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光谱信号乘性加性混合随机噪声去除方法

Denoising Method of Spectral Signal with Multiplicative and Additive Mixed Random Noises

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摘要

提出一种光谱信号噪声的乘性加性混合分析模型,并采用维纳滤波和同态滤波相结合的算法对光谱信号进行去噪处理。仿真结果表明,该算法比移动平均算法、最小均方算法和递归最小均方算法具有更好的去噪性能。实验结果表明,氙灯光谱信号中的噪声符合乘性加性混合模型。与移动平均算法、最小均方算法和递归最小均方算法相比,从该算法处理后的汞灯光谱信号中能够提取更加稳定的谱峰谷位置、谱峰幅度、谱峰半峰全宽等特征值,定量分析时能获得更好的结果。

Abstract

An analysis model for mixed multiplicative and additive noise of spectral signal is built, and an algorithm combining Wiener filtering and homomorphic filtering is proposed to denoise the spectral signal. Simulation results show that the proposed algorithm has better performance than moving average algorithm, least mean square algorithm and recursive least square algorithm. Experimental results indicate that the noise in xenon lamp spectral signal matches the mixed multiplicative and additive model. Compared with moving average algorithm, least mean square algorithm, and recursive least squares algorithm, after the mercury lamp spectral signal is processed by the proposed algorithm, more stable characteristic spectral parameters can be extracted, such as peak and valley locations, peak amplitude and full width at half maximum. Better result can be obtained in quantitative analysis with the proposed algorithm.

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中图分类号:O433.4;TP391

DOI:10.3788/aos201737.0730001

所属栏目:光谱学

基金项目:政府间国际科技创新合作重点专项(2016YFE0110600)、浙江省公益技术研究社会发展项目(2015C33087)、上海市国际科技合作基金项目(16520710500)

收稿日期:2017-01-22

修改稿日期:2017-02-26

网络出版日期:--

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陈正伟:中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800中国科学院大学, 北京 100049浙江科技学院工程训练中心, 浙江 杭州310023
张 方:中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800
周 扬:浙江科技学院工程训练中心, 浙江 杭州310023
黄惠杰:中国科学院上海光学精密机械研究所信息光学与光电技术实验室, 上海 201800中国科学院大学, 北京 100049

联系人作者:陈正伟(czw19831983@163.com)

备注:陈正伟(1983-),男,博士研究生,主要从事光学生物传感技术方面的研究。

【1】Lan Jiming, Xiong Gang, Zhang Haiyan. Adaptive moving average filtering algorithm weighting on amplitude and phase bivariate distance[J]. Computer Engineering and Applications, 2012, 48(10): 141-145.
蓝集明, 熊 刚, 张海燕. 幅相二元距离加权的自适应滑动平均滤波[J]. 计算机工程与应用, 2012, 48(10): 141-145.

【2】Han Qingyang, Zhou Pengji. Research on the method of eliminating noise and background in the meantime in detecting ethanol contention based on Raman spectra[J]. Spectroscopy and Spectral Analysis, 2015, 35(12): 3406-3409.
韩庆阳, 周鹏骥. 乙醇含量拉曼光谱检测中噪声与背景同时消除方法研究[J]. 光谱学与光谱分析, 2015, 35(12): 3406-3409.

【3】Xia Liangping, Li Huadong, Yin Shaoyun, et al. Eliminating complex background noise of Raman spectrum based on configuration similarity comparing method[J]. Acta Optica Sinica, 2013, 33(5): 0530003.
夏良平, 李华栋, 尹韶云, 等. 基于形状相似性比较法消除拉曼光谱的复杂背景噪声[J]. 光学学报, 2013, 33(5): 0530003.

【4】Xia Ruobin. Data acquisition and digital signal processing of CCD spectrometer[D]. Hangzhou: Zhejiang University, 2007: 56-77.
夏若彬. CCD光谱测量系统的数据采集及信号数字化处理[D]. 杭州: 浙江大学, 2007: 56-77.

【5】Chen Cong, Lu Qipeng, Peng Zhongqi. Preprocessing methods of near-infrared spectrum based on NLMS adaptive filtering[J]. Acta Optica Sinica, 2012, 32(5): 0530001.
陈 丛, 卢启鹏, 彭忠琦. 基于NLMS自适应滤波的近红外光谱去噪处理方法研究[J]. 光学学报, 2012, 32(5): 0530001.

【6】Lu Qipeng, Chen Cong, Peng Zhongqi. Application of adaptive filter to noninvasive biochemical examination by near infrared spectroscopy[J]. Optics and Precision Engineering, 2012, 20(4): 873-879.
卢启鹏, 陈 丛, 彭忠琦. 自适应滤波在近红外无创生化分析中的应用[J]. 光学 精密工程, 2012, 20(4): 873-879.

【7】Diniz P S R. Adaptive filtering: algorithms and practical implementation[M]. 3th ed. New York: Springer, 2008: 77-227.

【8】Sun Yi. Research on signal denoising technology based on adaptive lifting wavelet transform[D]. Hefei: University of Science and Technology of China, 2008: 23-37.
孙 轶. 基于自适应提升小波的信号去噪技术研究[D]. 合肥: 中国科学技术大学, 2008: 23-37.

【9】Om H, Biswas M. An adaptive image denoising method based on local parameters optimization[J]. Sadhana, 2014, 39(4): 879-900.

