  In this paper, a deep neural network with interpretable motion compensation
called CS-MCNet is proposed to realize high-quality and real-time decoding of
video compressive sensing. Firstly, explicit multi-hypothesis motion
compensation is applied in our network to extract correlation information of
adjacent frames(as shown in Fig. 1), which improves the recover performance.
And then, a residual module further narrows down the gap between reconstruction
result and original signal. The overall architecture is interpretable by using
algorithm unrolling, which brings the benefits of being able to transfer prior
knowledge about the conventional algorithms. As a result, a PSNR of 22dB can be
achieved at 64x compression ratio, which is about 4% to 9% better than
state-of-the-art methods. In addition, due to the feed-forward architecture,
the reconstruction can be processed by our network in real time and up to three
orders of magnitude faster than traditional iterative methods.
