Abstract

The spectroscopic data recorded by a dispersion spectrophotometer are usually degraded by the response function of the instrument. To improve the resolving power, double or triple cascade spectrophotometers and narrow slits have been employed, but the total flux of the radiation available decreases accordingly, resulting in a lower signal-to-noise ratio (SNR) and a longer measurement time. However, the spectral resolution can be improved by mathematically removing the effect of the instrument response function. A high-order statistical Gauss–Newton algorithm is proposed to blindly deconvolve the measured spectroscopic data. The true spectrum and the instrument response function are estimated simultaneously. Experiments on artificial and real measured spectroscopic data demonstrate the feasibility of this method.

High-order cumulant-based blind deconvolution of Raman spectra

Jinghe Yuan, Ziqiang Hu, and Jinzuo Sun
Appl. Opt. 44(35) 7595-7601 (2005)

Estimation of spectral slit width and blind deconvolution of spectroscopic data by homomorphic filtering

Yasuhiro Senga, Keiichiroh Minami, Satoshi Kawata, and Shigeo Minami
Appl. Opt. 23(10) 1601-1608 (1984)

High order statistics based blind deconvolution of bi-level images with unknown intensity values

Jeongtae Kim and Soohyun Jang
Opt. Express 18(12) 12872-12889 (2010)

Richardson–Lucy blind deconvolution of spectroscopic data with wavelet regularization

Hai Liu, Zhaoli Zhang, Sanya Liu, Tingting Liu, Luxin Yan, and Tianxu Zhang
Appl. Opt. 54(7) 1770-1775 (2015)

Blind image deconvolution using the zero-lag slice of higher-order statistics

Wenkai Lu
Opt. Lett. 31(12) 1839-1841 (2006)