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
this https URL

通过利用经典的编码曝光成像技术和新兴的隐式神经表示视频技术，本研究工作巧妙地在成像过程中嵌入运动方向线索，并开发了一种新颖的自递归神经网络，通过利用嵌入的运动方向线索从模糊图像中顺序检索潜在的视频序列。通过在基准数据集和实际捕获的模糊图像上进行大量实验证实了我们提出的框架的有效性和效率，结果表明我们的方法在质量和灵活性上显著优于现有方法。

基于编码曝光和视频隐式神经表示的轻量级高速摄影

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

As a bio-inspired sensor with high temporal resolution, the spiking camera
has an enormous potential in real applications, especially for motion
estimation in high-speed scenes. However, frame-based and event-based methods
are not well suited to spike streams from the spiking camera due to the
different data modalities. To this end, we present, SCFlow, a tailored deep
learning pipeline to estimate optical flow in high-speed scenes from spike
streams. Importantly, a novel input representation is introduced which can
adaptively remove the motion blur in spike streams according to the prior
motion. Further, for training SCFlow, we synthesize two sets of optical flow
data for the spiking camera, SPIkingly Flying Things and Photo-realistic
High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to
random high-speed and well-designed scenes. Experimental results show that the
SCFlow can predict optical flow from spike streams in different high-speed
scenes. Moreover, SCFlow shows promising generalization on \textbf{real spike
streams}. Codes and datasets refer to
this https URL

介绍了一种基于深度学习的用于估计高速场景下 spiking 相机的光流的流水线，并引入了一种新的输入表示方法以自适应地去除 spike 流中的运动模糊。通过在名为 SPIFT 和 PHM 的场景中合成的光流数据进行训练，该方法显示出对不同高速场景下的 spiking 数据能够有效地预测结果。