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基于Hadamard矩阵优化排序的快速单像素成像

李明飞 阎璐 杨然 刘院省

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基于Hadamard矩阵优化排序的快速单像素成像

李明飞, 阎璐, 杨然, 刘院省

Fast single-pixel imaging based on optimized reordering Hadamard basis

Li Ming-Fei, Yan Lu, Yang Ran, Liu Yuan-Xing
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  • 为提升单像素成像速度, 提出了基于Hadamard矩阵优化排序的压缩采样解决方案. 利用数值仿真和室外实验对提出的5种排序方法进行了对比分析. 研究结果表明: 按Haar小波变换系数绝对值排序时单像素成像效果最优, 排序对应到Walsh序后可利用快速变换重建图像, 速度达300帧/秒@64 × 64像素; 最优排序下, 采样率25%仍可重建图像, 采样速度可提升4倍. 针对排序方法与成像信噪比关系, 从关联成像角度给出了其物理解释: 测量基矩阵元邻域数值相等的区域面积等效于光场二阶相干面积, 当光场二阶相干面积随测量基由大到小排序时成像效果最优. 本文研究成果可用于提升单像素成像速度, 具有实用价值.
    Single-pixel imaging is a computational imaging scheme that offers novel solutions for multi-spectral imaging, feature-based imaging, polarimetric imaging, three-dimensional imaging, holographic imaging, and optical encryption. The single-pixel imaging scheme can be used for imaging in wave band such as infrared and micro wave imaging, or will be useful in the case where the array detector technique is difficult to meet the requirement such as the sensitivity or the volume. The main limitation for its application comes from a trade-off between spatial resolution and acquisition time, in other words, from relatively high measurement and reconstruction time. Although compressive sensing technique can be used to improve the acquisition time by reducing the number of samplings, the computational time to reconstruct an image is not fast enough to satisfy the real-time video. In this paper, we propose to reduce the required signal acquisition time by using a novel sampling scheme based on optimized ordering of the Hadamard basis, and improve the image reconstruction efficiency by using fast Walsh-Hadamard transform. In our method, the Hadamard basis is rearranged in the ascendant order of the values of its " sparsity” coefficients which are obtained through " Daubechies wavelets 1 (Haar wavelets)”, " Daubechies wavelets 2” wavelet transform and discrete cosine transform, and then compute each total sum of the transformed coefficients’ absolute value, respectively. The measurement order of the Hadamard basis is then rearranged directly according to Walsh order and random permutation order. The peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the retrieved images are computed and compared to test all the five reordering schemes above both in our numerical simulation and outdoor experiments. We find that the reordering method based on Haar wavelet transform is the best PSNR and SSIM and it can reconstruct image under a sampling ratio of 25% which corresponds to the recovering time in which 300 frame per second @64 × 64 pixels single-pixel imaging can be achieved. The optimized measurement order of Hadamard basis greatly simplifies post processing, resulting in significantly faster image reconstruction, which steps further toward high frame rate single-pixel imaging’s applications. Moreover, we propose a novel method to optimize measurement basis in single-pixel imaging, which may be useful in other basis optimizing, such as optimized random speckles, etc.
      通信作者: 李明飞, mf_li@sina.cn
    • 基金项目: 国防基础科研计划(批准号: JCKY2016601C005)和国家自然科学基金(批准号: 61805006)资助的课题.
      Corresponding author: Li Ming-Fei, mf_li@sina.cn
    • Funds: Project supported by the Defense Industrial Technology Development Program, China (Grant No. JCKY2016601C005) and the National Natural Science Foundation of China (Grant No. 61805006).
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    Duarte M, Davenport M, Takhar D, Laska J, Sun T, Kelly K, Baraniuk R 2008 IEEE Signal Proc. Mag. 25 83Google Scholar

    [4]

    Gong W L, Han S S 2009 arXiv: 0911.4750

    [5]

    Czajkowski K M, Pastuszczak A, Kotyński R 2017 arXiv: 1709.07739v2

    [6]

