Deep Compressive Sensing Image Reconstruction Network Based on Non-Local Prior

ZHONG Yuanhong; ZHOU Yujie; ZHANG Jing; ZHANG Chenxu

doi:10.11999/JEIT211506

Volume 45 Issue 2

Feb. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(2): 654-663

ZHONG Yuanhong, ZHOU Yujie, ZHANG Jing, ZHANG Chenxu. Deep Compressive Sensing Image Reconstruction Network Based on Non-Local Prior[J]. Journal of Electronics & Information Technology, 2023, 45(2): 654-663. doi: 10.11999/JEIT211506

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ZHONG Yuanhong, ZHOU Yujie, ZHANG Jing, ZHANG Chenxu. Deep Compressive Sensing Image Reconstruction Network Based on Non-Local Prior[J]. Journal of Electronics & Information Technology, 2023, 45(2): 654-663. doi: 10.11999/JEIT211506

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Deep Compressive Sensing Image Reconstruction Network Based on Non-Local Prior

doi: 10.11999/JEIT211506

School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China

Funds: The National Natural Science Foundation of China (61501069), The Technological Innovation and Application Development of Chongqing (cstc2019jscx-msxmX0167)

Received Date: 2021-12-14
Accepted Date: 2022-06-01
Rev Recd Date: 2022-05-24

Available Online: 2022-06-07

Publish Date: 2023-02-07

Abstract

Abstract

The traditional iterative-based Compressive Sensing (CS) image reconstruction algorithm is easy to integrate image prior information, but it has shortcomings such as insufficient performance and high computational complexity. The performance of the image reconstruction algorithm based on deep learning is better than the traditional reconstruction algorithm significantly, and it has lower time cost. Therefore, in order to design a deep learning image reconstruction algorithm that uses prior information more effectively, a deep compressive sensing image reconstruction network based on non-local priors is proposed. Firstly, the sparseness and non-local prior are combined to establish a compressed sensing image reconstruction model. Secondly, the model is decomposed into three sub-problems by the half quadratic splitting method. The solution of each sub-problem is carried out under the framework of deep learning. Finally, an end-to-end trainable image reconstruction model is jointly established. Simulation experiments show that the peak signal-to-noise ratio of the proposed algorithm under the tested sampling rate and dataset is improved by 0.18 dB, 1.59 dB, 2.09 dB on average compared with the current mainstream reconstruction algorithm SCSNet, CSNet, ISTA-Net⁺ respectively.
- Image reconstruction,
- Compressive Sensing (CS),
- Deep learning,
- Non-local prior,
- Half quadratic splitting

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References(30)

References

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