Spectral blind deconvolution with differential entropy regularization for infrared spectrum

Estimating an accuracy spectrum from a linearly degraded and noisy data record is often desirable in infrared spectroscopy application. In this paper, we proposed a new framework which can estimate the desired spectrum and the instrument function simultaneously from the degraded data. Firstly, the instrument function is parametrically represented as a Lorentzian function for the first time. Then, we construct the cost functional with the DE regularization, and minimize the functional to obtain the desired spectrum and instrument function. It has been found that the program is very useful to determine accurate line widths and peak positions from degraded spectral spectrum. Comparative results including quantitative and qualitative analyses manifest that the proposed method outperforms the conventional methods on peak narrowing and noise suppression.