    Olivas S J, Rachlin Y, Gu L, Gardiner B, Dawson R, Laine J P, Ford J E 2013 Appl. Opt. 52 4515Google Scholar

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    李明飞, 莫小范, 张安宁 2016 导航与控制 5 1Google Scholar

    Li M F, Mo X F, Zhang A N 2016 Navigtion and Control 5 1Google Scholar

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    李明飞, 莫小范, 赵连洁, 霍娟, 杨然, 李凯, 张安宁 2015 物理学报 65 064201Google Scholar

    Li M F, Mo X F, Zhao L J, Huo J, Yang R, Li K, Zhang A N 2015 Acta Phys. Sin. 65 064201Google Scholar

    [9]

    Zhang Z, Wang X, Zheng G, Zhong J 2017 Sci. Rep. 7 12029Google Scholar

    [10]

    Chen M L., Li E R, Han S S 2014 Appl. Opt. 53 2924Google Scholar

    [11]

    Sun S, Liu W T, Lin H Z, Zhang E F, Liu J Y, Li Q, Chen P X, 2016 Sci. Rep. 6 37013Google Scholar

    [12]

    Sun M J, Meng L T, Edgar M P, Padgett M J, Radwell N 2017 Sci. Rep. 7 3464Google Scholar

    [13]

    Li M F, Zhang Y R, Liu X F, Yao X R, Luo K H, Fan H, Wu L A 2013 Appl. Phys. Lett. 103 211119Google Scholar

    [14]

    Li M F, Zhang Y R, Luo K H, Wu L A, Fan H 2013 Phys. Rev. A 87 033813Google Scholar

    [15]

    Liu X L, Shi J H, Wu X Y, Zeng G H 2018 Sci. Rep. 8 5012Google Scholar

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    Beer T 1981 Am. J. Phys. 49 466Google Scholar

    [17]

    Li Q, Zhou M L, Shi B C, Wang N C 1998 Chinese Science Bulletin 43 627Google Scholar

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    Ferri F, Magatti D, Lugiato L A, Gatti A 2010 Phys. Rev. Lett. 104 253603Google Scholar

  • 图 1  单像素成像原理示意图, 其中待成像物体经主透镜成像到DMD上, 经编码矩阵调制的光束被反射, 反射光由中继透镜汇聚到光电探测器, 探测器将光信号转为电信号, 电信号经过A/D转换由模拟信号转为数字信号, 重复刷新DMD调制矩阵将得到一系列数字信号, 将数字信号采集到计算设备即可利用算法重构图像

    Fig. 1.  Schematic diagram of the experimental setup. Lena is the object to be recovered; the main lens imaging Lena onto the DMD, which is modulated by matrices, then the light is reflected and collected by the relay lens into the photo-detector. As DMD modulation matrices refreshing continuously, the analog-to-digital converter (A/D) connected with the photo-detector receives a series of digital signals, which are finally sent to recover the image by the computer.

    图 2  不同排序方法下图像SNR与测量次数m的关系, 其中排序方法包括Haar小波变换序、Db2小波变换序、Dct序、Walsh排序和随机排序

    Fig. 2.  Image SNR versus mth measurements under different ordering methods: Haar wavelet order, Db2 wavelet order and Dct order, Walsh order and random order.

    图 3  测试图像及不同排序方法在25%采样下的图像重建结果 (a) 原始测试图像64 × 64像素; (b)—(f) 排序方法为Db2小波序、Dct序、Walsh序、随机排序和Haar小波排序下重图像重建结果

    Fig. 3.  Original and recovered images under 25% full sampling: (a) The Lena, with 64 × 64 pixels; (b)−(f) images recovered corresponding to the ordering method of Db2 wavelet order, Dct order, Walsh order, random permutation order and Haar wavelet order, respectively.

    图 4  测试图3(a)在不同排序下仿真结果 (a) PSNR值与测量次数m的关系; (b) SSIM值与测量次数m的关系

    Fig. 4.  Simulation results of the Fig. 3(a): (a) The PSNR versus mth measurement; (b) the SSIM versus mth measurement.

