基于稀疏采样的关联成像算法研究

量子电子学报

基于稀疏采样的关联成像算法研究

孟文文1*，张家民2，时东锋2

(1合肥南极星环保科技有限公司安徽合肥 230000) (2中国科学院安徽光学精密机械研究所中国科学院大气光学重点实验室安徽合肥 230031)

出版日期:2019-01-28 发布日期:2019-01-17
通讯作者: 651783209@qq.com
作者简介:孟文文（1986-），女，山东泰安人，硕士，算法工程师，主要从事数字信号处理算法方面的研究工作。 E-mail:651783209@qq.com
基金资助:
Supported by National Natural Science Foundation of China (国家自然科学基金, 11404344）

Study of Correlated Imaging with Sparse Sampling

Meng Wenwen1*，Zhang Jiamin2，Shi Dongfeng2

(1 Hefei South Star Environmental Projection Technology Co., Ltd Anhui HF 230000) (2 Key Laboratory of Atmospheric Optics, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, Anhui 230031, China)

Published:2019-01-28 Online:2019-01-17

摘要/Abstract

摘要： 传统关联成像系统中对物体全部信息进行采样，而根据压缩感知理论可知绝大部分物体信息在某些变换下具有稀疏特性，因此对物体信息进行稀疏采样也可以复原出完整的物体信息。提出了利用关联成像对物体信息进行稀疏采样的方法，采用成像系统获取物体稀疏信息，再使用压缩感知算法对完整物体信息进行复原。对所提方法进行了实验研究，结果证实了使用稀疏采样能有效减少关联成像的数据量，提高系统的成像效率和质量。

关键词: 图像处理, 关联成像, 压缩感知, 稀疏采样

Abstract: For traditional correlated imaging system, the imaging scene is completely sampled by the illumination speckles. According to the compression theory, most of the object information has sparse characteristics under some transformations, and it can be recovered exactly from a relatively small number of samples. Here, a correlated imaging system with sparse speckles is proposed. Imaging scene is illuminated by sparse speckles, and the information of compressed scene is acquired by compressive imaging system. Finally, the complete scene information is accurately restored using compression sensing algorithm. The experimental results show that sparse speckles can effectively reduce the amount of data to improve the imaging efficiency and the quality of the imaging system.

Key words: image processing, correlated imaging, compression sensing, sparse sampling

孟文文1*，张家民2，时东锋2. 基于稀疏采样的关联成像算法研究[J]. 量子电子学报.

Meng Wenwen1*，Zhang Jiamin2，Shi Dongfeng2. Study of Correlated Imaging with Sparse Sampling[J]. Chinese Journal of Quantum Electronics.

参考文献

[1] Baraniuk R. G., Compressive sensing [J]. IEEE Signal Process. Mag. 2007, 24:118–121. [2] Shi Guangming, Liu Danhua, Gao Dahua, et al. Advances in Theory and Application of Compressed Sensing [J].Acta Electronica Sinica (电子学报), 2009,05:1070-1081. (in Chinese). [3] Dai Qionghai, Fu Changjun, Ji Xiangyang. Research on Compressed Sensing [J]. Chinese Journal of computers (计算机学报), 2011,03:3425-3434. (in Chinese). [4] Shao Wenze, Wei Zhihui, Advances and Perspectives on Compressed Sensing Theory [J]. Journal of Image and Graphics (中国图象图形学报), 2012, 01:1-12. (in Chinese). [5] Duarte, M. F. et al. Single-pixel imaging via compressive sampling [J]. IEEE Signal Process. Mag. 2008, 25:83–91. [6] Chen Tao, Li Zhengwei, Wang Jianli, et al. Imaging system of single pixel camera based on compressed sensing [J]. Optics and Precision Engineering (光学精密工程), 2012, 11:2523-2530. (in Chinese). [7] CHAN W L, CHARAN K, TAKHAR D, et al. A single-pixel terahertz imaging system based on compressed sensing [J]. Appl Phys Lett, 2008, 12:93-95. [8] SUN B, EDGAR M P, BOWMAN R, et al. 3D Computational Imaging with Single-Pixel Detectors [J]. Science, 2013, 340(6134): 844-847. [9] Radwell N. et al. Single-pixel infrared and visible microscope, Optica, 2014, 1:285-290. [10] Li Ming-Fei, Mo Xiao-Fan, Zhao Lian-Jie, Single-pixel remote imaging based on Walsh-Hadamard transform[J].Acta Physica Sinica (物理学报), 2016, 65(6): 64201-064201. (in Chinese). [11] Yu Wen-kai, Yao Xu-ri, Liu Xue-feng, et al. Compressed sensing for ultra-weak light counting imaging [J]. Optics and Precision Engineering (光学精密工程), 2012, 20(10):2283-2292. (in Chinese). [12] Bai Xu, Li Yong-Qiang, Zhao Sheng-Mei, Differential compressive correlated imaging [J], Acta Physica Sinica (物理学报), 2013, 04:206-213. (in Chinese). [13] Zhang Shuo, Wang Jie, Wang Jincheng, et al. Simple Calculation Method for Three-Dimensional Imaging Based on Compressed Sensing [J]. Acta Optica Sinica (光学学报), 2013,01:84-90. (in Chinese). [14] Ma Yanpeng, Wang Yanan, Wang Yikun, et al. Study of Single-Pixel Detection Computational Imaging Technology Based on Compressive Sensing [J]. Acta Optica Sinica (光学学报), 2013,01:84-90. [15] Chen jing, Wang Yongtian. Research of the Compressive Imaging Technology [J]. Laser & Optoelectronics Progress (激光与光电子学进展), 2012, 03:15-22. (in Chinese). [16] Guo Shu-xu, Zhang Chi, Cao Jun-sheng, et al. Object reconstruction by compressive sensing based on normalized ghost imaging [J]. Optics and Precision Engineering (光学精密工程), 2015, 23(1): 288-294. (in Chinese). [17] Lu Minghai, Shen Xia, Han Shensheng. Ghost Imaging via Compressive Sampling Based on Digital Micromirror Device [J]. Acta Optica Sinica (光学学报), 2011, 07:105-110. (in Chinese).