Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos

• URL: http://arxiv.org/abs/2311.13134v1
• Date: Wed, 22 Nov 2023 03:41:13 GMT
• Title: Lightweight High-Speed Photography Built on Coded Exposure and Implicit Neural Representation of Videos
• Authors: Zhihong Zhang, Runzhao Yang, Jinli Suo, Yuxiao Cheng, Qionghai Dai
• Abstract summary: coded exposure setup to encode a frame sequence into a blurry snapshot can serve as a lightweight solution. However, restoring motion from blur is quite challenging due to the high ill-posedness of motion blur decomposition. We develop a novel self-recursive neural network to sequentially retrieve the latent video sequence from the blurry image.
• Score: 36.64080221546024
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The compact cameras recording high-speed scenes with high resolution are highly demanded, but the required high bandwidth often leads to bulky, heavy systems, which limits their applications on low-capacity platforms. Adopting a coded exposure setup to encode a frame sequence into a blurry snapshot and retrieve the latent sharp video afterward can serve as a lightweight solution. However, restoring motion from blur is quite challenging due to the high ill-posedness of motion blur decomposition, intrinsic ambiguity in motion direction, and diverse motions in natural videos. In this work, by leveraging classical coded exposure imaging technique and emerging implicit neural representation for videos, we tactfully embed the motion direction cues into the blurry image during the imaging process and develop a novel self-recursive neural network to sequentially retrieve the latent video sequence from the blurry image utilizing the embedded motion direction cues. To validate the effectiveness and efficiency of the proposed framework, we conduct extensive experiments on benchmark datasets and real-captured blurry images. The results demonstrate that our proposed framework significantly outperforms existing methods in quality and flexibility. The code for our work is available at https://github.com/zhihongz/BDINR

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