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基于小波变换的压缩感知理论对水质检测紫外-可见光谱数据的去噪研究

Research on Denoising of UV-Vis Spectral Data for Water Quality Detection with Compressed Sensing Theory Based on Wavelet Transform

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摘要

消除噪声影响对提高直接光谱法水质检测系统的测量稳定性和精度都具有重要意义。 直接光谱法在线水质检测系统通常采用长寿命、 无需预热的脉冲氙灯和适用于复杂检测环境的工业级光谱探测装置。 针对整个光谱探测系统受到光源、 光路和光电转换器件的严重影响, 测定的光谱数据含有大量噪声这一实际问题, 提出了基于小波变换的压缩感知去噪算法, 并与传统小波阈值去噪方法进行了比较实验。 针对化学需氧量为200 mg·L-1的邻苯二甲酸氢钾标液的紫外-可见光谱数据进行去噪处理, 采用压缩感知去噪算法, 将信号在小波域内分解, 得到含噪高频系数; 采用随机高斯矩阵作为压缩感知算法的观测矩阵, 压缩比设置为2, 对高频系数进行观测; 选择正交匹配追踪算法恢复高频小波系数的稀疏性, 从而达到去噪目的。 同时针对传统的小波阈值去噪, 采用daubechies4作为小波基的软阈值滤波方法对光谱数据进行去噪处理。 为验证去噪算法的可行性, 采集某溪水和城市生活污水的光谱信号分别采用以上两种方法进行去噪处理, 实验结果表明: 基于小波变换的压缩感知去噪算法适用于紫外-可见光谱法在线水质检测系统, 该方法能在保留水样原始光谱信号的吸收特征的前提下有效地去噪, 且去噪效果优于小波阈值去噪算法。 与小波阈值去噪算法相比, 信噪比提高了12.201 5 dB, 均方根误差减小了0.009 3, 峰值信噪比增加了5.299 dB。 不仅避免了小波阈值去噪过程中阈值的选取依靠主观判断问题, 而且在重构过程中有效地抑制了噪声, 为直接光谱法检测水质参数提供了一种新的解决方案。

Abstract

It is of great significance to improve the measurement stability and accuracy of water quality detection system with direct spectrum method. Direct spectroscopy on-line water quality detection systems typically use long-lived, preheated pulsed xenon lamps and industrial-grade spectral detection devices for complex inspection environments. Since the whole spectral detection system is affected by the light source, the optical path and the photoelectric conversion device, the measured spectral data contains a large amount of noise, a wavelet denoising algorithm based on compressed sensing is proposed, which is compared with the traditional wavelet threshold denoising method. In this paper, the denoising was performed on the UV-Vis spectra of the standard solution of potassium hydrogen phthalate with chemical oxygen demand of 200 mg·L-1. The compressed sensing algorithm is used to decompose the signal in the wavelet domain, and the high frequency coefficients are obtained. Using the random Gaussian matrix as the observation matrix of the compression sensing algorithm, the compression ratio is set to 2, and the high frequency coefficients are observed. The orthogonal matching algorithm is used to recover the sparsity of the high frequency wavelet coefficients to achieve the denoising. At the same time, for the traditional wavelet threshold denoising, the soft-threshold filtering method is used to denoise the spectral data, and the wavelet base is daubechies 4. In order to verify the feasibility of the noise reduction algorithm, the spectral signals of a stream and domestic sewage were collected, and the above two methods were used to denoise the spectral signal. The experimental results show that the compressed sensing algorithm based on wavelet transform is suitable for the on-line water quality detection system based on UV-Vis spectroscopy. The method can effectively denoise under the premise of preserving the absorption characteristics of the original spectral signal of the water sample, and the denoising effect is better than the wavelet threshold denoising algorithm. Compared with the wavelet threshold denoising algorithm, the SNR is increased by 12.201 5 dB, the RMSE is neduced by 0.009 3, and the PSNR is increased by 5.299 dB. The proposed method not only avoids the problem of threshold selection in wavelet threshold denoising, but also effectively suppresses the noise in the reconstruction process. This method provides a new solution for direct spectroscopy to detect water quality parameters.

