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A Spectral Hazy Image Database
We introduce a new database to promote visibility enhancement techniques intended for spectral image dehazing. SHIA (Spectral Hazy Image database for Assessment) is composed of two real indoor scenes M1 and M2 of 10 levels of fog each and their corresponding fog-free (ground-truth) images, taken in...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340961/ http://dx.doi.org/10.1007/978-3-030-51935-3_5 |
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author | El Khoury, Jessica Thomas, Jean-Baptiste Mansouri, Alamin |
author_facet | El Khoury, Jessica Thomas, Jean-Baptiste Mansouri, Alamin |
author_sort | El Khoury, Jessica |
collection | PubMed |
description | We introduce a new database to promote visibility enhancement techniques intended for spectral image dehazing. SHIA (Spectral Hazy Image database for Assessment) is composed of two real indoor scenes M1 and M2 of 10 levels of fog each and their corresponding fog-free (ground-truth) images, taken in the visible and the near infrared ranges every 10 nm starting from 450 to 1000 nm. The number of images that form SHIA is 1540 with a size of [Formula: see text] pixels. All images are captured under the same illumination conditions. Three of the well-known dehazing image methods based on different approaches were adjusted and applied on the spectral foggy images. This study confirms once again a strong dependency between dehazing methods and fog densities. It urges the design of spectral-based image dehazing able to handle simultaneously the accurate estimation of the parameters of the visibility degradation model and the limitation of artifacts and post-dehazing noise. The database can be downloaded freely at http://chic.u-bourgogne.fr. |
format | Online Article Text |
id | pubmed-7340961 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73409612020-07-08 A Spectral Hazy Image Database El Khoury, Jessica Thomas, Jean-Baptiste Mansouri, Alamin Image and Signal Processing Article We introduce a new database to promote visibility enhancement techniques intended for spectral image dehazing. SHIA (Spectral Hazy Image database for Assessment) is composed of two real indoor scenes M1 and M2 of 10 levels of fog each and their corresponding fog-free (ground-truth) images, taken in the visible and the near infrared ranges every 10 nm starting from 450 to 1000 nm. The number of images that form SHIA is 1540 with a size of [Formula: see text] pixels. All images are captured under the same illumination conditions. Three of the well-known dehazing image methods based on different approaches were adjusted and applied on the spectral foggy images. This study confirms once again a strong dependency between dehazing methods and fog densities. It urges the design of spectral-based image dehazing able to handle simultaneously the accurate estimation of the parameters of the visibility degradation model and the limitation of artifacts and post-dehazing noise. The database can be downloaded freely at http://chic.u-bourgogne.fr. 2020-06-05 /pmc/articles/PMC7340961/ http://dx.doi.org/10.1007/978-3-030-51935-3_5 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article El Khoury, Jessica Thomas, Jean-Baptiste Mansouri, Alamin A Spectral Hazy Image Database |
title | A Spectral Hazy Image Database |
title_full | A Spectral Hazy Image Database |
title_fullStr | A Spectral Hazy Image Database |
title_full_unstemmed | A Spectral Hazy Image Database |
title_short | A Spectral Hazy Image Database |
title_sort | spectral hazy image database |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7340961/ http://dx.doi.org/10.1007/978-3-030-51935-3_5 |
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