Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

Laser-induced fluorescence (LIF) spectroscopy is employed for plasma diagnosis, necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal. However, direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components. To address this issue, we propose a deconvolution algorithm based on the maximum entropy principle. We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels (signal-to-noise ratio, SNR = 20–80 dB) and measured LIF spectra with Xe as the working fluid. In the typical measured spectrum (SNR = 26.23 dB) experiment, compared with the Gaussian filter and the Richardson–Lucy (R-L) algorithm, the proposed algorithm demonstrates an increase in SNR of 1.39 dB and 4.66 dB, respectively, along with a reduction in the root-mean-square error (RMSE) of 35% and 64%, respectively. Additionally, there is a decrease in the spectral angle (SA) of 0.05 and 0.11, respectively. In the high-quality spectrum (SNR = 43.96 dB) experiment, the results show that the running time of the proposed algorithm is reduced by about 98% compared with the R-L iterative algorithm. Moreover, the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation. In conclusion, the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise, thus highlighting its advantage in LIF spectral deconvolution applications.