Band narrowing with sparsity regularization for spectroscopic data

Spectroscopic data often suffers from common problems of bands overlap and random Gaussian noise. Spectral resolution can be improved by mathematically removing the effect of the instrument response function (IRF). In this paper, a novelty model is proposed to deconvolute the measured spectrum with the sparsity regularization. The proposed model is solved by iteratively reweighted least square method. The major novelty of the proposed method is that it can estimate the IRF and latent spectrum simultaneously. Experimental results with actual Raman spectra manifest that this algorithm can recover the overlap peaks as well as suppress the noise effectively.