Multi-order blind deconvolution algorithm with adaptive Tikhonov regularization for infrared spectroscopic data

Infrared spectra often suffer from common problems of bands overlap and random noise. In this paper, we introduce a blind spectral deconvolution method to recover the degraded infrared spectra. Firstly, we present an analysis of the causes of band-side artifacts found in current deconvolution methods, and model the spectral noise with the multi-order derivative that are inspired by those analysis. Adaptive Tikhonov regularization is employed to preserve the spectral structure and suppress the noise. Then, an effective optimization scheme is described to alternate between IRF estimation and latent spectrum until convergence. Numerical experiments demonstrate the superior performance of the proposed method comparing with the traditional methods.