    图 5  不同排序方法25%采样下的实验数据, 即Haar小波变换、Db2小波变换、Dct、Walsh排序和随机随机排序对应的实验数据

    Fig. 5.  Experimental data of different ordering methods: Haar wavelet, Db2 wavelet and Dct, Walsh order and random order.

    图 6  室外实验结果和不同排序方法在25%采样下的重建图像 (a)目标区域, 对应距离800 m; (b)相机拍摄的目标图像; (c) 无压缩单像素成像, 64 × 64像素, 100幅图像累加结果; (d)—(h) 分别对应排序方法为Db2小波序、Dct序、Walsh序、随机排序和Haar小波排序下重图像重建结果

    Fig. 6.  Outdoor experiment and recovered images under 25% full sampling with different ordering methods: (a) The target region, with the distance 800 meters; (b) target image captured by camera; (c) image recovered by single-pixel camera, with 64 × 64 pixels, with 100 recovered images averaged; (d)−(h) images recovered corresponding to the ordering method of Db2 wavelet order, Dct order, Walsh order, random permutation order and Haar wavelet order, respectively.

  • [1]

    Candès E J, Romberg J, Tao T 2006 IEEE Trans. Inform. Theory 52 489Google Scholar

    [2]

    Romberg J 2008 IEEE Signal Proc. Mag. 25 14Google Scholar

    [3]

    Duarte M, Davenport M, Takhar D, Laska J, Sun T, Kelly K, Baraniuk R 2008 IEEE Signal Proc. Mag. 25 83Google Scholar

    [4]

    Gong W L, Han S S 2009 arXiv: 0911.4750

    [5]

    Czajkowski K M, Pastuszczak A, Kotyński R 2017 arXiv: 1709.07739v2

    [6]

    Olivas S J, Rachlin Y, Gu L, Gardiner B, Dawson R, Laine J P, Ford J E 2013 Appl. Opt. 52 4515Google Scholar

    [7]

    李明飞, 莫小范, 张安宁 2016 导航与控制 5 1Google Scholar

    Li M F, Mo X F, Zhang A N 2016 Navigtion and Control 5 1Google Scholar

    [8]

    李明飞, 莫小范, 赵连洁, 霍娟, 杨然, 李凯, 张安宁 2015 物理学报 65 064201Google Scholar

    Li M F, Mo X F, Zhao L J, Huo J, Yang R, Li K, Zhang A N 2015 Acta Phys. Sin. 65 064201Google Scholar

    [9]

    Zhang Z, Wang X, Zheng G, Zhong J 2017 Sci. Rep. 7 12029Google Scholar

    [10]

    Chen M L., Li E R, Han S S 2014 Appl. Opt. 53 2924Google Scholar

    [11]

    Sun S, Liu W T, Lin H Z, Zhang E F, Liu J Y, Li Q, Chen P X, 2016 Sci. Rep. 6 37013Google Scholar

    [12]

    Sun M J, Meng L T, Edgar M P, Padgett M J, Radwell N 2017 Sci. Rep. 7 3464Google Scholar

    [13]

    Li M F, Zhang Y R, Liu X F, Yao X R, Luo K H, Fan H, Wu L A 2013 Appl. Phys. Lett. 103 211119Google Scholar

    [14]

    Li M F, Zhang Y R, Luo K H, Wu L A, Fan H 2013 Phys. Rev. A 87 033813Google Scholar

    [15]

    Liu X L, Shi J H, Wu X Y, Zeng G H 2018 Sci. Rep. 8 5012Google Scholar

    [16]

    Beer T 1981 Am. J. Phys. 49 466Google Scholar

    [17]

    Li Q, Zhou M L, Shi B C, Wang N C 1998 Chinese Science Bulletin 43 627Google Scholar

    [18]

    Ferri F, Magatti D, Lugiato L A, Gatti A 2010 Phys. Rev. Lett. 104 253603Google Scholar

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出版历程
  • 收稿日期:  2018-10-22
  • 修回日期:  2019-01-02
  • 上网日期:  2019-03-01
  • 刊出日期:  2019-03-20

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