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.

本文提出了一种名为 DeepBinaryMask 的新型编码器 - 解码器神经网络模型，用于视频压缩感知。该模型通过训练感知矩阵以及视频重建来提高重建性能，并通过分析训练出的感知矩阵的特性来解释性能提升的原因。