Sub-Nyquist single-pixel imaging by optimizing sampling basis

Zi-dong ZHAO; Zhao-hua YANG; Gao-liang LI

doi:10.37188/OPE.20212905.1008

您当前的位置：

首页 >

文章列表页 >

Sub-Nyquist single-pixel imaging by optimizing sampling basis

Modern Applied Optics | 更新时间：2021-06-21

- Sub-Nyquist single-pixel imaging by optimizing sampling basis
- Optics and Precision Engineering Vol. 29, Issue 5, Pages: 1008-1013(2021)
- 作者机构：
  
  1.北京航空航天大学仪器科学与光电工程学院，北京 100191
  2.北京华航无线电测量研究所，北京 100010
- 作者简介：
  
  E-mail： yangzh@buaa.edu.cn
- 基金信息：
- DOI：10.37188/OPE.20212905.1008
  CLC： O431.2
- Received：14 October 2020，
  
  Revised：29 October 2020，
  
  Published：15 May 2021
- 稿件说明：
移动端阅览
赵梓栋,杨照华,李高亮.基于测量基优化的低采样率单像素成像[J].光学精密工程,2021,29(05):1008-1013.

ZHAO Zi-dong,YANG Zhao-hua,LI Gao-liang.Sub-Nyquist single-pixel imaging by optimizing sampling basis[J].Optics and Precision Engineering,2021,29(05):1008-1013.
赵梓栋,杨照华,李高亮.基于测量基优化的低采样率单像素成像[J].光学精密工程,2021,29(05):1008-1013. DOI： 10.37188/OPE.20212905.1008.

ZHAO Zi-dong,YANG Zhao-hua,LI Gao-liang.Sub-Nyquist single-pixel imaging by optimizing sampling basis[J].Optics and Precision Engineering,2021,29(05):1008-1013. DOI： 10.37188/OPE.20212905.1008.

摘要

单像素成像结合压缩感知相关算法利用很少一部分无空间分辨的桶探测器采样值就能重构出成像物体的高质量图像。然而，随机地选取投射的散斑序列无法在更低的采样率下成像。为进一步实现单像素成像在极低采样率下的成像效果，本文提出了基于数据驱动的哈达玛矩阵排序方案，利用对整个数据集的训练效果来自适应地选择透射的散斑信号序列，在重构图像过程中使用两种不同的压缩感知相关算法在数值仿真和实验条件下实现了5%超低采样率下对成像物体的图像重构，并和目前最优的哈达玛矩阵排序方案进行了比较，发现在1%~5%的采样率下本文方法的重构效果更优。本文研究成果可用于提升单像素成像的成像速度，在成像制导和医学成像中有着极大的应用前景。

Abstract

Single-pixel imaging combined with compressed sensing can reconstruct high-quality images of an imaged object from a small part of the measurement results of a bucket detector without a spatial resolution. However， at low sampling rates， randomly selected projected speckle sequences limit the quality of reconstructed images. To achieve improved imaging at very low sampling rates， this paper proposes a data-driven Hadamard matrix sorting scheme， which uses the training effect of an entire dataset to adaptively select transmitted speckle signal sequences. In the process of reconstructing an image， two different compressed-sensing-related algorithms are employed to realize the image reconstruction of an imaged object at an ultra-low sampling rate of 5% in a numerical simulation and physical experiment， and it is sorted with the current optimal correlation Hadamard matrix. The schemes are compared， and it is found that the reconstruction effect of the method proposed in this paper is better at sampling rates of 1% to 5%. The research results presented in this paper can be used to increase the imaging speed of single-pixel imaging， and can be applied to fields such as imaging guidance and medical imaging.

关键词

Keywords

references

ROMBERG J . Imaging via compressive sampling ［J］. IEEE Signal Processing Magazine ， 2008 ， 25 （ 2 ）： 14 - 20 .

FERRI F ， MAGATTI D ， LUGIATO L A ， et al . Differential ghost imaging ［J］. Physical Review Letters ， 2010 ， 104 （ 25 ）： 253603 .

YU W K . Super sub-nyquist single-pixel imaging by means of cake-cutting hadamard basis sort ［J］. Sensors （Basel， Switzerland）， 2019 ， 19 （ 19 ）： E4122 .

EDGAR M P ， GIBSON G M ， BOWMAN R W ， et al . Simultaneous real-time visible and infrared video with single-pixel detectors ［J］. Scientific Reports ， 2015 ， 5 ： 10669 .

SUN M J ， MENG L T ， EDGAR M P ， et al . A Russian Dolls ordering of the Hadamard basis for compressive single-pixel imaging ［J］. Scientific Reports ， 2017 ， 7 （ 1 ）： 3464 .

QI W F ， ZHOU J J . Distribution of 0 and 1 in the highest level of primitive sequences over Z/（2 e ）［J］. Science in China ，Ser A， 1997 ， 40 （ 6 ）： 606 - 611 .

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 （ 6 ）： 87 - 94 .

LI M F ， YAN L ， YANG R ， et al . Fast single-pixel imaging based on optimized reordering Hadamard basis ［J］. Acta Physica Sinica ， 2019 ， 68 （ 6 ）： 87 - 94 . （in Chinese）

CZAJKOWSKI K M ， PASTUSZCZAK A ， KOTYŃSKI R . Real-time single-pixel video imaging with Fourier domain regularization ［J］. Optics Express ， 2018 ， 26 （ 16 ）： 20009 - 20022 .

HAMOOD M T ， BOUSSAKTA S . Fast Walsh–hadamard-Fourier transform algorithm ［J］. IEEE Transactions on Signal Processing ， 2011 ， 59 （ 11 ）： 5627 - 5631 .

AGOSTINELLI F ， HOFFMAN M ， SADOWSKI P ， et al . Learning activation functions to improve deep neural networks ［J］. Computer Science ， 2015 ：arXiv： 1412.6830 .

ZHANG Z ， WANG X ， ZHENG G ， et al . Fast Fourier single-pixel imaging via binary illumination ［J］. Scientific Reports ， 2017 ， 7 （ 1 ）： 12029 .

LI CH B . An Efficient Algorithm for Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing ［D］. Houston ： Rice University ， 2010 .

Views

1200

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Passive compressive ghost imaging with low rank clustering

Double hyperchaotic systems combine compressed sensing and Fibonacci transform for color image encryption

Efficient coding and reconstruction for optical remote sensing images

Improved Zigzag traversal and Lorenz chaotic construction of hash

Sparse reconstruction of frequency domain OCT image based on compressed sensing

Related Author

WANG Shiyong

MA Yizhe

DING Xuezhuan

ZHANG Yiming

LEI Teng

WU Yingyue

JIA Jingwen

ZHANG Zhao

Related Institution

Laboratory of Infrared Detection and Imaging Technology， Chinese Academy of Sciences

University of Chinese Academy of Sciences

Shanghai Institute of Technical Physics， Chinese Academy of Sciences

School of Electronic and Information Engineering， University of Science and Technology Liaoning

School of Computer Science and Software Engineering， University of Science and Technology Liaoning

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