  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.
