应用压缩传感理论的单像素相机成像系统

陈涛; 李正炜; 王建立; 王斌; 郭爽

doi:10.3788/OPE.20122011.2523

您当前的位置：

首页 >

文章列表页 >

应用压缩传感理论的单像素相机成像系统

信息科学 | 更新时间：2020-08-12

- 应用压缩传感理论的单像素相机成像系统
- Imaging system of single pixel camera based on compressed sensing
- 光学精密工程 2012年20卷第11期页码：2523-2530
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院大学北京,中国,100049
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目(No.2011AA8082035)()
- DOI：10.3788/OPE.20122011.2523
  中图分类号： TN27;TP391
- 收稿日期：2012-06-07，
  
  修回日期：2012-06-25，
  
  纸质出版日期：2012-11-10
- 稿件说明：
移动端阅览
陈涛, 李正炜, 王建立, 王斌, 郭爽. 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

CHEN Tao, LI Zheng-wei, WANG Jian-li, WANG Bin, GUO Shuang. Imaging system of single pixel camera based on compressed sensing[J]. Editorial Office of Optics and Precision Engineering, 2012,20(11): 2523-2530
陈涛, 李正炜, 王建立, 王斌, 郭爽. 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 Zheng-wei, WANG Jian-li, WANG Bin, GUO Shuang. Imaging system of single pixel camera based on compressed sensing[J]. Editorial Office of Optics and Precision Engineering, 2012,20(11): 2523-2530 DOI： 10.3788/OPE.20122011.2523.

摘要

采用稀疏二进制矩阵作为测量矩阵完成了单像素相机对不同场景的拍摄。基于压缩传感理论设计的单像素相机

利用数字微镜阵列和单个探测元件实现高分辨率图像的拍摄

将图像采集和压缩合二为一

减少了数据

降低了系统规模、复杂度和成本。对测量矩阵进行了分析

给出了二进制测量矩阵精确重构条件。搭建了硬件实验平台

采用稀疏二进制测量矩阵

对笔划复杂度不同的"中"字、"国"字和复杂实物这3种不同的场景进行了拍摄实验

利用梯度投影重构算法重构出目标图像

图像分辨率为数字微镜阵列大小。实验结果显示

对于3种不同场景

在测量次数为目标图像像素总数的20%~30%时

能精确重构出目标图像

表明单像素相机能实现高分辨率图像的拍摄。

Abstract

A single pixel camera was used to shoot different scenes by taking a sparse binary matrix as the measurement matrix. The single pixel camera based on compressed sensing theory could acquire a high resolution image by using a Digital Micromirror Device (DMD) and a single detector. It captured and compressed the picture simultaneously

so that the data amount

scale

complexity and the cost of the system were reduced greatly. In this paper

the effect of measurement matrix upon the precise of reconstructed picture was analyzed

and the condition to reconstruct the binary matrix for object picture precisely was given. An experimental platform was established and imaging experiments under three different scenes were performed for different strokes of the words "China" and complex real objects using sparse binary measurement matrix. The picture with the picture resolution of as large as the size of the DMD was reconstructed by gradient projection algorithm. Experiment results indicate that single pixel camera could reconstruct the image precisely when the number of measurements are 20%~30% of the total number of pixels for the object image. It concludes that the single pixel camera can acquire high resolution images.

关键词

Keywords

references

DONOHO D L.Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.[2] CANDS E, 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.[3] CANDS E. Compressive sampling. Proceedings of International Congress of Mathematicians, 2006: 1433-1452.[4] 刘欣悦,董磊,王建立. 稀疏采样傅里叶望远镜成像[J]. 光学精密工程,2010,8(3): 521-527. LIU X Y, DONG L, WANG J L. Fourier telescopy imaging via sparse sampling[J]. Opt. Precision Eng., 2010,18(3): 521-527. (in Chinese)[5] 朱明,高文,郭立强. 压缩感知理论在图像处理领域的应用[J]. 中国光学,2011,4(5): 441-447. ZHU M, GAO W, GUO L Q. Application of compressed sensing theory in image processing[J]. Chinese Opt., 2011, 4(5): 411-447. (in Chinese)[6] 郭军伟. 应用CS理论实现同步采样压缩成像[J]. 中国光学, 2009, 2(6): 525-530. GUO J W. Imaging system of synchronous sample and compression based on CS theory[J]. Chinese Opt., 2011, 4(5): 411-447. (in Chinese)[7] TAKHAR D, LASKA J N, WAKIN M, et al.. A new compressive imaging camera architecture using opticaldomain compression[J]. Proc. IS&T/SPIE Symposium on Electronic Imaging: Computational Imaging, 2006, 6065: 43-52.[8] CHAN W L, CHARAN K, TAKHAR D, et al.. A single-pixel terahertz imaging system based on compressed sensing[J]. Appl. Phys. Let., 2008, 93: 121105-121105-3.[9] MA J W. Single-Pixel Remote Sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(2): 199-203.[10] MA J W. A single-pixel imaging system for remote sensing by two-step iterative curvelet thresholding[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(4): 676-680.[11] FU W X, ZHU S P, ZHANG K. Conceptual research on application of single pixel infrared imaging to missile guidance. Asia-Pacific Conference on Information Processing, 2009: 185-188.[12] FILIIPE M, FRANCISCO M A, MIGUEL V C, et al.. Active illumination single-pixel camera based on compressive sensing[J]. Applied Optics, 2011, 50(4): 405-414.[13] HE Z X, TAKAHIRO O, HASEYAMA M. The simplest measurement matrix for compressed sensing of natural images. Proceedings of 2010 IEEE 17th International Conference on Image Processing September, 2010: 4301-4303.[14] M'ARIO A T F, ROBERT D N, STEPHEN J W. Gradient projection for sparse reconstruction: Application to Compressed Sensing and Other Inverse Problems[J].IEEE Journal of Selected Topics in Signal Processing, 2007,1: 586-597.

浏览量

361

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

双臂对称性对压缩传感用于关联成像重构的影响

压缩传感用于极弱光计数成像