Image Compressed Sensing Using Non-local Neural Network
- URL: http://arxiv.org/abs/2112.03712v1
- Date: Tue, 7 Dec 2021 14:06:12 GMT
- Title: Image Compressed Sensing Using Non-local Neural Network
- Authors: Wenxue Cui, Shaohui Liu, Feng Jiang and Debin Zhao
- Abstract summary: In this paper, a novel image CS framework using non-local neural network (NL-CSNet) is proposed.
In the proposed NL-CSNet, two non-localworks are constructed for utilizing the non-local self-similarity priors.
In the subnetwork of multi-scale feature domain, the affinities between the dense feature representations are explored.
- Score: 43.51101614942895
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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