2D Compressed Sensing Algorithm Based on Adaptive Blocking and Joint Optimization Smooth <i>l</i><sub>0</sub> Norm

ZHANG Xiaobei; TANG Chen; TU Ximei; LU Xiaogang; ZHANG Qi

doi:10.11999/JEIT221097

Volume 45 Issue 12

Dec. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(12): 4431-4439

ZHANG Xiaobei, TANG Chen, TU Ximei, LU Xiaogang, ZHANG Qi. 2D Compressed Sensing Algorithm Based on Adaptive Blocking and Joint Optimization Smooth l0 Norm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4431-4439. doi: 10.11999/JEIT221097

Citation:

ZHANG Xiaobei, TANG Chen, TU Ximei, LU Xiaogang, ZHANG Qi. 2D Compressed Sensing Algorithm Based on Adaptive Blocking and Joint Optimization Smooth l₀ Norm[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4431-4439. doi: 10.11999/JEIT221097

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2D Compressed Sensing Algorithm Based on Adaptive Blocking and Joint Optimization Smooth l₀ Norm

doi: 10.11999/JEIT221097

1.
School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
2.
Shanghai Aircraft Design and Research Institute, Shanghai 201210, China

Funds: The Ministry of Industry and Information Technology Civil Aircraft Special Research Project (MJZ-2018-S-30)

Received Date: 2022-08-22
Rev Recd Date: 2023-03-31

Available Online: 2023-04-04

Publish Date: 2023-12-26

Abstract

Abstract

A 2-dimension compressed sensing algorithm based on adaptive blocking and joint optimization Smooth l₀ (SL0) norm is proposed to solve the problem of poor compression and reconstruction performance of the traditional compressed sensing model and reconstruction method. In the compression process, gray entropy and quadtree algorithm are used for adaptive blocking and sample rate allocation. At the same time, the compressed sensing model is optimized and the chaotic cyclic matrix is used as the measure matrix, which improves the compression performance. In the reconstruction process based on SL0 algorithm, a fitting function with higher steepness and a scheme combined with Quasi-Newton method and dynamic iteration are adopted to improve the reconstruction quality and efficiency. Compared with other algorithms, the peak signal to noise ratio and structural similarity index of the proposed algorithm are improved by 5.44 dB and 21.08% on average respectively. The average calculation time is only 1.59 s. Based on realizing image compression and accurate reconstruction stably and quickly, the proposed algorithm provides a new method for compressed sensing and image reconstruction.
- 2-Dimensional compressed sensing,
- Adaptive blocking,
- Image reconstruction,
- Smooth l₀ algorithm,
- Quasi-Newton method

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References(24)

References

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