Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data

verfasst von

Miroslav Zabic, Michel Reifenrath, Charlie Wegner, Hans Bethge, Timm Landes, Sophia Rudorf, Dag Heinemann

Abstract

Hyperspectral imaging (HSI) systems acquire images with spectral information over a wide range of wavelengths but are often affected by chromatic and other optical aberrations that degrade image quality. Deconvolution algorithms can improve the spatial resolution of HSI systems, yet retrieving the point spread function (PSF) is a crucial and challenging step. To address this challenge, we have developed a method for PSF estimation in HSI systems based on computed wavefronts. The proposed technique optimizes an image quality metric by modifying the shape of a computed wavefront using Zernike polynomials and subsequently calculating the corresponding PSFs for input into a deconvolution algorithm. This enables noise-free PSF estimation for the deconvolution of HSI data, leading to significantly improved spatial resolution and spatial co-registration of spectral channels over the entire wavelength range.

Organisationseinheit(en)

Hannoversches Zentrum für Optische Technologien (HOT)
Institut für Gartenbauliche Produktionssysteme
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme
Institut für Zellbiologie und Biophysik

Externe Organisation(en)

Haip Solutions GmbH

Typ

Artikel

Journal

Scientific reports

Band

15

ISSN

2045-2322

Publikationsdatum

03.01.2025

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Allgemein

Elektronische Version(en)

https://doi.org/10.1038/s41598-024-84790-6 (Zugang: Offen)