Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 673 |
| Fachzeitschrift | Scientific reports |
| Jahrgang | 15 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 3 Jan. 2025 |
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.
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in: Scientific reports, Jahrgang 15, Nr. 1, 673, 03.01.2025.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data
AU - Zabic, Miroslav
AU - Reifenrath, Michel
AU - Wegner, Charlie
AU - Bethge, Hans
AU - Landes, Timm
AU - Rudorf, Sophia
AU - Heinemann, Dag
N1 - Publisher Copyright: © The Author(s) 2025.
PY - 2025/1/3
Y1 - 2025/1/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85214101065&partnerID=8YFLogxK
U2 - 10.1038/s41598-024-84790-6
DO - 10.1038/s41598-024-84790-6
M3 - Article
AN - SCOPUS:85214101065
VL - 15
JO - Scientific reports
JF - Scientific reports
SN - 2045-2322
IS - 1
M1 - 673
ER -