Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data
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 retriev...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-84790-6 |
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| author | Miroslav Zabic Michel Reifenrath Charlie Wegner Hans Bethge Timm Landes Sophia Rudorf Dag Heinemann |
| author_facet | Miroslav Zabic Michel Reifenrath Charlie Wegner Hans Bethge Timm Landes Sophia Rudorf Dag Heinemann |
| author_sort | Miroslav Zabic |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-26e0091bcdc84fb482ee9a74d8ff892b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-26e0091bcdc84fb482ee9a74d8ff892b2025-01-05T12:14:56ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-84790-6Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging dataMiroslav Zabic0Michel Reifenrath1Charlie Wegner2Hans Bethge3Timm Landes4Sophia Rudorf5Dag Heinemann6Hannover Centre for Optical Technologies (HOT), Leibniz University HannoverHannover Centre for Optical Technologies (HOT), Leibniz University HannoverHannover Centre for Optical Technologies (HOT), Leibniz University HannoverHannover Centre for Optical Technologies (HOT), Leibniz University HannoverHannover Centre for Optical Technologies (HOT), Leibniz University HannoverInstitute of Cell Biology and Biophysics, Leibniz University HannoverHannover Centre for Optical Technologies (HOT), Leibniz University HannoverAbstract 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.https://doi.org/10.1038/s41598-024-84790-6 |
| spellingShingle | Miroslav Zabic Michel Reifenrath Charlie Wegner Hans Bethge Timm Landes Sophia Rudorf Dag Heinemann Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data Scientific Reports |
| title | Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data |
| title_full | Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data |
| title_fullStr | Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data |
| title_full_unstemmed | Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data |
| title_short | Point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data |
| title_sort | point spread function estimation with computed wavefronts for deconvolution of hyperspectral imaging data |
| url | https://doi.org/10.1038/s41598-024-84790-6 |
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