Compressed sensing (CS) is a signal processing framework for efficiently
reconstructing a signal from a small number of measurements, obtained by linear
projections of the signal. Block-based CS is a lightweight CS approach that is
mostly suitable for processing very high-dimensional images and videos: it
operates on local patches, employs a low-complexity reconstruction operator and
requires significantly less memory to store the sensing matrix. In this paper
we present a deep learning approach for block-based CS, in which a
fully-connected network performs both the block-based linear sensing and
non-linear reconstruction stages. During the training phase, the sensing matrix
and the non-linear reconstruction operator are \emph{jointly} optimized, and
the proposed approach outperforms state-of-the-art both in terms of
reconstruction quality and computation time. For example, at a 25% sensing rate
the average PSNR advantage is 0.77dB and computation time is over 200-times
faster.

本文提出了一种基于块的压缩感知深度学习算法，通过完全连接网络进行块状线性感知和非线性重建，优化了感知矩阵和非线性重构算子，且在重构质量和计算时间方面优于现有技术。