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基于分块平滑投影二次重构算法的单像素成像系统

牟晓霜 黎淼 王玺 梁文凯 王峰 理玉龙 关赞洋 余泊汕 张磊 高翊喆 张佳杰

牟晓霜, 黎淼, 王玺, 等. 基于分块平滑投影二次重构算法的单像素成像系统[J]. 强激光与粒子束, 2022, 34: 119002. doi: 10.11884/HPLPB202234.220190
引用本文: 牟晓霜, 黎淼, 王玺, 等. 基于分块平滑投影二次重构算法的单像素成像系统[J]. 强激光与粒子束, 2022, 34: 119002. doi: 10.11884/HPLPB202234.220190
Mou Xiaoshuang, Li Miao, Wang Xi, et al. Single-pixel imaging system based on block smoothed projected quadratic reconstruction algorithm[J]. High Power Laser and Particle Beams, 2022, 34: 119002. doi: 10.11884/HPLPB202234.220190
Citation: Mou Xiaoshuang, Li Miao, Wang Xi, et al. Single-pixel imaging system based on block smoothed projected quadratic reconstruction algorithm[J]. High Power Laser and Particle Beams, 2022, 34: 119002. doi: 10.11884/HPLPB202234.220190

基于分块平滑投影二次重构算法的单像素成像系统

doi: 10.11884/HPLPB202234.220190
基金项目: 国家自然科学基金项目(61604028)
详细信息
    作者简介:

    牟晓霜,2016210786@stu.cqupt.edu.cn

    通讯作者:

    王 玺,xiwang@cqupt.edu.cn

  • 中图分类号: O439

Single-pixel imaging system based on block smoothed projected quadratic reconstruction algorithm

  • 摘要: 单像素成像系统是通过无空间分辨能力的单像元探测器来获取目标二维分布信息的计算光学成像技术,与传统直接成像技术相比具有高能量收集效率、高灵敏度等一系列优点,在高能物理诊断技术领域有着广阔的应用前景。针对实际单像素压缩感知成像系统在复杂诊断环境中存在的重建噪声较大的问题,提出并实现了基于分块平滑投影Landweber二次重构算法的单像素成像系统。根据算法观测矩阵分布特性以及数字微镜硬件输入要求实现了实际投影观测矩阵的变换,利用二次重构算法实现了单像素诊断的仿真分析与实验测试。仿真结果表明,在采样率为20%~30%的条件下,重建图像峰值信噪比大于20 dB,结构相似性高于0.8。进一步搭建单像素成像平台完成实验研究及验证,实验结果表明,利用二次重构算法模型对目标场景进行恢复的效果优于其余两种传统算法。二次重构单像素成像系统在采样率仅为20%的条件下能够重建出清晰的原始图像,具有较好的噪声抑制特性。
  • 图  1  压缩感知单像素成像数学模型

    Figure  1.  Mathematical model of compressed sensing single-pixel imaging

    图  2  基于压缩感知单像素成像二次重构实验系统

    Figure  2.  Quadratic reconstruction experimental system based on compressed sensing single-pixel imaging

    图  3  BCS-SPL算法流程图[22]

    Figure  3.  BCS-SPL algorithm flow chart[22]

    图  4  采样率30%时,不同算法对图像重构的效果

    Figure  4.  Effect of different algorithms on image reconstruction at 30% sampling rate

    图  5  不同采样率下SPL二次重构模型图像重建效果

    Figure  5.  Image reconstruction effect of SPL quadratic reconstruction model under different sampling rates

    图  6  不同算法和采样率下图像重构的效果

    Figure  6.  Effect of image reconstruction under different algorithms and sampling rates

    表  1  采样率30%时,不同重构算法间性能的比较

    Table  1.   Performance comparison between different reconstruction algorithms at 30% sampling rate

    algorithmSSIMPSNR/dBreconstruction time/s
    BP0.4419.1979.5
    OMP0.5322.261.4
    proposed algorithm0.7627.614.6
    下载: 导出CSV

