Sampling-Priors-Augmented Deep Unfolding Network for Robust Video
  Compressive Sensing
Yuhao Huang Gangrong Qu Youran Ge
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.