Adaptive Operator-Based Spectral Deconvolution With the Levenberg-Marquardt Algorithm

• Main Authors: Chan Huang, Feinan Chen, Yuyang Chang, Lin Han, Shuang Li, Jin Hong
• Format: Article
• Language: English
• Published: SpringerOpen 2019-12-01
• Series: Photonic Sensors
• Subjects: Optical data processing; spectrometer; deconvolution
• Online Access: https://doi.org/10.1007/s13320-019-0571-8

Abstract Spectral distortion often occurs in spectral data due to the influence of the bandpass function of the spectrometer. Spectral deconvolution is an effective restoration method to solve this problem. Based on the theory of the maximum posteriori estimation, this paper transforms the spectral deconvolution problem into a multi-parameter optimization problem, and a novel spectral deconvolution method is proposed on the basis of Levenberg-Marquardt algorithm. Furthermore, a spectral adaptive operator is added to the method, which improves the effect of the regularization term. The proposed methods, Richardson-Lucy (R-L) method and Huber-Markov spectroscopic semi-blind deconvolution (HMSBD) method, are employed to deconvolute the white light-emitting diode (LED) spectra with two different color temperatures, respectively. The correction errors, root mean square errors, noise suppression ability, and the computation speed of above methods are compared. The experimental results prove the superiority of the proposed algorithm.