基于稀疏表示的杂波量化尺度研究
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摘要
光电成像技术的发展,在大大提高光电成像系统分辨率和灵敏度的同时,使得背景杂波逐渐成为影响光电成像系统目标获取性能的主要因素之一。合理准确地量化背景杂波效应,对建立符合光电成像系统外场性能的目标获取性能表征模型有重要的意义。
本文在研究背景杂波的物理实质、基本概念的基础上,通过分析人眼视觉在目标搜索过程中的特性,结合稀疏表示的理论,研究了一种新的背景杂波量化尺度——基于稀疏表示的杂波量化尺度;进而,以Search_2图像数据库为依托,借助相关性分析和误差分析的理论,对所这一新的杂波尺度进行了验证;并在此基础上,对压缩感知理论进行了初步探索,尝试性地建立了一种基于压缩感知的背景杂波量化尺度,并用Search_2图像数据库进行了验证。
本文研究的背景杂波量化尺度以背景特征和目标特征为依据,模拟了人眼视觉探测目标时在特征提取阶段和联合搜索阶段的特性,符合背景杂波的物理意义,预测结果与主观实验的一致性进一步反映了本文所研究的杂波尺度的合理性。
With the development of electro-optical imaging technology, background clutter has become an important factor affecting target acquisition performance of imaging system. It is becoming more and more important to measure the affection of clutter accurately for building a perfect target acquisition performance model.
Based on the analysis of the definition and the physical essence of clutter, its development and the target-searching characteristic of human vision, a novel clutter metric called Sparse-Representation-Based clutter metric is proposed by combining theory of sparse representation with the perception of human vision in target detection. With the use of the relevance analysis and the error analysis, the Search_2 database is used to explain the feasibility of the SRC metric. What’s more, compressed sensing theory is studied followed by the primary proposition of another novel clutter metric called Compressed-Sensing-Based clutter metric.
The clutter metrics proposed in this paper can simulate the characteristic of the process of feature extraction and target researching of human vision reasonably. Besides, they are based on the characteristic of the background and the target, so they can match up to the physical significance of the clutter. Further more, the experience results show that their prediction results correlate well with the detection probability obtained by subjective experiments for human visual systems.
本文在研究背景杂波的物理实质、基本概念的基础上,通过分析人眼视觉在目标搜索过程中的特性,结合稀疏表示的理论,研究了一种新的背景杂波量化尺度——基于稀疏表示的杂波量化尺度;进而,以Search_2图像数据库为依托,借助相关性分析和误差分析的理论,对所这一新的杂波尺度进行了验证;并在此基础上,对压缩感知理论进行了初步探索,尝试性地建立了一种基于压缩感知的背景杂波量化尺度,并用Search_2图像数据库进行了验证。
本文研究的背景杂波量化尺度以背景特征和目标特征为依据,模拟了人眼视觉探测目标时在特征提取阶段和联合搜索阶段的特性,符合背景杂波的物理意义,预测结果与主观实验的一致性进一步反映了本文所研究的杂波尺度的合理性。
With the development of electro-optical imaging technology, background clutter has become an important factor affecting target acquisition performance of imaging system. It is becoming more and more important to measure the affection of clutter accurately for building a perfect target acquisition performance model.
Based on the analysis of the definition and the physical essence of clutter, its development and the target-searching characteristic of human vision, a novel clutter metric called Sparse-Representation-Based clutter metric is proposed by combining theory of sparse representation with the perception of human vision in target detection. With the use of the relevance analysis and the error analysis, the Search_2 database is used to explain the feasibility of the SRC metric. What’s more, compressed sensing theory is studied followed by the primary proposition of another novel clutter metric called Compressed-Sensing-Based clutter metric.
The clutter metrics proposed in this paper can simulate the characteristic of the process of feature extraction and target researching of human vision reasonably. Besides, they are based on the characteristic of the background and the target, so they can match up to the physical significance of the clutter. Further more, the experience results show that their prediction results correlate well with the detection probability obtained by subjective experiments for human visual systems.
