CUP-VISAR image reconstruction based on low-rank prior and total-variation regularization

Zheng Kaitao; Li Haiyan; Gan Huaquan; Huang Yunbao; Li Yulong; Jing Longfei; Guan Zanyang; Huang Qingxin; Yu Yuanping

doi:10.11884/HPLPB202335.230011

Volume 35 Issue 7

Jun. 2023

Turn off MathJax

Article Contents

Article Navigation > High Power Laser and Particle Beams > 2023 > 35(7): 072002

Zheng Kaitao, Li Haiyan, Gan Huaquan, et al. CUP-VISAR image reconstruction based on low-rank prior and total-variation regularization[J]. High Power Laser and Particle Beams, 2023, 35: 072002. doi: 10.11884/HPLPB202335.230011

Citation:

Zheng Kaitao, Li Haiyan, Gan Huaquan, et al. CUP-VISAR image reconstruction based on low-rank prior and total-variation regularization[J]. High Power Laser and Particle Beams, 2023, 35: 072002. doi: 10.11884/HPLPB202335.230011

Citation:

PDF( 11297 KB)

CUP-VISAR image reconstruction based on low-rank prior and total-variation regularization

doi: 10.11884/HPLPB202335.230011

1.
School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China
2.
Laser Fusion Research Center, CAEP, Mianyang 621900, China

Received Date: 2023-01-14
Accepted Date: 2023-04-12
Rev Recd Date: 2023-04-20

Available Online: 2023-05-09

Publish Date: 2023-06-15

Abstract

Abstract

To solve the problem of reconstructing two-dimensional shock wave fringe from compressed image obtained from Compressed Ultrafast Photography (CUP) and two-dimensional Velocity Interferometer System for Any Reflector (VISAR), a compressed image reconstruction algorithm based on low-rank constraint and total-variation regularization is proposed. The algorithm uses the similarity and smoothness of the spatial structure of the fringe image to transform the reconstruction problem into an optimization problem of kernel norm minimization and total-variation regularization, and splits the optimization problem into multiple sub-problems using the plug-and-play alternate direction multiplier method to solve the optimization problem, thus realizing accurate reconstruction of the CUP-VISAR compressed image. The simulation results show that under the condition of high noise, the peak signal-to-noise ratio of the reconstructed image is increased by 8.45 dB, and the structural similarity is increased by 8.52%. The reconstruction effect is better than that of the mainstream reconstruction algorithm. The experimental results show that the relative error of the maximum velocity of the shock wave fringe is reduced from 13.5% to 3.46% (reduced by nearly 10%), which verifies the effectiveness of the algorithm.
- inertial confinement fusion,
- CUP-VISAR,
- image reconstruction,
- low-rank prior,
- alternating direction multiplier method

FullText(HTML)

References(27)

