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Computational Imaging Prediction of Starburst-Effect Diffraction Spikes

When imaging bright light sources, rays of light emanating from their centres are commonly observed; this ubiquitous phenomenon is known as the starburst effect. The prediction and characterization of starburst patterns formed by extended sources have been neglected to date. In the present study, we...

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Detalles Bibliográficos
Autores principales: Lendermann, Markus, Tan, Joel Shi Quan, Koh, Jin Ming, Cheong, Kang Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240111/
https://www.ncbi.nlm.nih.gov/pubmed/30446668
http://dx.doi.org/10.1038/s41598-018-34400-z
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author Lendermann, Markus
Tan, Joel Shi Quan
Koh, Jin Ming
Cheong, Kang Hao
author_facet Lendermann, Markus
Tan, Joel Shi Quan
Koh, Jin Ming
Cheong, Kang Hao
author_sort Lendermann, Markus
collection PubMed
description When imaging bright light sources, rays of light emanating from their centres are commonly observed; this ubiquitous phenomenon is known as the starburst effect. The prediction and characterization of starburst patterns formed by extended sources have been neglected to date. In the present study, we propose a novel trichromatic computational framework to calculate the image of a scene viewed through an imaging system with arbitrary focus and aperture geometry. Diffractive light transport, imaging sensor behaviour, and implicit image adjustments typical in modern imaging equipment are modelled. Characterization methods for key optical parameters of imaging systems are also examined. Extensive comparisons between theoretical and experimental results reveal excellent prediction quality for both focused and defocused systems.
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spelling pubmed-62401112018-11-26 Computational Imaging Prediction of Starburst-Effect Diffraction Spikes Lendermann, Markus Tan, Joel Shi Quan Koh, Jin Ming Cheong, Kang Hao Sci Rep Article When imaging bright light sources, rays of light emanating from their centres are commonly observed; this ubiquitous phenomenon is known as the starburst effect. The prediction and characterization of starburst patterns formed by extended sources have been neglected to date. In the present study, we propose a novel trichromatic computational framework to calculate the image of a scene viewed through an imaging system with arbitrary focus and aperture geometry. Diffractive light transport, imaging sensor behaviour, and implicit image adjustments typical in modern imaging equipment are modelled. Characterization methods for key optical parameters of imaging systems are also examined. Extensive comparisons between theoretical and experimental results reveal excellent prediction quality for both focused and defocused systems. Nature Publishing Group UK 2018-11-16 /pmc/articles/PMC6240111/ /pubmed/30446668 http://dx.doi.org/10.1038/s41598-018-34400-z Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Lendermann, Markus
Tan, Joel Shi Quan
Koh, Jin Ming
Cheong, Kang Hao
Computational Imaging Prediction of Starburst-Effect Diffraction Spikes
title Computational Imaging Prediction of Starburst-Effect Diffraction Spikes
title_full Computational Imaging Prediction of Starburst-Effect Diffraction Spikes
title_fullStr Computational Imaging Prediction of Starburst-Effect Diffraction Spikes
title_full_unstemmed Computational Imaging Prediction of Starburst-Effect Diffraction Spikes
title_short Computational Imaging Prediction of Starburst-Effect Diffraction Spikes
title_sort computational imaging prediction of starburst-effect diffraction spikes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6240111/
https://www.ncbi.nlm.nih.gov/pubmed/30446668
http://dx.doi.org/10.1038/s41598-018-34400-z
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