引文
[1]常洪花,“光电图像背景杂波的定量表征及其对成像系统目标获取性能的影响,西安电子科技大学博士论文, 2006.
[2]常洪花,张建奇,王晓蕊,“表征光电成像系统性能的两种不同方法,”红外与激光工程, 416-420, 2003.
[3]洪花,张建奇,“基于人眼视觉的红外背景杂波量化技术,”红外技术, 13-18, 2004年.
[4] Honghua Chang, Jianqi Zhang, Xiaorui Wang, Yong Li,“Background clutter and detection-based staring IR seeker performance evaluation”, Proc. SPIE, pp.381-390, 2004.
[5]常洪花,张建奇,“基于机器视觉的红外背景杂波量化技术,”红外技术, 403-407, 2005年
[6] Honghua Chang, Jianqi Zhang,“New metric for clutter affection human target acquisition,”IEEE Trans. On Aerospace and Electronic System, Vol.42, No.1, pp.361-368, Jan. 2006.
[7]何国经,张建奇,刘德连,常洪花,“一种基于结构相似度的杂波尺度,”西安电子科技大学学报(自然科学版), 36(1), 2009.
[8] Honghua Chang, Jianqi Zhang,“Detection Probability and Detection Time Using Clutter Metrics,”Infrared Physics & TechNology, Infrared Physics & TechNology. Vol.51, No.2, pp.83-90, 2007.
[9] Jie Wu, Cui Yang, Jianqi Zhang.“Similarity Quantization: Compressed Sensing Application,”IEEE 2010 International Conference on Audio, 2010, Nov.11.
[10] Yang Cui, Zhang JianQi, Xu Xing, Chang HongHua, He GuoJing,“Quaternion phase-correlation-based clutter metric for color images,”Optical Engineering. Vol. 46, No.12, pp.1-7, December 2007.
[11]常洪花,张建奇,刘鑫“基于CIELAB的彩色图像背景杂波定量表征,”红外与激光工程增刊,Vol.39, pp.643-646,2010.
[12] He Guojing,Zhang Jianqi,Liu Delian,Chang Honghua,“Clutter metric based on the Cramer-Rao lower bound on automatic target recognition,”Applied Optics,Vol.47, No.29, pp.5534-5540, 2008.
[13] Honghua Chang, Jianqi Zhang,“Evaluation of human detection performance using TSSIM clutter metrics,”Optical Engineering. Vol.45, No.9, 2006.
[14] Qian Li, Cui Yang, Jianqi Zhang.“Relative-Target-Complexity-Based Clutter Metric for Human Visual Systems. IEEE 2010 International Conference on Audio, Nov.11, 2010.
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[28] Margaret A. Phillips, S. Richard F. Sims,“A Signal to Clutter Measure for ATR Performance Comparison,”SPIE. Vol.3069, pp.74-81, 1997.
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[32] R. Richter, J.S. Davis and M.J. Duggin,“A radiometric sensor performance model including atmospheric and infrared clutter effects,”SPIE proc. Vol.3062, pp.322-329, 1997.
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[40] Lance M.Kaplan, Eomanin Murenze, Edward Asika, Kameswara Namuduri,“Effect of Signal-to-Clutter Ratio on Template-Based ATR,”SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol.3370, pp.408-419, Apri.l1998.
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[2]常洪花,张建奇,王晓蕊,“表征光电成像系统性能的两种不同方法,”红外与激光工程, 416-420, 2003.
[3]洪花,张建奇,“基于人眼视觉的红外背景杂波量化技术,”红外技术, 13-18, 2004年.
[4] Honghua Chang, Jianqi Zhang, Xiaorui Wang, Yong Li,“Background clutter and detection-based staring IR seeker performance evaluation”, Proc. SPIE, pp.381-390, 2004.
[5]常洪花,张建奇,“基于机器视觉的红外背景杂波量化技术,”红外技术, 403-407, 2005年
[6] Honghua Chang, Jianqi Zhang,“New metric for clutter affection human target acquisition,”IEEE Trans. On Aerospace and Electronic System, Vol.42, No.1, pp.361-368, Jan. 2006.
