  This paper addresses the real-time encoding-decoding problem for
high-frame-rate video compressive sensing (CS). Unlike prior works that perform
reconstruction using iterative optimization-based approaches, we propose a
non-iterative model, named "CSVideoNet". CSVideoNet directly learns the inverse
mapping of CS and reconstructs the original input in a single forward
propagation. To overcome the limitations of existing CS cameras, we propose a
multi-rate CNN and a synthesizing RNN to improve the trade-off between
compression ratio (CR) and spatial-temporal resolution of the reconstructed
videos. The experiment results demonstrate that CSVideoNet significantly
outperforms the state-of-the-art approaches. With no pre/post-processing, we
achieve 25dB PSNR recovery quality at 100x CR, with a frame rate of 125 fps on
a Titan X GPU. Due to the feedforward and high-data-concurrency natures of
CSVideoNet, it can take advantage of GPU acceleration to achieve three orders
of magnitude speed-up over conventional iterative-based approaches. We share
the source code at https://github.com/PSCLab-ASU/CSVideoNet.
