光谱去噪是光谱检测的重要环节。 针对光谱信号易受光谱仪热噪声、 现场机械振动以及随机噪声等因素影响, 而在线监测系统要求减少人为参数选择对去噪效果的影响, 提出利用奇异值分解(SVD)理论对光谱信号去噪。 提出一种改进的降噪阶次选取方法: 指定奇异值差分谱最大峰值点θ１为所选阶次下界; 利用奇异值、 奇异值差分谱综合信息选取阶次上界θ２; 将区间θ１～θ２定义为模糊区域, 通过模糊C均值聚类求取隶属度, 赋予模糊区域内奇异值相应的权重系数。 用所提方法对不同信噪比下SO2紫外光谱信号去噪, 将信噪比、 均方根误差、 波形相似系数、 平滑度指标用于去噪效果的评价。 去噪结果表明: 所提方法完全基于数据驱动, 具有较好的去噪效果, 能够真实的恢复原始信号。

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.