在光谱数据的定量分析中, 噪声的存在常常会影响结果的准确性。 为提高红外光谱分析精度, 需要对光谱数据进行去噪处理。 将一种光滑阈值函数和一种分层阈值选取方法应用到提升小波域光谱信号的去噪处理中, 并对提升小波重构信号进行中值滤波。 对某小麦品种的实测光谱信号, 添加信噪比为21.17 dB的噪声后采用该方法进行去噪处理, 并利用信噪比(SNR)、 均方根误差(RMSE)、 峰值平均相对误差(AREPV)以及峰位平均误差(AEPP)四项指标对去噪效果进行评价。 结果表明, 与软阈值法与硬阈值法相比, 该方法能更有效地去除光谱信号中的噪声, 保留光谱中的有用信息, 提高光谱信噪比, 降低均方根误差、 峰值平均相对误差以及峰位平均误差, 提高光谱的分析能力。

Infrared spectra are often corrupted by noise, which may greatly influence the accuracy and precision of the analytical result. To improve the analytical precision, the authors need to denoise the spectrum data first. In the present paper, a spectrum denoising method by the second generation wavelet transform domain thresholding combined with the median filtering is introduced. The spectrum of a certain kind of wheat was used to test the performance of the proposed denoising method. In the experiment, noise with signal to noise ratio 21.17 dB was first added to the spectrum, and then removed by the proposed denoising method. The signal to noise ratio (SNR), the root mean square error(RMSE), the average relative error of the peak value (AREPV) and the average error of the peak position (AEPP) were used to evaluate the performance of the proposed denoising method. Experimental results show that the proposed method can remove the spectrum noise and keep the useful information more effective than Donoho’s soft and hard threshold method. At the same time, it can achieve a higher PSNR, a lower RMSE, a lower AREPV and a lower AEPP than the other two denoising methods.