红外与激光工程, 2016, 45 (2): 0228005, 网络出版: 2016-04-05   

基于压缩感知的偏振光成像技术研究

Polarization imaging based on compressed sensing theory
作者单位
1 中国科学院西安光学精密机械研究所,陕西 西安 710119
2 中国科学院大学,北京 100049
3 中国人民解放军91550部队,辽宁 大连 116023
摘要
偏振成像技术是一种基于目标自身辐射或反射信号中所包含的偏振信息获取物体图像的方法,尤其在人工目标的探测和表面识别方面,相对光强度探测方式具有独特的优势。针对传统的偏振成像技术在复杂的成像环境中成像距离短和成像质量差的缺点,提出了一种基于压缩感知的新型偏振光成像技术。阐述了压缩感知理论的基本原理,构造了合适的采样矩阵和重构算法,设计了具体的成像系统,并通过压缩感知偏振成像实验证明了该成像技术的可行性。空气中实验结果表明,该成像系统能够重构出预先放置目标靶的偏振图像。此外在现有的实验条件基础上讨论并提出了几种改进系统成像质量的措施。
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.
参考文献

[1] 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.

    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)

[2] 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.

    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)

[3] 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.

    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)

[4] 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.

    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)

[5] 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.

    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)

[6] 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.

[7] 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.

[8] Candes E J, Tao T. Decoding by linear programming [J]. IEEE Transactions on Info Theory, 2005, 51(12): 4203-4215.

[9] 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.

[10] 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.

[11] 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.

[12] Mallat S. A Wavelet Tour of Signal Processing[M]. San Diego: Academic Press, 1999.

[13] 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.

[14] 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.

[15] 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.

[16] 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.

    Zhang Cheng, Yang Hairong, Cheng Hong, et al. Image super-resolution reconstruction based on compressed sensing [J]. Journal of Optoelectronics Laser, 2013, 24(4): 805-811.(in Chinese)

[17] Gan L. Block compressed sensing of natural images[C]// Proceedings of the 15th International Conference on Digital Signal Processing, 2007: 403-406.

王朋, 荣志斌, 何俊华, 吕沛. 基于压缩感知的偏振光成像技术研究[J]. 红外与激光工程, 2016, 45(2): 0228005. Wang Peng, Rong Zhibin, He Junhua, Lv Pei. Polarization imaging based on compressed sensing theory[J]. Infrared and Laser Engineering, 2016, 45(2): 0228005.

本文已被 5 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!