Lightweight High-Speed Photography Built on Coded Exposure and Implicit
  Neural Representation of Videos
Zhihong Zhang Runzhao Yang Jinli Suo Yuxiao Cheng Qionghai Dai
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