Image Haze Removal Based on Superpixels and Markov Random Field

Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel si...

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Autores principales:	Yibo Tan, Guoyu Wang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2020
Materias:	Superpixel Markov random field haze removal edge preservation dark channel prior Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a09ce9411daa49a5ac491306854abaca
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id	oai:doaj.org-article:a09ce9411daa49a5ac491306854abaca
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spelling	oai:doaj.org-article:a09ce9411daa49a5ac491306854abaca2021-11-18T00:00:44ZImage Haze Removal Based on Superpixels and Markov Random Field2169-353610.1109/ACCESS.2020.2982910https://doaj.org/article/a09ce9411daa49a5ac491306854abaca2020-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9046040/https://doaj.org/toc/2169-3536Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel single-image dehazing method based on superpixels and Markov random field. We obtain the transmission map in the superpixel domain to eliminate the block/halo effect and introduce Markov random field to revise the transmission map in the superpixel domain. The key idea is that the sparsely distributed, incorrectly estimated transmittances can be corrected by properly characterizing the spatial dependencies between the incorrectly estimated superpixels and the neighbouring well-estimated superpixels. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods.Yibo TanGuoyu WangIEEEarticleSuperpixelMarkov random fieldhaze removaledge preservationdark channel priorElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 8, Pp 60728-60736 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Superpixel Markov random field haze removal edge preservation dark channel prior Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Superpixel Markov random field haze removal edge preservation dark channel prior Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yibo Tan Guoyu Wang Image Haze Removal Based on Superpixels and Markov Random Field
description	Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel single-image dehazing method based on superpixels and Markov random field. We obtain the transmission map in the superpixel domain to eliminate the block/halo effect and introduce Markov random field to revise the transmission map in the superpixel domain. The key idea is that the sparsely distributed, incorrectly estimated transmittances can be corrected by properly characterizing the spatial dependencies between the incorrectly estimated superpixels and the neighbouring well-estimated superpixels. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods.
format	article
author	Yibo Tan Guoyu Wang
author_facet	Yibo Tan Guoyu Wang
author_sort	Yibo Tan
title	Image Haze Removal Based on Superpixels and Markov Random Field
title_short	Image Haze Removal Based on Superpixels and Markov Random Field
title_full	Image Haze Removal Based on Superpixels and Markov Random Field
title_fullStr	Image Haze Removal Based on Superpixels and Markov Random Field
title_full_unstemmed	Image Haze Removal Based on Superpixels and Markov Random Field
title_sort	image haze removal based on superpixels and markov random field
publisher	IEEE
publishDate	2020
url	https://doaj.org/article/a09ce9411daa49a5ac491306854abaca
work_keys_str_mv	AT yibotan imagehazeremovalbasedonsuperpixelsandmarkovrandomfield AT guoyuwang imagehazeremovalbasedonsuperpixelsandmarkovrandomfield
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Image Haze Removal Based on Superpixels and Markov Random Field

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