Video Compressed Sensing (VCS) aims to reconstruct multiple frames from one single captured measurement, thus achieving high-speed scene recording with a low-frame-rate sensor. Although there have been impressive advances in VCS recently, those state-of-the-art (SOTA) methods also significantly increase model complexity and suffer from poor generality and robustness, which means that those networks need to be retrained to accommodate the new system. Such limitations hinder the real-time imaging and practical deployment of models. In this work, we propose a Sampling-Priors-Augmented Deep Unfolding Network (SPA-DUN) for efficient and robust VCS reconstruction. Under the optimization-inspired deep unfolding framework, a lightweight and efficient U-net is exploited to downsize the model while improving overall performance. Moreover, the prior knowledge from the sampling model is utilized to dynamically modulate the network features to enable single SPA-DUN to handle arbitrary sampling settings, augmenting interpretability and generality. Extensive experiments on both simulation and real datasets demonstrate that SPA-DUN is not only applicable for various sampling settings with one single model but also achieves SOTA performance with incredible efficiency.

翻译：视频压缩感知旨在从单次捕获的测量值中重建多帧图像，从而利用低帧率传感器实现高速场景记录。尽管近年来视频压缩感知领域取得了显著进展，但这些最先进方法也显著增加了模型复杂度，并存在泛化性和鲁棒性差的问题，即网络需要重新训练以适应新系统。这些局限性阻碍了模型的实时成像和实际部署。本文提出一种采样先验增强的深度展开网络，用于高效且鲁棒的VCS重建。在优化启发的深度展开框架下，采用轻量高效的U-net结构来缩减模型规模同时提升整体性能。此外，利用采样模型的先验知识动态调制网络特征，使单一SPA-DUN模型能够处理任意采样设置，增强可解释性与泛化能力。在仿真和真实数据集上的大量实验表明，SPA-DUN不仅能够以单一模型适用于多种采样设置，还能以惊人的效率实现最先进的性能。