References

[1]	郑万国, 齐红基. 人类首次实现聚变“点火”, 激光聚变取得历史性突破[J]. 人工晶体学报, 2023, 52(1):1-7 doi: 10.3969/j.issn.1000-985X.2023.01.001 Zheng Wanguo, Qi Hongji. An exclusive interview with ZHENG Wanguo on the “Ignition” milestone in human history[J]. Journal of Synthetic Crystals, 2023, 52(1): 1-7 doi: 10.3969/j.issn.1000-985X.2023.01.001
[2]	Hurricane O A, Callahan D A, Casey D T, et al. Inertially confined fusion plasmas dominated by alpha-particle self-heating[J]. Nature Physics, 2016, 12(8): 800-806. doi: 10.1038/nphys3720
[3]	Meezan N B, Edwards M J, Hurricane O A, et al. Indirect drive ignition at the National Ignition Facility[J]. Plasma Physics and Controlled Fusion, 2017, 59: 014021. doi: 10.1088/0741-3335/59/1/014021
[4]	王峰, 关赞洋, 理玉龙, 等. 基于神光Ⅲ装置的光学诊断系统介绍[J]. 中国科学:物理学力学天文学, 2018, 48(6):48-58 Wang Feng, Guan Zanyang, Li Yulong, et al. Optical diagnostic systems based on Shenguang Ⅲ[J]. SCIENTIA SINICA Physica, Mechanica & Astronomica, 2018, 48(6): 48-58
[5]	刘寿先, 李泽仁, 彭其先, 等. 一种新的线成像激光干涉测速系统[J]. 强激光与粒子束, 2009, 21(2):213-216 Liu Shouxian, Li Zeren, Peng Qixian, et al. A novel line-imaging velocity interferometer for shock diagnostics[J]. High Power Laser and Particle Beams, 2009, 21(2): 213-216
[6]	刘寿先, 李泽仁, 彭其先, 等. 用于激光驱动飞片诊断的线成像速度干涉仪[J]. 强激光与粒子束, 2010, 22(10):2281-2284 doi: 10.3788/HPLPB20102210.2281 Liu Shouxian, Li Zeren, Peng Qixian, et al. Line-imaging velocity interferometer for laser driven flyer diagnostics[J]. High Power Laser and Particle Beams, 2010, 22(10): 2281-2284 doi: 10.3788/HPLPB20102210.2281
[7]	Yang Yongmei, Li Yulong, Guan Zanyang, et al. A diagnostic system toward high-resolution measurement of wavefront profile[J]. Optics Communications, 2020, 456: 124554. doi: 10.1016/j.optcom.2019.124554
[8]	Gao Liang, Liang Jinyang, Li Chiye, et al. Single-shot compressed ultrafast photography at one hundred billion frames per second[J]. Nature, 2014, 516(7529): 74-77. doi: 10.1038/nature14005
[9]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582
[10]	王峰, 理玉龙, 关赞洋, 等. 压缩感知技术在激光惯性约束聚变研究中的应用[J]. 强激光与粒子束, 2022, 34:031021 doi: 10.11884/HPLPB202234.210250 Wang Feng, Li Yulong, Guan Zanyang, et al. Application of compressed sensing technology in laser inertial confinement fusion[J]. High Power Laser and Particle Beams, 2022, 34: 031021 doi: 10.11884/HPLPB202234.210250
[11]	Zhu Liren, Chen Yujia, Liang Jinyang, et al. Space- and intensity-constrained reconstruction for compressed ultrafast photography[J]. Optica, 2016, 3(7): 694-697. doi: 10.1364/OPTICA.3.000694
[12]	Liang Jinyang, Ma Cheng, Zhu Liren, et al. Single-shot real-time video recording of a photonic Mach cone induced by a scattered light pulse[J]. Science Advances, 2017, 3: e1601814. doi: 10.1126/sciadv.1601814
[13]	Yang Chengshuai, Qi Dalong, Liang Jinyang, et al. Compressed ultrafast photography by multi-encoding imaging[J]. Laser Physics Letters, 2018, 15: 116202. doi: 10.1088/1612-202X/aae198
[14]	Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1/4): 259-268.
[15]	Bioucas-Dias J M, Figueiredo M A T. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 2007, 16(12): 2992-3004. doi: 10.1109/TIP.2007.909319
[16]	马坚伟, 徐杰, 鲍跃全, 等. 压缩感知及其应用: 从稀疏约束到低秩约束优化[J]. 信号处理, 2012, 28(5):609-623 doi: 10.3969/j.issn.1003-0530.2012.05.001 Ma Jianwei, Xu Jie, Bao Yuequan, et al. Compressive sensing and its application: from sparse to low-rank regularized optimization[J]. Signal Processing, 2012, 28(5): 609-623 doi: 10.3969/j.issn.1003-0530.2012.05.001
[17]	Candès E J, Li Xiaodong, Ma Yi, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58: 11.
[18]	Chen S H, Wang Xiran, Elgendy O A. Plug-and-Play ADMM for image restoration: fixed-point convergence and applications[J]. IEEE Transactions on Computational Imaging, 2017, 3(1): 84-98. doi: 10.1109/TCI.2016.2629286
[19]	Venkatakrishnan S V, Bouman C A, Wohlberg B. Plug-and-Play priors for model based reconstruction[C]//IEEE Global Conference on Signal and Information Processing. 2013: 945-948.
[20]	Madych W R. Solutions of underdetermined systems of linear equations[J]. Spatial Statistics and Imaging, 1991, 20: 227-238.
[21]	Afonso M V, Bioucas-Dias J M, Figueiredo M A T. An augmented Lagrangian approach to the constrained optimization formulation of imaging inverse problems[J]. IEEE Transactions on Image Processing, 2011, 20(3): 681-695. doi: 10.1109/TIP.2010.2076294
[22]	Candès E J, Recht B. Exact matrix completion via convex optimization[J]. Foundations of Computational Mathematics, 2009, 9(6): 717-772. doi: 10.1007/s10208-009-9045-5
[23]	Candès E J, Tao T. The power of convex relaxation: near-optimal matrix completion[J]. IEEE Transactions on Information Theory, 2010, 56(5): 2053-2080. doi: 10.1109/TIT.2010.2044061
[24]	Cai Jianfeng, Candès E J, Shen Zuowei. A singular value thresholding algorithm for matrix completion[J]. SIAM Journal on Optimization, 2010, 20(4): 1956-1982. doi: 10.1137/080738970
[25]	Donoho D L. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627. doi: 10.1109/18.382009
[26]	Tanabe Y, Ishida T. Quantification of the accuracy limits of image registration using peak signal-to-noise ratio[J]. Radiological Physics and Technology, 2017, 10(1): 91-94. doi: 10.1007/s12194-016-0372-3
[27]	Brunet D, Vrscay E R, Wang Zhou. On the mathematical properties of the structural similarity index[J]. IEEE Transactions on Image Processing, 2012, 21(4): 1488-1499. doi: 10.1109/TIP.2011.2173206