Total Variation Regularized Reconstruction Algorithms for Block Compressive Sensing

Derong CHEN; Haibo LÜ; Qiufu LI; Jiulu GONG; Zhiqiang LI; Xiaojun HAN

doi:10.11999/JEIT180931

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2217-2223

Derong CHEN, Haibo LÜ, Qiufu LI, Jiulu GONG, Zhiqiang LI, Xiaojun HAN. Total Variation Regularized Reconstruction Algorithms for Block Compressive Sensing[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2217-2223. doi: 10.11999/JEIT180931

Citation:

Derong CHEN, Haibo LÜ, Qiufu LI, Jiulu GONG, Zhiqiang LI, Xiaojun HAN. Total Variation Regularized Reconstruction Algorithms for Block Compressive Sensing[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2217-2223. doi: 10.11999/JEIT180931

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Total Variation Regularized Reconstruction Algorithms for Block Compressive Sensing

doi: 10.11999/JEIT180931

1.
Beijing Institute of Technology, Beijing 100081, China
2.
Beijing Institute of Astronautical Systems Engineering, Beijing 100076, China
3.
Beijing HYTQ Technology Ltd. Co., Beijing 100043, China

Received Date: 2018-09-30
Rev Recd Date: 2019-02-18

Available Online: 2019-03-23

Publish Date: 2019-09-10

Abstract

Abstract

In order to improve the quality of reconstruction image by Block Compressed Sensing (BCS), a Total Variation Iterative Threshold regularization image reconstruction algorithm (BCS-TVIT) is proposed. Combining the properties of local smoothing and bounded variation of the image, BCS-TVIT uses the minimization l₀ norm and total variation to construct the objective function. To solve the problem that l₀ norm term and the block measurement constraint can not be optimized directly, the iterative threshold method is used to minimize the l₀ norm of the reconstructed image, and the convex set projection is employed to guarantee the block measurement constraint condition. Experiments show that BCS-TVIT has better performance than BCS-SPL in PSNR by 2 dB. Meanwhile, BCS-TVIT can eliminate the " bright spot” effect of BCS-SPL, having better visual effect. Comparing with the minimum total variation, the proposed algorithm increases PSNR by 1 dB, and the reconstruction time is reduced by two orders of magnitude.
- Block Compressed Sensing (BCS),
- l₀ norm,
- Total Variation (TV),
- Threshold filtering,
- Convex set projection

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References(23)

References

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