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

翻译：高速高分辨率场景的紧凑相机需求迫切，但所需的高带宽常导致系统笨重庞大，限制了其在低容量平台上的应用。采用编码曝光装置将帧序列编码为模糊快照，再重建潜在清晰视频的方法可提供轻量化解决方案。然而，由于运动模糊分解的高度病态性、运动方向的固有歧义性以及自然视频中运动的多样性，从模糊中恢复运动极具挑战。本研究通过融合经典编码曝光成像技术与新兴视频隐式神经表示，巧妙地在成像过程中将运动方向线索嵌入模糊图像，并开发了新型自递归神经网络，利用嵌入的运动方向线索从模糊图像中顺序恢复潜在视频序列。为验证所提框架的有效性与效率，我们在基准数据集和真实采集的模糊图像上进行了大量实验。结果表明，所提框架在质量和灵活性上显著优于现有方法。本工作代码详见 https://github.com/zhihongz/BDINR