【10】Meher S K, Singhawat B. An improved recursive and adaptive median filter for high density impluse noise[J]. AEU-International Journal of Electronics and Communications, 2014, 68(12): 1173-1179.

【11】Zhou Tao. Study on the real-time monitoring method of flue gas by DOAS[D]. Tianjin: Tianjin University, 2008: 33-50.
周 涛. 烟气排放紫外差分吸收光谱实时监测方法的研究[D]. 天津: 天津大学, 2008: 33-50.

【12】Li Xiaojie. Method research and system design for testing photoelectric parameters of CCD[D]. Beijing: University of Chinese Academy of Sciences, 2014: 47-64.
李晓杰. CCD光电参数测试方法研究及系统设计[D]. 北京: 中国科学院大学, 2014: 47-64.

【13】Lu D Y. A hybrid optimization method for multiplicative noise and blur removal[J]. Journal of Computational and Applied Mathematics, 2016, 302: 224-233.

【14】Rodrigues P M, Freitas D, Teixeirab J P. Electroencephalogram cepstral distances in Alzheimer''s disease diagnosis[J]. Procedia Computer Science, 2015, 64: 879-884.

【15】Wu Decao, Wei Biao, Feng Peng, et al. Denoising algorithm of UV-Vis spectroscopy on water quality detection based on two-dimension restructuring and dynamic pane[J]. Spectroscopy and Spectral Analysis, 2016, 36(4): 1044-1050.
吴德操, 魏 彪, 冯 鹏, 等. 基于二维重组和动态窗格的水质检测紫外-可见光谱去噪算法[J]. 光谱学与光谱分析, 2016, 36(4): 1044-1050.

【16】Yan Jun. The application of the modified Wiener filtering in the seismic data processing[D]. Changchun: Jilin University, 2007: 27-40.
颜 军. 改进的维纳滤波在地震资料处理中的应用[D]. 长春: 吉林大学, 2007: 27-40.

【17】Wang Shutao, Zeng Qiuju, Song Haobing, et al. Signal denoising method based on the SVM filter absorption methane detection[J]. Chinese J Lasers, 2014, 41(9): 0915001.
王书涛, 曾秋菊, 宋浩兵, 等. 基于SVM滤波器的吸收式甲烷检测的信号去噪方法[J]. 中国激光, 2014, 41(9): 0915001.

【18】Cui Xiaojie. The application of Wiener filtering[D]. Xi′an: Chang′an University, 2006: 2-24.
崔晓杰. 维纳滤波的应用研究[D]. 西安: 长安大学, 2006: 2-24.

【19】Mohan J, Krishnaveni V, Guo Y. MRI denoising using nonlocal neutrosophic set approach of Wiener filtering[J]. Biomedical Signal Processing and Control, 2013, 8: 779-791.

【20】Gao Hongxia, Wu Lixuan, Xu Han, et al. Denoising method of micro-focus X-ray images corrupted with mixed multiplicative and additive noises[J]. Optics and Precision Engineering, 2014, 22(11): 3100-3113.
高红霞, 吴丽璇, 徐 寒, 等. 微焦点X射线图像乘性加性混合噪声的去除[J]. 光学 精密工程, 2014, 22(11): 3100-3113.

【21】Lui Jieli. Research on spectral line profile and line width of high-resolution (saturation) molecular spectra and its application[D]. Wuhan: Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, 2000: 15-31.
林洁丽. 高分辨(饱和)分子光谱谱线线型、线宽及其应用的研究[D]. 武汉: 中国科学院武汉物理与数学研究所, 2000: 15-31.

【22】GB/T 26798-2011 Single beam UV visible spectrophotometer[S]. Standardization Administration of the People′s Republic of China, 2011: 1-14.
GB/T 26798-2011 单光束紫外可见分光光度计[S]. 中国国家标准管理委员会, 2011: 1-14.

【23】Wang Shutao, Chen Dongying, Wang Xinglong, et al. Detection of polycyclic aromatic hydrocarbons combining fluorescence analysis with ABC-BP neural network[J]. Chinese J Lasers, 2015, 42(11): 1115001.
王书涛, 陈东营, 王兴龙, 等. 荧光分析法和ABC-BP 神经网络相结合的多环芳烃的检测[J]. 中国激光, 2015, 42(11): 1115001.

【24】Fu Bo, Hu Yongxiang, Liu Rong, et al. Near-infrared measurement with medium concentration sample as reference[J]. Acta Optica Sinica, 2016, 36(2): 0230003.
傅 博, 胡永翔, 刘 蓉, 等. 基于中等浓度样品参考测量的近红外光谱检测方法[J]. 光学学报, 2016, 36(2): 0230003.

引用该论文

Chen Zhengwei,Zhang Fang,Zhou Yang,Huang Huijie. Denoising Method of Spectral Signal with Multiplicative and Additive Mixed Random Noises[J]. Acta Optica Sinica, 2017, 37(7): 0730001

陈正伟,张 方,周 扬,黄惠杰. 光谱信号乘性加性混合随机噪声去除方法[J]. 光学学报, 2017, 37(7): 0730001

被引情况

【1】蔡剑华,肖永良,黎小琴. 基于广义S变换和奇异值分解的近红外光谱去噪. 光学学报, 2018, 38(4): 430005--1

【2】黄乐,吴功平,叶旭辉. 输电线巡检机器人弱光条件下的障碍物识别研究. 光学学报, 2018, 38(9): 915006--1

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