  Most deep network methods for compressive sensing reconstruction suffer from
the black-box characteristic of DNN. In this paper, a deep neural network with
interpretable motion estimation named CSMCNet is proposed. The network is able
to realize high-quality reconstruction of video compressive sensing by
unfolding the iterative steps of optimization based algorithms. A DNN based,
multi-hypothesis motion estimation module is designed to improve the
reconstruction quality, and a residual module is employed to further narrow
down the gap between re-construction results and original signal in our
proposed method. Besides, we propose an interpolation module with corresponding
training strategy to realize scalable CS reconstruction, which is capable of
using the same model to decode various compression ratios. Experiments show
that a PSNR of 29.34dB can be achieved at 2% CS ratio (compressed by 98%),
which is superior than other state-of-the-art methods. Moreover, the
interpolation module is proved to be effective, with significant cost saving
and acceptable performance losses.
