L2−Lq reconstruction model for computational spectrometer. (April 2023)

• Record Type: Journal Article
• Authors: Xu, Jia-Jia; Xiao, You; Wang, Zeng-Qi; Li, Hao
• Is Part Of: Applied mathematics letters. Volume 138(2023)
• Publication Date: 2023-04
• Subjects: Computational spectrum reconstruction -- L2–Lq regularization -- Linear regression -- Non-convex optimization; Applied mathematics -- Periodicals; 519.05
• DOI: 10.1016/j.aml.2022.108509
• Languages: English
• ISSNs: 0893-9659

Abstract

Abstract: The extensive application of computational strategies in spectral detection enables optics-free spectrometers, promoting the miniaturization of spectrometers. It reconstructs the spectral composition of the incident light based on the measurement results from a set of detectors and a known spectral response matrix. The spectral reconstruction model and its simulation are key to identifying the incident light components with high precision. In this study, we reconstructed the incident light function by discretizing the integral equation and applying L 2 − L q optimization approach to obtain the spectral reconstruction model. Simulation results show that L 2 − L q optimization has higher resolution and higher noise tolerance than certain other optimization approaches, which is advantageous for identifying unknown optical signals with a broad spectrum and high dynamic range.