    表  2  不同采样率下重建图像的PSNR和SSIM

    Table  2.   PSNR and SSIM of reconstructed images at different sampling rates

    imagePSNR at different sampling rate/dBSSIM at different sampling rate
    10%20%30%10%20%30%
    “重” 19.46 21.60 23.66 0.61 0.83 0.86
    “CQUPT” 16.90 20.44 23.19 0.56 0.80 0.82
    rabbit 18.56 21.48 22.70 0.58 0.81 0.83
    下载: 导出CSV

    表  3  不同算法和采样率下图像重构所需时间

    Table  3.   Time required for image reconstruction under different algorithms and sampling rates

    imagealgorithmtime at different sampling rate/s
    10%20%30%
    “重” BP 161.5 566.7 1026.1
    OMP 0.9 1.8 2.9
    proposed algorithm 2.6 3.6 4.7
    “CQUPT” BP 155.5 566.6 1026.1
    OMP 0.9 1.8 2.9
    proposed algorithm 2.5 3.6 4.8
    rabbit BP 155.8 589.1 1204.3
    OMP 0.9 1.9 2.8
    proposed algorithm 2.6 3.8 4.7
    下载: 导出CSV
  • [1] 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
    [2] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. doi: 10.1109/TIT.2006.871582
    [3] Candes E J, Romberg J, Tao T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Transactions on Information Theory, 2006, 52(2): 489-509. doi: 10.1109/TIT.2005.862083
    [4] Candes E J, Tao T. Near-optimal signal recovery from random projections: universal encoding strategies[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406-5425. doi: 10.1109/TIT.2006.885507
    [5] Guan Zanyang, Li Yulong, Wang Feng, et al. Study on the length of diagnostic time window of CUP-VISAR[J]. Measurement Science and Technology, 2021, 32: 125208. doi: 10.1088/1361-6501/ac29d4
    [6] 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
    [7] Duarte M F, Davenport M A, Takhar D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine, 2008, 25(2): 83-91. doi: 10.1109/MSP.2007.914730
    [8] 李明飞, 阎璐, 杨然, 等. 基于Hadamard矩阵优化排序的快速单像素成像[J]. 物理学报, 2019, 68:064202 doi: 10.7498/aps.68.20181886

    Li Mingfei, Yan Lu, Yang Ran, et al. Fast single-pixel imaging based on optimized reordering Hadamard basis[J]. Acta Physica Sinica, 2019, 68: 064202 doi: 10.7498/aps.68.20181886
    [9] 马彦鹏, 王亚南, 王义坤, 等. 基于压缩感知的单点探测计算成像技术研究[J]. 光学学报, 2013, 33:1211007 doi: 10.3788/AOS201333.1211007

    Ma Yanpeng, Wang Yanan, Wang Yikun, et al. Study of single-pixel detection computational imaging technology based on compressive sensing[J]. Acta Optica Sinica, 2013, 33: 1211007 doi: 10.3788/AOS201333.1211007
    [10] 陈涛, 李正炜, 王建立, 等. 应用压缩传感理论的单像素相机成像系统[J]. 光学 精密工程, 2012, 20(11):2523-2530 doi: 10.3788/OPE.20122011.2523

    Chen Tao, Li Zhengwei, Wang Jianli, et al. Imaging system of single pixel camera based on compressed sensing[J]. Optics and Precision Engineering, 2012, 20(11): 2523-2530 doi: 10.3788/OPE.20122011.2523
    [11] Wu Shaohua, Zhang Tiantian, Wu Bo, et al. Single-pixel camera in the visible band with fiber signal collection[J]. IEEE Access, 2018, 6: 17768-17775. doi: 10.1109/ACCESS.2018.2819358
    [12] Wei Ziran, Zhang Jianlin, Xu Zhiyong, et al. Optimization methods of compressively sensed image reconstruction based on single-pixel imaging[J]. Applied Sciences, 2020, 10: 3288. doi: 10.3390/app10093288
    [13] Mun S, Fowler J E. Block compressed sensing of images using directional transforms[C]//2009 16th IEEE International Conference on Image Processing (ICIP). Cairo: IEEE, 2009: 3021-3024.
    [14] Candès E J. The restricted isometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique, 2008, 346(9/10): 589-592.
    [15] Chen S S, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit[J]. SIAM Journal on Scientific Computing, 1998, 20(1): 33-61. doi: 10.1137/S1064827596304010
    [16] Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586-597. doi: 10.1109/JSTSP.2007.910281
    [17] Mallat S G, Zhang Zhifeng. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12): 3397-3415. doi: 10.1109/78.258082
    [18] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12): 4655-4666. doi: 10.1109/TIT.2007.909108
    [19] Needell D, Tropp J A. CoSaMP: iterative signal recovery from incomplete and inaccurate samples[J]. Communications of the ACM, 2010, 53(12): 93-100. doi: 10.1145/1859204.1859229
    [20] 苏杰, 翟爱平, 赵文静, 等. 自适应斜Z字形采样Hadamard单像素成像[J]. 光子学报, 2021, 50:0311003 doi: 10.3788/gzxb20215003.0311003

