Noise reduction of spatial heterodyne interferogram based on two-dimensional frequency domain analysis

Spatial heterodyne spectroscopy is a new Fourier transform spectroscopy technique, which is widely used in the field of atmospheric remote sensing because of its high sensitivity and high resolution. The interferogram data obtained by the space heterodyne spectrometer is often accompanied by noise interference, which makes the measurement results appear to be wrong. Based on this, we proposed a spatial heterodyne interferogram noise reduction method based on 2D frequency domain filtering. By analyzing the characteristics of measured 2D spectrum diagram of potassium lamp and the simulated ideal 2D spectrum diagram, the threshold data was introduced to construct a specific filter, so as to separate the effective signal and the noise signal, and compare with the average spectral data of the interferogram before and after noise reduction. The application effect of the treatment method was evaluated. The proposed method in this paper was applied to the noise reduction processing of the interferograms from 3 light sources: potassium lamp (quasi-monochromatic light), xenon lamp, and water vapor (continuous light). The results show that after noise reduction of the potassium lamp interferogram, the characteristic peaks in its transformed spectrum are highlighted and the surrounding noise is significantly reduced. After noise reduction of the xenon lamp and water vapor interferograms, the dark spots in the interferograms are suppressed and the non-uniformity is improved. By comparing the spectra of 3 rows before and after correction with the average spectrum, the root mean square errors decreased by 80.61%, 76.21%, 78.45% and 78.59%, 80.04%, 87.72%, respectively; the peak signal-to-noise ratios increased by 44.74%, 38.58%, 41.03% and 39.10%, 41.98%, 54.59%, respectively. Compared with the results of the rotation filtering algorithm and the wavelet algorithm, the root mean square error of the xenon lamp is optimized by 68.08% and 54.36%, and the peak signal-to-noise ratio is optimized by 27.84% and 16.32%. The corresponding values for water vapor are 78.79%, 80.40% and 38.83%, 41.56%. The noise reduction effect is obvious, indicating that the algorithm proposed is feasible for the application of noise reduction in space heterodyne spectral data.