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。

DeepBinaryMask: 学习二进制掩码进行视频压缩感知

DeepBinaryMask: Learning a Binary Mask for Video Compressive Sensing

In this work we present a deep learning framework for video compressive
sensing. The proposed formulation enables recovery of video frames in a few
seconds at significantly improved reconstruction quality compared to previous
approaches. Our investigation starts by learning a linear mapping between video
sequences and corresponding measured frames which turns out to provide
promising results. We then extend the linear formulation to deep
fully-connected networks and explore the performance gains using deeper
architectures. Our analysis is always driven by the applicability of the
proposed framework on existing compressive video architectures. Extensive
simulations on several video sequences document the superiority of our approach
both quantitatively and qualitatively. Finally, our analysis offers insights
into understanding how dataset sizes and number of layers affect reconstruction
performance while raising a few points for future investigation.
Code is available at Github: this https URL

本文提出了一种基于深度学习的视频压缩感知框架，通过学习视频序列和测量帧之间的线性映射或深度全连接网络，获取了比以往方法更好的重建质量和速度，在多个视频序列上的广泛模拟中明确了该方法的优越性能，并对数据集规模和层数对重建性能的影响提出了未来研究的几点思考。