    Su Jie, Zhai Aiping, Zhao Wenjing, et al. Hadamard single-pixel imaging using adaptive oblique zigzag sampling[J]. Acta Photonica Sinica, 2021, 50: 0311003 doi: 10.3788/gzxb20215003.0311003
    [21] Gan Lu. Block compressed sensing of natural images[C]//2007 15th International Conference on Digital Signal Processing. Cardiff: IEEE, 2007: 403-406.
    [22] Van Trinh C, Dinh K Q, Jeon B. Edge-preserving block compressive sensing with projected Landweber[C]//2013 20th International Conference on Systems, Signals and Image Processing (IWSSIP). Bucharest: IEEE, 2013: 71-74.
    [23] 张赛文, 于斌, 陈丹妮, 等. 基于压缩感知的高密度分子定位算法比较[J]. 中国激光, 2018, 45:0307014 doi: 10.3788/CJL201845.0307014

    Zhang Saiwen, Yu Bin, Chen Danni, et al. Comparison of algorithms of high-density molecule localization based on compressed sensing[J]. Chinese Journal of Lasers, 2018, 45: 0307014 doi: 10.3788/CJL201845.0307014
    [24] 吴光文, 张爱军, 王昌明. 一种用于压缩感知理论的投影矩阵优化算法[J]. 电子与信息学报, 2015, 37(7):1682-1687 doi: 10.11999/JEIT141450

    Wu Guangwen, Zhang Aijun, Wang Changming, et al. Novel optimization method for projection matrix in compress sensing theory[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1682-1687 doi: 10.11999/JEIT141450
    [25] Li Shufeng, Cao Guangjing, Wei Shanshan. Improved measurement matrix and reconstruction algorithm for compressed sensing[C]//2018 8th International Conference on Electronics Information and Emergency Communication (ICEIEC). 2018: 136-139.
    [26] Wan Rentao, Zhou Jinjia, Huang Bowen, et al. APMC: adjacent pixels based measurement coding system for compressively sensed images[J]. IEEE Transactions on Multimedia, 2021, 24: 3558-3569.
    [27] Kuusela T A. Single-pixel camera[J]. American Journal of Physics, 2019, 87(10): 846-850. doi: 10.1119/1.5122745
    [28] 王之润, 赵文静, 翟爱平, 等. 不同正交变换深度Q网络单像素成像性能比较[J]. 光子学报, 2022, 51:0311003 doi: 10.3788/gzxb20225103.0311003

    Wang Zhirun, Zhao Wenjing, Zhai Aiping, et al. Comparison on performance of deep Q network based single-pixel imaging using different orthogonal transformations[J]. Acta Photonica Sinica, 2022, 51: 0311003 doi: 10.3788/gzxb20225103.0311003
    [29] Sun Mingjie, Xu Zihao, Wu Ling’an. Collective noise model for focal plane modulated single-pixel imaging[J]. Optics and Lasers in Engineering, 2018, 100: 18-22. doi: 10.1016/j.optlaseng.2017.07.005
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出版历程
  • 收稿日期:  2022-06-06
  • 修回日期:  2022-09-13
  • 网络出版日期:  2022-09-15
  • 刊出日期:  2022-09-20

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