Open-Path Fourier Transform Infrared Spectrum De-Noising Based on Improved Threshold Lifting Wavelet Transform and Adaptive Filter

The major component of atmospheric pollutants is volatile organic compounds (VOCs), Fourier transform infrared spectroscopy (FTIR) is a widely used VOCs on-line measurement method at the present stage. The infrared spectrum obtained by open path (OP-FTIR) is easily to be polluted by various noise. Therefore, the development of effective and rapid methods to remove the noise in infrared spectrum is a crucial problem in the research of on-line atmospheric real-time monitoring system. The lifting wavelet transform (LWT) has the advantages of simple structure, and low computation; the least mean square algorithm (LMS) adaptive filter has the performance of automatically adjusts parameters to achieve optimal filtering. From above algorithm performance we proposes a infrared spectroscopy denoising algorithm combined with improved threshold LWT denoising and LMS adaptive filter. The algorithm first uses improved threshold LWT denoising preserve more spectral information and then uses the LWT decomposition of the high-frequency coefficients to reconstruct the noise correlation signal. Take this noise as the reference input of the LMS adaptive filter, the final denoising signal is effective for the removal of the noise signal overlap with spectral spectrum. In the experimental part, the standard infrared spectrum plus noise and the measured infrared spectrum of open optical channel over Hefei city were denoised respectively, the results show that the signal-to-noise ratio of the spectral signal processed by the proposed algorithm is about 3dB higher than that of the traditional soft threshold wavelet denoising. The root mean square error (RSME) is also reduced by about 30%, and the running time is reduced by 46% or so. Experimental results show that the algorithm is simple and fast in operation, and has important practical significance for the real-time noise elimination system of atmospheric environment monitoring.