Image Compressed Sensing Reconstruction Based on  Structural Group Total Variation

Hui ZHAO; Xiaojun YANG; Jing ZHANG; Chao SUN; Tianqi ZHANG

doi:10.11999/JEIT190243

Volume 42 Issue 11

Nov. 2020

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Article Navigation > Journal of Electronics & Information Technology > 2020 > 42(11): 2773-2780

Hui ZHAO, Xiaojun YANG, Jing ZHANG, Chao SUN, Tianqi ZHANG. Image Compressed Sensing Reconstruction Based on Structural Group Total Variation[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2773-2780. doi: 10.11999/JEIT190243

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Hui ZHAO, Xiaojun YANG, Jing ZHANG, Chao SUN, Tianqi ZHANG. Image Compressed Sensing Reconstruction Based on Structural Group Total Variation[J]. Journal of Electronics & Information Technology, 2020, 42(11): 2773-2780. doi: 10.11999/JEIT190243

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Image Compressed Sensing Reconstruction Based on Structural Group Total Variation

doi: 10.11999/JEIT190243

1.
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61671095)

Received Date: 2019-04-11
Rev Recd Date: 2020-03-07

Available Online: 2020-04-09

Publish Date: 2020-11-16

Abstract

Abstract

To solve the problem that the traditional Compressed Sensing (CS) algorithm based on Total Variation (TV) model can not effectively restore details and texture of image, which leads to over-smoothing of reconstructed image, an image Compressed Sensing (CS) reconstruction algorithm based on Structural Group TV (SGTV) model is proposed. The proposed algorithm utilizes the non-local self-similarity and structural sparsity of image, and converts the CS recovery problem into the total variation minimization problem of the structural group constructed by non-local self-similar image blocks. In addition, the optimization model of the proposed algorithm is built with regularization constraint of the structural group total variation model, and it uses the split Bregman iterative algorithm to separate it into multiple sub-problems, and then solves them respectively. The proposed algorithm makes full use of the information and structural sparsity of image to protects the image details and texture. The experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art total variation based algorithm in both PSNR and visual perception.
- Image reconstruction,
- Compressed Sensing (CS),
- Nonlocal self-similarity,
- Total Variation (TV)

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References(20)

References

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