC), containing real-world events, intensity images, and dense disparity maps.
• Score: 8.201943408103995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the extremely low latency, events have been recently exploited to supplement lost information for motion deblurring. Existing approaches largely rely on the perfect pixel-wise alignment between intensity images and events, which is not always fulfilled in the real world. To tackle this problem, we propose a novel coarse-to-fine framework, named NETwork of Event-based motion Deblurring with STereo event and intensity cameras (St-EDNet), to recover high-quality images directly from the misaligned inputs, consisting of a single blurry image and the concurrent event streams. Specifically, the coarse spatial alignment of the blurry image and the event streams is first implemented with a cross-modal stereo matching module without the need for ground-truth depths. Then, a dual-feature embedding architecture is proposed to gradually build the fine bidirectional association of the coarsely aligned data and reconstruct the sequence of the latent sharp images. Furthermore, we build a new dataset with STereo Event and Intensity Cameras (StEIC), containing real-world events, intensity images, and dense disparity maps. Experiments on real-world datasets demonstrate the superiority of the proposed network over state-of-the-art methods.

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