Compressed Sensing Image Restoration Based on Non-local Low Rank and Weighted Total Variation

Hui ZHAO; Jing ZHANG; Le ZHANG; Yingli LIU; Tianqi ZHANG

doi:10.11999/JEIT180828

Volume 41 Issue 8

Aug. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(8): 2025-2032

Hui ZHAO, Jing ZHANG, Le ZHANG, Yingli LIU, Tianqi ZHANG. Compressed Sensing Image Restoration Based on Non-local Low Rank and Weighted Total Variation[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2025-2032. doi: 10.11999/JEIT180828

Citation:

Hui ZHAO, Jing ZHANG, Le ZHANG, Yingli LIU, Tianqi ZHANG. Compressed Sensing Image Restoration Based on Non-local Low Rank and Weighted Total Variation[J]. Journal of Electronics & Information Technology, 2019, 41(8): 2025-2032. doi: 10.11999/JEIT180828

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Compressed Sensing Image Restoration Based on Non-local Low Rank and Weighted Total Variation

doi: 10.11999/JEIT180828

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: 2018-08-22
Rev Recd Date: 2019-01-28

Available Online: 2019-02-25

Publish Date: 2019-08-01

Abstract

Abstract

In order to reconstruct natural image from Compressed Sensing(CS) measurements accurately and effectively, a CS image reconstruction algorithm based on Non-local Low Rank(NLR) and Weighted Total Variation(WTV) is proposed. The proposed algorithm considers the Non-local Self-Similarity(NSS) and local smoothness in the image and improves the traditional TV model, in which only the weights of image’s high-frequency components are set and constructed with a differential curvature edge detection operator. Besides, the optimization model of the proposed algorithm is built with constraints of the improved TV and the non-local low rank model, and a non-convex smooth function and a soft thresholding function are utilized to solve low rank and TV optimization problems respectively. By taking advantage of them, the proposed method makes full use of the property of image, and therefore conserves the details of image and is more robust and adaptable. Experimental results show that, compared with the CS reconstruction algorithm via non-local low rank, at the same sampling rate, the Peak Signal to Noise Ratio(PSNR) of the proposed method increases by 2.49 dB at most and the proposed method is more robust, which proves the effectiveness of the proposed algorithm.
- Compressed Sensing(CS),
- Image reconstruction,
- Non-local Low Rank(NLR),
- Weighted Total Variation(WTV)

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References(24)

References

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