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中图分类号:X593

DOI:10.3964/j.issn.1000-0593(2018)03-0844-07

基金项目:国家自然科学基金项目(61401049), 重庆市教委科学技术研究项目(KJ1709201)资助

收稿日期:2017-03-28

修改稿日期:2017-07-19

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赵明富:重庆理工大学现代光电检测技术与仪器重点实验室, 重庆 400054重庆理工大学光纤传感与光电检测重庆市重点实验室, 重庆 400054
唐 平:重庆理工大学现代光电检测技术与仪器重点实验室, 重庆 400054重庆理工大学光纤传感与光电检测重庆市重点实验室, 重庆 400054
汤 斌:重庆理工大学现代光电检测技术与仪器重点实验室, 重庆 400054重庆理工大学光纤传感与光电检测重庆市重点实验室, 重庆 400054重庆大学光电技术及系统教育部重点实验室, 重庆 400044
何 鹏:重庆大学光电技术及系统教育部重点实验室, 重庆 400044
徐杨非:重庆理工大学现代光电检测技术与仪器重点实验室, 重庆 400054重庆理工大学光纤传感与光电检测重庆市重点实验室, 重庆 400054
邓思兴:重庆理工大学现代光电检测技术与仪器重点实验室, 重庆 400054重庆理工大学光纤传感与光电检测重庆市重点实验室, 重庆 400054
石胜辉:重庆理工大学现代光电检测技术与仪器重点实验室, 重庆 400054重庆理工大学光纤传感与光电检测重庆市重点实验室, 重庆 400054

联系人作者:赵明富(1469273789@qq.com)

备注:赵明富, 1964年生, 重庆理工大学现代光电检测技术与仪器重点实验室教授

【1】TANG Bin, WEI Biao, WU De-cao(汤 斌, 魏 彪, 吴德操). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(11): 3020.

【2】ZHOU Shi-long, GONG De-ren, DUAN Deng-ping(周世龙, 龚德仁, 段登平). Sensor and Micro Systems(传感器与微系统), 2015, 34(3): 154.

【3】TANG Bin, WEI Biao, MAO Ben-jiang(汤 斌, 魏 彪, 毛本将). Progress in Lasers and Optoelectronics(激光与光电子学进展), 2014(4): 197.

【4】Feng Z, Liang M, Chu F. Mechanical Systems and Signal Processing, 2013, 38(1): 165.

【5】Al-Qazzaz N K, Ali S, Ahmad S A, et al. Biomedical Engineering and Sciences (IECBES), 2014 IEEE Conference on. IEEE, 2014. 214.

【6】Zhu W, Chen B X. Multidimensional Systems and Signal Processing, 2015, 26(1): 113.

【7】CHEN Yu-shan, ZHANG Xiong-wei, YANG Ji-bin(陈栩杉, 张雄伟, 杨吉斌). Acta Automatica Sinica(自动化学报), 2015, 42(3): 335.

【8】Feng W, Fengwei C, Jia W. Open Electrical & Electronic Engineering Journal, 2015, 9: 74.

【9】ZHANG Jia-yan, HE Wei-ji, CHEN Qian(庄佳衍, 何伟基, 陈 钱). Chinese Journal of Optics(光子学报), 2015, 44(12): 70.

【10】TIAN Wen-biao, KANG Jian, ZHANG Yang(田文飚, 康 健, 张 洋). Electronic Journal(电子学报), 2014, 42(6): 1061.

【11】LI Hui-juan, GONG Xian-yong, LI Ying-cheng(李会娟, 巩现勇, 李英成). Science of Surveying and Mapping(测绘科学), 2014, 39(4): 131.

【12】Metzler C A, Maleki A, Baraniuk R G. IEEE Transactions on Information Theory, 2016, 62(9): 5117.

【13】Wang R, Yang Z, Liu L, et al. ACM Transactions on Graphics (TOG), 2014, 33(2): 18.

【14】Lopes M E. IEEE Transactions on Information Theory, 2016, 62(9): 5145.

【15】Majumdar A, Ansari N, Aggarwal H, et al. Signal Processing, 2016, 119: 136.

【16】WANG Qiang, LI Jia, SHEN Yi(王 强, 李 佳, 沈 毅). Chinese Journal of Electronics(电子学报), 2013, 41(10): 2041.

【17】Ma Q, Quan X, Zhong Y, et al. Cogent Engineering, 2016, 3(1): 1247611.

【18】YUAN Qin, WU Xuan-gou, XIONG Yan(袁 琴, 吴宣够, 熊 焰). Computer Science(计算机科学), 2014, 41(3): 314.

引用该论文

ZHAO Ming-fu,TANG Ping,TANG Bin,HE Peng,XU Yang-fei,DENG Si-xing,SHI Sheng-hui. Research on Denoising of UV-Vis Spectral Data for Water Quality Detection with Compressed Sensing Theory Based on Wavelet Transform[J]. Spectroscopy and Spectral Analysis, 2018, 38(3): 844-850

赵明富,唐 平,汤 斌,何 鹏,徐杨非,邓思兴,石胜辉. 基于小波变换的压缩感知理论对水质检测紫外-可见光谱数据的去噪研究[J]. 光谱学与光谱分析, 2018, 38(3): 844-850

被引情况

【1】贾良权,祁亨年,胡文军,赵光武,阚瑞峰,高璐,郑雯,许琴. 采用TDLAS技术的玉米种子活力快速无损分级检测. 中国激光, 2019, 46(9): 911002--1

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