Low computational demand stray light correction method for hyperspectral imaging spectrometers

Stray light correction in hyperspectral imaging spectrometers has long been restricted by high computational requirements. This paper presents a low computational demand method, based on the matrix operations for spectrometer stray light correction, using an iterative approach to efficiently correct stray light across both spectral and spatial dimensions. The efficacy of this method is demonstrated through its application to simulated and real images, achieving an overall reduction of stray light by over 50%, with significantly reduced computation time and memory usage compared to the method by Zong et al. based on a sparse matrix [ Appl. Opt. 45 , 1111 ( 2006 ) 10.1364/AO.45.001111 APOPAI 2155-3165 ]. By enabling stray light correction on general-purpose computers, this method enhances affordability and accessibility, promoting broader use and reducing measurement uncertainties in various hyperspectral imaging applications.