Polarization imaging based on compressed sensing theory

Wang Peng; Rong Zhibin; He Junhua; Lv Pei

doi:10.3788/IRLA201645.0228005

Polarization imaging technology is a method that acquires the object images by collecting the polarization information of the target radiation or reflected signals. In particular, compared with the light intensity detection, it has unique advantages in the artificial target detection and surface recognition. Due to the short range and low quality of the conventional polarization imaging in complex imaging environment, a new kind of polarization imaging technology based on compressed sensing was proposed. The basic principle of compressed sensing theory was elaborated. By constructing reasonable sampling matrix and reconstruction algorithm, the specific imaging system was designed. Besides, the feasibility of this technology was confirmed through the imaging experiment. The study results in the air show the system can reconstruct the polarization images of the pre-positioned target. Additionally, in the existing experimental conditions, some measures are investigated and proposed to improve the system imaging performance.

Polarization imaging based on compressed sensing theory

1. Xi'an Institute of Optics and Precision Mechanics of CAS,Xi'an 710119,China;

2. University of Chinese Academic of Sciences,Beijing 100049,China;

3. The Chinese People's Liberation Army 91550 Troops,Dalian 116023,China

Received Date: 2015-06-08

Rev Recd Date: 2015-07-10

Publish Date: 2016-02-25

Key words:

Abstract: Polarization imaging technology is a method that acquires the object images by collecting the polarization information of the target radiation or reflected signals. In particular, compared with the light intensity detection, it has unique advantages in the artificial target detection and surface recognition. Due to the short range and low quality of the conventional polarization imaging in complex imaging environment, a new kind of polarization imaging technology based on compressed sensing was proposed. The basic principle of compressed sensing theory was elaborated. By constructing reasonable sampling matrix and reconstruction algorithm, the specific imaging system was designed. Besides, the feasibility of this technology was confirmed through the imaging experiment. The study results in the air show the system can reconstruct the polarization images of the pre-positioned target. Additionally, in the existing experimental conditions, some measures are investigated and proposed to improve the system imaging performance.

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Reference (33)

[1]
[2]	Li Hailan, Wang Xia, Zhang Chuntao, et al. The development and analysis of target detection research based on polarization imaging technology[J]. Optical Technique, 2009, 35(5):695-700.(in Chinese)李海兰, 王霞, 张春涛, 等. 基于偏振成像技术的目标探测研究进展及分析[J]. 光学技术, 2009, 35(5):695-700.
[3]	Zhang Chaoyang, Cheng Haifeng, Chen Zhaohui, et al. Polarimetric imaging of camouflage screen in visible and infrared wave hand[J]. Infrared and Laser Engineering, 2009, 38(3):424-427.(in Chinese)张朝阳, 程海峰, 陈朝晖, 等. 伪装遮障的光学与红外偏振成像[J]. 红外与激光工程, 2009, 38(3):424-427.
[4]
[5]	Gong Jieqiong, Zhan Haigang, Liu Dazhao. A review on polarization information in the remote sensing detection[J]. Spectroscopy and Spectral Analysis, 2010, 30(4):1088-1095.(in Chinese)弓洁琼, 詹海刚, 刘大召. 遥感遥测中偏振信息的研究发展[J]. 光谱学与光谱分析, 2010, 30(4):1088-1095.
[6]
[7]	Yao Tianfu, Zhu Jing, Fan Ye, et al. Usage of polarization characteristic of laser beam in underwater target imaging detection[J]. Laser Optoelectronics Progress, 2010, 47(6):061402.(in Chinese)姚天甫, 朱靖, 樊烨, 等. 激光偏振特性用于水下目标探测[J]. 激光与光电子学进展, 2010, 47(6):061402.
[8]
[9]	Zhao Qingliang, Wei Huajiang, Guo Zhouyi, et al. Application of polarized reflectance spectroscopy in biomedical photonics[J]. Laser Optoelectronics Progress, 2009, 46(10):78-87.(in Chinese)赵庆亮, 魏华江, 郭周义, 等. 偏振反射光谱在生物医学光子学中的应用[J]. 激光与光电子学进展, 2009, 46(10):78-87.
[10]
[11]
[12]	Walker J G, Chang Peter C Y, Hopcraft K I. Visibility depth improvement in active polarization imaging in scattering media[J]. Applied Optics, 2000, 39(27):4933-4941.
[13]	Candes E J, Tao T. Near optimal signal recovery from random projections:Universal encoding strategies[J]. IEEE Transactions on Info Theory, 2006, 52(12):5406-5425.
[14]
[15]
[16]	Candes E J, Tao T. Decoding by linear programming[J]. IEEE Transactions on Info Theory, 2005, 51(12):4203-4215.
[17]
[18]	Donoho D L. For most large under determined systems of linear equations the minimal norm solution is also the sparsest solution[J]. Communication on Pure and Applied Mathematics, 2006, 59(6):797-829.
[19]	Bregman L M. The method of successive project for analysis and filtering of complex wavelets for shift invariant analysis and filtering of signals[J]. Applied and Computational Harmonic Analysis, 2001, 10(3):234-253.
[20]
[21]
[22]	Daubechies I, Defrise M, Mol C D. An iterative thresholding algorithm for linear inverse problems with least squares[J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4):606-617.
[23]
[24]	Mallat S. A Wavelet Tour of Signal Processing[M]. San Diego:Academic Press, 1999.
[25]	Bajwa W, Haupt J, Sayeed A, at al. Joint source-channel communication for distributed estimation in sensor networks[J]. IEEE Transactions Info Theory, 2007, 53(10):3629-3653.
[26]
[27]
[28]	Varshney K R, Cetin M, Fisher J W, et al. Sparse representation in structured to synthetic aperture radar[J]. IEEE Transaction Signal Processing, 2008, 56(8):3548-3561.
[29]
[30]	Lustig M, Donoho D L, Pauly J M. Sparse MRI:the application of compressed sensing for rapid MR imaging[J]. Magnetic Resonance in Medicine, 2007, 58(6):1182-1195.
[31]	Zhang Cheng, Yang Hairong, Cheng Hong, et al. Image super-resolution reconstruction based on compressed sensing[J]. Journal of OptoelectronicsLaser, 2013, 24(4):805-811.(in Chinese)张成, 杨海蓉, 程鸿, 等. 基于压缩感知的超分辨率图像重建[J]. 光电子激光, 2013, 24(4):805-811.
[32]
[33]	Gan L. Block compressed sensing of natural images[C]//Proceedings of the 15th International Conference on Digital Signal Processing, 2007:403-406.

Polarization imaging based on compressed sensing theory

doi: 10.3788/IRLA201645.0228005

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