Learning Event-Based Motion Deblurring

• URL: http://arxiv.org/abs/2004.05794v1
• Date: Mon, 13 Apr 2020 07:01:06 GMT
• Title: Learning Event-Based Motion Deblurring
• Authors: Zhe Jiang, Yu Zhang, Dongqing Zou, Jimmy Ren, Jiancheng Lv, Yebin Liu
• Abstract summary: Fast motion can be captured as events at high time rate for event-based cameras. We show how its optimization can be unfolded with a novel end-to-end deep architecture. The proposed approach achieves state-of-the-art reconstruction quality, and generalizes better to handling real-world motion blur.
• Score: 39.16921854492941
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recovering sharp video sequence from a motion-blurred image is highly ill-posed due to the significant loss of motion information in the blurring process. For event-based cameras, however, fast motion can be captured as events at high time rate, raising new opportunities to exploring effective solutions. In this paper, we start from a sequential formulation of event-based motion deblurring, then show how its optimization can be unfolded with a novel end-to-end deep architecture. The proposed architecture is a convolutional recurrent neural network that integrates visual and temporal knowledge of both global and local scales in principled manner. To further improve the reconstruction, we propose a differentiable directional event filtering module to effectively extract rich boundary prior from the stream of events. We conduct extensive experiments on the synthetic GoPro dataset and a large newly introduced dataset captured by a DAVIS240C camera. The proposed approach achieves state-of-the-art reconstruction quality, and generalizes better to handling real-world motion blur.

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