Recently, deep network-based image compressed sensing methods achieved high
reconstruction quality and reduced computational overhead compared with
traditional methods. However, existing methods obtain measurements only from
partial features in the network and use them only once for image
reconstruction. They ignore there are low, mid, and high-level features in the
network\cite{zeiler2014visualizing} and all of them are essential for
high-quality reconstruction. Moreover, using measurements only once may not be
enough for extracting richer information from measurements. To address these
issues, we propose a novel Measurements Reuse Convolutional Compressed Sensing
Network (MR-CCSNet) which employs Global Sensing Module (GSM) to collect all
level features for achieving an efficient sensing and Measurements Reuse Block
(MRB) to reuse measurements multiple times on multi-scale. Finally,
experimental results on three benchmark datasets show that our model can
significantly outperform state-of-the-art methods.

本研究介绍了一种新颖的测量重用卷积压缩感知网络 (MR-CCSNet)，它利用全局感知模块 (GSM) 收集所有级别的特征，使用多尺度上的多次测量重用块 (MRB) 来增强信息提取能力。实验结果表明，在三个基准数据集上，该模型大大优于现有的最先进方法。

全球感知与测量重用于图像压缩感知

Global Sensing and Measurements Reuse for Image Compressed Sensing

Deep network-based image Compressed Sensing (CS) has attracted much attention
in recent years. However, the existing deep network-based CS schemes either
reconstruct the target image in a block-by-block manner that leads to serious
block artifacts or train the deep network as a black box that brings about
limited insights of image prior knowledge. In this paper, a novel image CS
framework using non-local neural network (NL-CSNet) is proposed, which utilizes
the non-local self-similarity priors with deep network to improve the
reconstruction quality. In the proposed NL-CSNet, two non-local subnetworks are
constructed for utilizing the non-local self-similarity priors in the
measurement domain and the multi-scale feature domain respectively.
Specifically, in the subnetwork of measurement domain, the long-distance
dependencies between the measurements of different image blocks are established
for better initial reconstruction. Analogically, in the subnetwork of
multi-scale feature domain, the affinities between the dense feature
representations are explored in the multi-scale space for deep reconstruction.
Furthermore, a novel loss function is developed to enhance the coupling between
the non-local representations, which also enables an end-to-end training of
NL-CSNet. Extensive experiments manifest that NL-CSNet outperforms existing
state-of-the-art CS methods, while maintaining fast computational speed.

提出了一种使用非局部神经网络（NL-CSNet）的新型图像压缩感知（CS）框架，利用测量域和多尺度特征域中的非局部自相似先验知识，旨在改善重构质量。