[7]何国经,张建奇,刘德连,常洪花,“一种基于结构相似度的杂波尺度,”西安电子科技大学学报(自然科学版), 36(1), 2009.
[8] Honghua Chang, Jianqi Zhang,“Detection Probability and Detection Time Using Clutter Metrics,”Infrared Physics & TechNology, Infrared Physics & TechNology. Vol.51, No.2, pp.83-90, 2007.
[9] Jie Wu, Cui Yang, Jianqi Zhang.“Similarity Quantization: Compressed Sensing Application,”IEEE 2010 International Conference on Audio, 2010, Nov.11.
[10] Yang Cui, Zhang JianQi, Xu Xing, Chang HongHua, He GuoJing,“Quaternion phase-correlation-based clutter metric for color images,”Optical Engineering. Vol. 46, No.12, pp.1-7, December 2007.
[11]常洪花,张建奇,刘鑫“基于CIELAB的彩色图像背景杂波定量表征,”红外与激光工程增刊,Vol.39, pp.643-646,2010.
[12] He Guojing,Zhang Jianqi,Liu Delian,Chang Honghua,“Clutter metric based on the Cramer-Rao lower bound on automatic target recognition,”Applied Optics,Vol.47, No.29, pp.5534-5540, 2008.
[13] Honghua Chang, Jianqi Zhang,“Evaluation of human detection performance using TSSIM clutter metrics,”Optical Engineering. Vol.45, No.9, 2006.
[14] Qian Li, Cui Yang, Jianqi Zhang.“Relative-Target-Complexity-Based Clutter Metric for Human Visual Systems. IEEE 2010 International Conference on Audio, Nov.11, 2010.
[15]金伟其,“一代通用组件热像仪静态性能计算软件,”SPTIS,光学技术(增刊),pp.49-53, 1995.
[16] Zhou Yan, Jin Weiqi, Xie Fuzeng, and Zhang Jianyong,“The MRC method for watched imaging system,”Proc.SPIE.5640, pp.227-234, 2004.
[17] D. E. Schmieder, M. R. Weathersby.“Detection performance in clutter with variable resolution,”J. IEEE Trans on Aerospace and Electronic Systems, Vol.19, No.4, pp.622-630, 1983.
[18] T. J.Meitzler, G. R. Gerhart, E. Sohn and H. Singh.“Detection probability using relative clutter in infrared images,”IEEE Transactions on Aerospace and Electronics Systems, Vol.34, No.3, pp.955-962, 1998.
[19] Liviu I. Voicu, Mosleh Uddin and Harley R. Myler,“Clutter modeling in infrared images using genetic programming,”Opt. Eng. Vol.39, No.9, pp.2359–2371, Sep.2000.
[20] S. Nyberg and L.Bohman,“Characterizing low signature targets in background using spatial and spectral features,”SPIE Proc., Vol.5152, pp.139-149, 2003.
[21] A. C. Copeland and M. M. Trivedi,“Computational models for search and discrimination,”Opt. Eng, Vol. 40, No. 9, pp.1885-1895, 2001.
[22] G. Tidhar and S.R. Rotman,“New method of target acquisition in the presence of clutter,”SPIE Pro., Vol.1486, pp.188-199, 1991.
[23] D. L. Wilson,“Image-based contrast-to-clutter modeling of detection,”Opt. Eng. Vol.40, No.9, pp.1852-1857, September 2001.
[24] J. D. Silk,“Statistical variance analysis of clutter scenes and application to a target acquisition test,”IDA Paper P-2950, November 1995.
[25] G. Tidhar, G. Reiter, Z. Avital, Y. Hadar, and S.R. Rotmam,“Modeling human search and target acquisition performance: IV,”Detection probability in the cluttered environment. Opt. Eng., Vol.33, pp. 801-808, 1994.
[26] S. R. Rotmam, D. Hsu, A. Cohen, D. Shamay and M. L. Kowalczyk,“Texture metrics for clutter affecting human target acquisition”IR Physics &Technology,Vol.37, pp.667-674, 1996.
