Deep Fully-Connected Networks for Video Compressive Sensing

Understand

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.