  In this paper, we propose a novel encoder-decoder neural network model
referred to as DeepBinaryMask for video compressive sensing. In video
compressive sensing one frame is acquired using a set of coded masks (sensing
matrix) from which a number of video frames is reconstructed, equal to the
number of coded masks. The proposed framework is an end-to-end model where the
sensing matrix is trained along with the video reconstruction. The encoder
learns the binary elements of the sensing matrix and the decoder is trained to
recover the unknown video sequence. The reconstruction performance is found to
improve when using the trained sensing mask from the network as compared to
other mask designs such as random, across a wide variety of compressive sensing
reconstruction algorithms. Finally, our analysis and discussion offers insights
into understanding the characteristics of the trained mask designs that lead to
the improved reconstruction quality.