[27] Yang Cui, Zhang Jian Qi, Xu Xing, Chang HongHua, He GuoJing,“Quaternion phase-correlation-based clutter metric for color images,”Optical Engineering, Vol. 46, No.12. pp.1-7, December 2007.
[28] Margaret A. Phillips, S. Richard F. Sims,“A Signal to Clutter Measure for ATR Performance Comparison,”SPIE. Vol.3069, pp.74-81, 1997.
[29] S. Richard F. Sims,“Putting ATR performance on an equal basis– The measurement of knowledge base distortion and relevant clutter,”SPIE, Vol.4050, pp.55-60, 2000.
[30] Thomas J. Meitzler, Labib Arafeh, Grant R. Gerhart,“Fuzzy logic approach for computing the probability of target detection in cluttered scenes,”Opt. Eng. Vol.35, No.12, pp.3623-3636, December 1996.
[31] Abhijit Mahalanobis, S. Richard F. Sims, Alan Van Nevel,“Signal-to clutter measure for measuring automatic target recognition performance using complimentary eigenvalue distribution analysis,”Opt. Eng. Vol.42, No.4, pp.1144-1151, April 2003.
[32] R. Richter, J.S. Davis and M.J. Duggin,“A radiometric sensor performance model including atmospheric and infrared clutter effects,”SPIE proc. Vol.3062, pp.322-329, 1997.
[33] Z.Avital, Y.Hadar, G. Reiter, G. Tidhar, and S.R Rotmam,“The influence of clutter on human target acquisition,”SPIE Proc., Vol.1971, pp.436-446, 1992.
[34] G. Tidhar and Z. Zalevsky,“Performance evaluation methodology of missile warning and IRST systems,”SPIE proc. Vol. 3436, pp. 891-891, 1998.
[35] J. D. McGlynn and D. J. Sofianos,“Parametric Model-Based Characterization of IR Clutter,”SPIE Proc., Vol. 2470,pp.236-244, 1998.
[36] R. D. Brewer, J.V. Richard, John D. McGlynn, and D. J. Sofianos,“An IR seeker/sensor dynamic performance prediction model,”SPIE proc. Vol. 2470, pp.89-97, 1995.
[37] K.Gillian, Groves, and M. Kim,“Quantification of clutter in EO tracking systems,”SPIE Proceedings, Vol.2221, pp.287-295, 1994.
[38] A.Toet, P.Bijl, and J.M.Valeton,“Image dataset for testing search and detection models,”Opt.Eng. Vol.40, pp.1760-1767,2001.
[39] A.Toet, P.Bijl, J.M.Valeton, and F.L.Kooi,“A high-resolution image data set for testing search and detection models,”Report no. TM-98-A020, TNO HumanFactors Research Institude, Mar.1998.
[40] Lance M.Kaplan, Eomanin Murenze, Edward Asika, Kameswara Namuduri,“Effect of Signal-to-Clutter Ratio on Template-Based ATR,”SPIE Conference on Algorithms for Synthetic Aperture Radar Imagery V, Vol.3370, pp.408-419, Apri.l1998.
[41] W. R. Reynolds,“Toward quantifying infrared clutter”, Proc. SPIE, 1311, pp: 232-240, 1990.
[42] B. Bhanu,“Automatic target recognition: state of the art survey,”IEEE Transactions on Aerospace and Electronics System, Vol.22, No.4, July 1986.
[43] Aaron D.Lanterman, Joseph A, O’Sullivan, Michael l.Miller,“Kullback-Lerbler Distance for quantifying clutter and models,”Opt.Eng. Vol.38, No.12, pp.2134-2146, Dec. 1999.
[44] David Donoho, Compressed sensing. IEEE Trans. on Information Theory, Vol.52, No.4, pp. 1289 - 1306, April 2006.
[45] Emmanuel Candès,“Compressive sampling,”Int. Congress of Mathematics, Vol.3, pp. 1433-1452, Madrid, Spain, 2006.
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