Enhanced fractional-order total variation regularization-based velocity field reconstruction for CUP-VISAR diagnostic system

被引:0
|
作者
Li, Miao [1 ]
Ang, Chenyan [1 ]
Yu, Baishan [1 ]
Ang, Xi [1 ]
Li, Yulong [1 ]
Guan, Zanyang [2 ]
Wang, Feng [1 ]
Zhang, Lingqiang [1 ]
Fu, Yuting [1 ]
机构
[1] Chongqing Univ Posts & Telecommun, Sch Optoelect Engn, Chongqing 400065, Peoples R China
[2] China Acad Engn Phys, Laser Fus Res Ctr, Mianyang 621900, Peoples R China
来源
OPTICS EXPRESS | 2024年 / 32卷 / 19期
基金
中国国家自然科学基金;
关键词
The fusion of a velocity interferometer system for any reflector with compressed ultrafast photography systems in recent literature can achieve two-dimensional spatiotemporal diagnosis of shock wave velocities. Addressing the limitations posed by 7 × 7 coded aperture sampling; this study introduces an enhanced three-dimensional reconstruction algorithm grounded in fractional-order total variation regularization (E-3DFOTV). Simulated reconstructions and analysis were conducted on 80 frames of 350 × 800 fringes. The results show that compared with TWIST; ADMM; and E-3DTV; the average PSNR of the E-3DFOTV algorithm is increased by 16.81 dB; 14.46; dB; and; 2.98; respectively; and the average SSIM of the E-3DFOTV algorithm is increased by 53.20%; 27%; 3.19%; Moreover; the reconstruction time consumption of E-3DFOTV is reduced by 33.48% compared with the E-3DTV algorithm and 2.94% compared with the ADMM algorithm. The two-dimensional distribution of shock wave velocity fields reconstructed using E-3DFOTV exhibits minimal errors; with percentages within 1.67%; 1.00%; and 2.14% at different slices; the experiment was conducted on the ShenGuang-III prototype laser facility and VISAR data has been reconstructed in 1.25 ns range. Reconstruction results from experimental data demonstrate that the percentage errors at maximum velocity location for ADMM; E-3DTV; and E-3DFOTV are 12.08%; 19.27%; 3.59%; and the maximum percentage error for E-3DFOTV is 6.65%; underscoring the feasibility of the algorithm. © 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement;
D O I
10.1364/OE.533054
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
The fusion of a velocity interferometer system for any reflector with compressed ultrafast photography systems in recent literature can achieve two-dimensional spatiotemporal diagnosis of shock wave velocities. Addressing the limitations posed by 7 x 7 coded aperture sampling, this study introduces an enhanced three-dimensional reconstruction algorithm grounded in fractional-order total variation regularization (E-3DFOTV). Simulated reconstructions and analysis were conducted on 80 frames of 350 x 800 fringes. The results show that compared with TWIST, ADMM, and E-3DTV, the average PSNR of the E-3DFOTV algorithm is increased by 16.81 dB, 14.46 dB, and 2.98 dB, respectively, and the average SSIM of the E-3DFOTV algorithm is increased by 53.20%, 27%, and 3.19%, respectively. Moreover, the reconstruction time consumption of E-3DFOTV is reduced by 33.48% compared with the E-3DTV algorithm and 2.94% compared with the ADMM algorithm. The two-dimensional distribution of shock wave velocity fields reconstructed using E-3DFOTV exhibits minimal errors, with percentages within 1.67%, 1.00%, and 2.14% at different slices, respectively. Moreover, the experiment was conducted on the ShenGuang-III prototype laser facility and VISAR data has been reconstructed in 1.25 ns range. Reconstruction results from experimental data demonstrate that the percentage errors at maximum velocity location for ADMM, E-3DTV, and E-3DFOTV are 12.08%, 19.27%, and 3.59%, and the maximum percentage error for E-3DFOTV is 6.65%, underscoring the feasibility of the algorithm. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
引用
收藏
页码:32629 / 32642
页数:14
相关论文
共 50 条
  • [1] Research into CUP-VISAR velocity reconstruction based on weighted DRUNet and total variation joint optimization
    Wang, Xi
    Zhang, Lei
    Li, Miao
    Yu, Boshan
    Guo, Zhaohui
    Zhao, Xueyin
    Wang, Feng
    Li, Yulong
    Guan, Zanyang
    OPTICS LETTERS, 2023, 48 (20) : 5181 - 5184
  • [2] Fractional-order iterative regularization method for total variation based image denoising
    Zhang, Jun
    Wei, Zhihui
    Xiao, Liang
    JOURNAL OF ELECTRONIC IMAGING, 2012, 21 (04)
  • [3] Total fractional-order variation regularization based image reconstruction method for capacitively coupled electrical resistance tomography
    Shi, Yanyan
    Liao, Juanjuan
    Wang, Meng
    Li, Yating
    Fu, Feng
    Soleimani, Manuchehr
    FLOW MEASUREMENT AND INSTRUMENTATION, 2021, 82
  • [4] Hybrid High-Order and Fractional-Order Total Variation with Nonlocal Regularization for Compressive Sensing Image Reconstruction
    Hou, Lijia
    Qin, Yali
    Zheng, Huan
    Pan, Zemin
    Mei, Jicai
    Hu, Yingtian
    ELECTRONICS, 2021, 10 (02) : 1 - 17
  • [5] A Fractional-Order Total Variation Regularization-Based Method for Recovering Geiger-Mode Avalanche Photodiode Light Detection and Ranging Depth Images
    Xie, Da
    Wang, Xinjian
    Wang, Chunyang
    Yuan, Kai
    Wei, Xuyang
    Liu, Xuelian
    Huang, Tingsheng
    FRACTAL AND FRACTIONAL, 2023, 7 (06)
  • [6] Image Restoration with Fractional-Order Total Variation Regularization and Group Sparsity
    Bhutto, Jameel Ahmed
    Khan, Asad
    Rahman, Ziaur
    MATHEMATICS, 2023, 11 (15)
  • [7] Directional fractional-order total variation hybrid regularization for image deblurring
    Liu, Qiaohong
    Gao, Song
    JOURNAL OF ELECTRONIC IMAGING, 2020, 29 (03)
  • [8] Super-resolution image reconstruction using fractional-order total variation and adaptive regularization parameters
    Xiaomei Yang
    Jiawei Zhang
    Yanan Liu
    Xiujuan Zheng
    Kai Liu
    The Visual Computer, 2019, 35 : 1755 - 1768
  • [9] Super-resolution image reconstruction using fractional-order total variation and adaptive regularization parameters
    Yang, Xiaomei
    Zhang, Jiawei
    Liu, Yanan
    Zheng, Xiujuan
    Liu, Kai
    VISUAL COMPUTER, 2019, 35 (12): : 1755 - 1768
  • [10] A Total Fractional-Order Variation Regularized Reconstruction Method for CT
    Chen, Yun
    Guo, Baoyu
    Lu, Yao
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020