Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing

Under foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of th...

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Autores principales:	Haoqiang Wu, Yiran Fu, Quanxing Zha, Aidong Chen, Hongyuan Jing
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/51b25d01fff946649d52438cdc74a1da
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spelling	oai:doaj.org-article:51b25d01fff946649d52438cdc74a1da2021-11-29T00:56:53ZRegional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing1875-919X10.1155/2021/3377905https://doaj.org/article/51b25d01fff946649d52438cdc74a1da2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/3377905https://doaj.org/toc/1875-919XUnder foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of the image to decrease and sometimes introduce halos in the sky area. To solve this problem, we proposed a region similarity optimisation algorithm based on a dark channel prior. First, a vector comprising RGB layer dark channel value was obtained as the original atmospheric ambient light, and then, the proposed regional similarity linear function was used to adjust the atmospheric ambient light matrix. Next, the transmittance of different colour channels was derived and the multichannel soft matting algorithm was employed to produce more effective transmittance. Finally, the atmospheric ambient light and transmittance were substituted into the atmospheric scattering model to calculate clean images. Experimental results show that the proposed algorithm outperformed the existing mainstream dehazing algorithms in terms of both visual judgement and quality analysis with nonhomogeneous haze datasets. The algorithm not only improves the image details but also improves the brightness and saturation of the dehazing result; therefore, the proposed algorithm is effective in the restoration of the hazy image.Haoqiang WuYiran FuQuanxing ZhaAidong ChenHongyuan JingHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Haoqiang Wu Yiran Fu Quanxing Zha Aidong Chen Hongyuan Jing Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing
description	Under foggy and other severe weather conditions, image acquisition equipment is not effective. It often produces an image with low contrast and low scene brightness, which is difficult to use in other image-based applications. The dark channel prior dehazing algorithm will cause the brightness of the image to decrease and sometimes introduce halos in the sky area. To solve this problem, we proposed a region similarity optimisation algorithm based on a dark channel prior. First, a vector comprising RGB layer dark channel value was obtained as the original atmospheric ambient light, and then, the proposed regional similarity linear function was used to adjust the atmospheric ambient light matrix. Next, the transmittance of different colour channels was derived and the multichannel soft matting algorithm was employed to produce more effective transmittance. Finally, the atmospheric ambient light and transmittance were substituted into the atmospheric scattering model to calculate clean images. Experimental results show that the proposed algorithm outperformed the existing mainstream dehazing algorithms in terms of both visual judgement and quality analysis with nonhomogeneous haze datasets. The algorithm not only improves the image details but also improves the brightness and saturation of the dehazing result; therefore, the proposed algorithm is effective in the restoration of the hazy image.
format	article
author	Haoqiang Wu Yiran Fu Quanxing Zha Aidong Chen Hongyuan Jing
author_facet	Haoqiang Wu Yiran Fu Quanxing Zha Aidong Chen Hongyuan Jing
author_sort	Haoqiang Wu
title	Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing
title_short	Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing
title_full	Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing
title_fullStr	Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing
title_full_unstemmed	Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing
title_sort	regional atmospheric light optimisation algorithm for heterogeneous image dehazing
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/51b25d01fff946649d52438cdc74a1da
work_keys_str_mv	AT haoqiangwu regionalatmosphericlightoptimisationalgorithmforheterogeneousimagedehazing AT yiranfu regionalatmosphericlightoptimisationalgorithmforheterogeneousimagedehazing AT quanxingzha regionalatmosphericlightoptimisationalgorithmforheterogeneousimagedehazing AT aidongchen regionalatmosphericlightoptimisationalgorithmforheterogeneousimagedehazing AT hongyuanjing regionalatmosphericlightoptimisationalgorithmforheterogeneousimagedehazing
_version_	1718407642614857728

Regional Atmospheric Light Optimisation Algorithm for Heterogeneous Image Dehazing

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