Abstract：Spectrum denoising is an important part of spectrum detection. As we know, spectral signal is susceptible to thermal noise, mechanical vibration on site and random noise, etc. However, online monitoring systems require to reduce the impact of parameter selection caused by human operation on denoising, so a method based on singular value decomposition is proposed to denoise spectrum signal. An improved effective singular value selection method is also proposed. First, the author specify the maximum peak of the difference spectrum of singular value for the lower bound which named θ1, using the integrated information of singular value and its difference spectrum to select the upper bound, which is called θ2. The interval θ1～θ2 is defined as a fuzzy area. Then, the membership is obtained with Fuzzy C-means clusting and corresponding weight coefficients to the singular values in the fuzzy area are given. Finally, the proposed method is used to denoise UV spectrum signal with different signal to noise ratio. The signal to noise ratio, root mean square error, normalied correlation coefficient and smoothness radio are used to evaluate the result of denoising. The result shows that: based on data-driven, the proposed method has a good denoising effect, which can effectively restore the original signal.
Key words：Spectra denoising;Singular value decomposition;Fuzzy C-means clusting
代荡荡1，王先培1*，赵 宇1，田 猛1，龙嘉川1，朱国威1，张龙飞2 . 一种改进的奇异值降噪阶次选取方法用于紫外光谱信号去噪的研究 [J]. 光谱学与光谱分析, 2016, 36(07): 2139-2143.
DAI Dang-dang1, WANG Xian-pei1*, ZHAO Yu1, TIAN Meng1, LONG Jia-chuan1, ZHU Guo-wei1, ZHANG Long-fei2 . Research on Denoising Ultraviolet Spectrum Signal with An Improved Effective Singular Value Selection Method. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(07): 2139-2143.
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