  In this work we present a deep learning framework for video compressive
sensing. The proposed formulation enables recovery of video frames in a few
seconds at significantly improved reconstruction quality compared to previous
approaches. Our investigation starts by learning a linear mapping between video
sequences and corresponding measured frames which turns out to provide
promising results. We then extend the linear formulation to deep
fully-connected networks and explore the performance gains using deeper
architectures. Our analysis is always driven by the applicability of the
proposed framework on existing compressive video architectures. Extensive
simulations on several video sequences document the superiority of our approach
both quantitatively and qualitatively. Finally, our analysis offers insights
into understanding how dataset sizes and number of layers affect reconstruction
performance while raising a few points for future investigation.
  Code is available at Github: https://github.com/miliadis/DeepVideoCS
