Sampling-Priors-Augmented Deep Unfolding Network for Robust Video Compressive Sensing
This work addresses limitations in real-time imaging and practical deployment for VCS, offering a more robust and efficient solution, though it appears incremental as it builds on deep unfolding frameworks.
The paper tackles the problem of poor generality and robustness in Video Compressive Sensing (VCS) reconstruction, proposing SPA-DUN, which achieves state-of-the-art performance with high efficiency and handles arbitrary sampling settings using a single model.
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.