DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing

You can find the paper here.

In this paper, we propose a novel encoder-decoder neural network model called DeepBinaryMask for video compressive
sensing. 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 reconstruct the video sequence.

The reconstruction performance is found to improve when using the trained sensing mask from the network 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.