Discrete Spectrum Reconstruction using Integral Approximation Algorithm
For spectroscopists, this method improves spectral resolution without solving nonlinear equations, but it is an incremental improvement over existing deconvolution techniques.
The paper addresses the inverse problem of restoring discrete spectra from observed data affected by a spectrometer's line spread function, proposing an integral approximation algorithm that avoids nonlinear equations. Numerical examples on synthetic and experimental spectra demonstrate effective resolution enhancement.
An inverse problem in spectroscopy is considered. The objective is to restore the discrete spectrum from observed spectrum data, taking into account the spectrometer's line spread function. The problem is reduced to solution of a system of linear-nonlinear equations (SLNE) with respect to intensities and frequencies of the discrete spectral lines. The SLNE is linear with respect to lines' intensities and nonlinear with respect to the lines' frequencies. The integral approximation algorithm is proposed for the solution of this SLNE. The algorithm combines solution of linear integral equations with solution of a system of linear algebraic equations and avoids nonlinear equations. Numerical examples of the application of the technique, both to synthetic and experimental spectra, demonstrate the efficacy of the proposed approach in enabling an effective enhancement of the spectrometer's resolution.