Single image mixed dehazing method based on numerical iterative model and DehazeNet.

As one of the most common adverse weather phenomena, haze has caused detrimental effects on many computer vision systems. To eliminate the effect of haze, in the field of image processing, image dehazing has been studied intensively, and many advanced dehazing algorithms have been proposed. Physical...

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Autores principales:	Wenjiang Jiao, Xingwu Jia, Yuetong Liu, Qun Jiang, Ziyi Sun
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7213acde60ab437aad672abd370ec3bd
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spelling	oai:doaj.org-article:7213acde60ab437aad672abd370ec3bd2021-12-02T20:08:52ZSingle image mixed dehazing method based on numerical iterative model and DehazeNet.1932-620310.1371/journal.pone.0254664https://doaj.org/article/7213acde60ab437aad672abd370ec3bd2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254664https://doaj.org/toc/1932-6203As one of the most common adverse weather phenomena, haze has caused detrimental effects on many computer vision systems. To eliminate the effect of haze, in the field of image processing, image dehazing has been studied intensively, and many advanced dehazing algorithms have been proposed. Physical model-based and deep learning-based methods are two competitive methods for single image dehazing, but it is still a challenging problem to achieve fidelity and effectively dehazing simultaneously in real hazy scenes. In this work, a mixed iterative model is proposed, which combines a physical model-based method with a learning-based method to restore high-quality clear images, and it has good performance in maintaining natural attributes and completely removing haze. Unlike previous studies, we first divide the image into different regions according to the density of haze to accurately calculate the atmospheric light for restoring haze-free images. Then, dark channel prior and DehazeNet are used to jointly estimate the transmission to promote the final clear haze-free image that is more similar to the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic datasets and real-world datasets. Moreover, to indicate the universality of the proposed method, we further apply it to the remote sensing datasets, which can also produce visually satisfactory results.Wenjiang JiaoXingwu JiaYuetong LiuQun JiangZiyi SunPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254664 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wenjiang Jiao Xingwu Jia Yuetong Liu Qun Jiang Ziyi Sun Single image mixed dehazing method based on numerical iterative model and DehazeNet.
description	As one of the most common adverse weather phenomena, haze has caused detrimental effects on many computer vision systems. To eliminate the effect of haze, in the field of image processing, image dehazing has been studied intensively, and many advanced dehazing algorithms have been proposed. Physical model-based and deep learning-based methods are two competitive methods for single image dehazing, but it is still a challenging problem to achieve fidelity and effectively dehazing simultaneously in real hazy scenes. In this work, a mixed iterative model is proposed, which combines a physical model-based method with a learning-based method to restore high-quality clear images, and it has good performance in maintaining natural attributes and completely removing haze. Unlike previous studies, we first divide the image into different regions according to the density of haze to accurately calculate the atmospheric light for restoring haze-free images. Then, dark channel prior and DehazeNet are used to jointly estimate the transmission to promote the final clear haze-free image that is more similar to the real scene. Finally, a numerical iterative strategy is employed to further optimize the atmospheric light and transmission. Extensive experiments demonstrate that our method outperforms existing state-of-the-art methods on synthetic datasets and real-world datasets. Moreover, to indicate the universality of the proposed method, we further apply it to the remote sensing datasets, which can also produce visually satisfactory results.
format	article
author	Wenjiang Jiao Xingwu Jia Yuetong Liu Qun Jiang Ziyi Sun
author_facet	Wenjiang Jiao Xingwu Jia Yuetong Liu Qun Jiang Ziyi Sun
author_sort	Wenjiang Jiao
title	Single image mixed dehazing method based on numerical iterative model and DehazeNet.
title_short	Single image mixed dehazing method based on numerical iterative model and DehazeNet.
title_full	Single image mixed dehazing method based on numerical iterative model and DehazeNet.
title_fullStr	Single image mixed dehazing method based on numerical iterative model and DehazeNet.
title_full_unstemmed	Single image mixed dehazing method based on numerical iterative model and DehazeNet.
title_sort	single image mixed dehazing method based on numerical iterative model and dehazenet.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7213acde60ab437aad672abd370ec3bd
work_keys_str_mv	AT wenjiangjiao singleimagemixeddehazingmethodbasedonnumericaliterativemodelanddehazenet AT xingwujia singleimagemixeddehazingmethodbasedonnumericaliterativemodelanddehazenet AT yuetongliu singleimagemixeddehazingmethodbasedonnumericaliterativemodelanddehazenet AT qunjiang singleimagemixeddehazingmethodbasedonnumericaliterativemodelanddehazenet AT ziyisun singleimagemixeddehazingmethodbasedonnumericaliterativemodelanddehazenet
_version_	1718375130119274496

Single image mixed dehazing method based on numerical iterative model and DehazeNet.

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