Unsupervised water scene dehazing network using multiple scattering model.

In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unli...

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Autores principales:	Shunmin An, Xixia Huang, Linling Wang, Zhangjing Zheng, Le Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9115f839c514446abd77ca74645cbdb6
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id	oai:doaj.org-article:9115f839c514446abd77ca74645cbdb6
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spelling	oai:doaj.org-article:9115f839c514446abd77ca74645cbdb62021-12-02T20:09:54ZUnsupervised water scene dehazing network using multiple scattering model.1932-620310.1371/journal.pone.0253214https://doaj.org/article/9115f839c514446abd77ca74645cbdb62021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0253214https://doaj.org/toc/1932-6203In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.Shunmin AnXixia HuangLinling WangZhangjing ZhengLe WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0253214 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shunmin An Xixia Huang Linling Wang Zhangjing Zheng Le Wang Unsupervised water scene dehazing network using multiple scattering model.
description	In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.
format	article
author	Shunmin An Xixia Huang Linling Wang Zhangjing Zheng Le Wang
author_facet	Shunmin An Xixia Huang Linling Wang Zhangjing Zheng Le Wang
author_sort	Shunmin An
title	Unsupervised water scene dehazing network using multiple scattering model.
title_short	Unsupervised water scene dehazing network using multiple scattering model.
title_full	Unsupervised water scene dehazing network using multiple scattering model.
title_fullStr	Unsupervised water scene dehazing network using multiple scattering model.
title_full_unstemmed	Unsupervised water scene dehazing network using multiple scattering model.
title_sort	unsupervised water scene dehazing network using multiple scattering model.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9115f839c514446abd77ca74645cbdb6
work_keys_str_mv	AT shunminan unsupervisedwaterscenedehazingnetworkusingmultiplescatteringmodel AT xixiahuang unsupervisedwaterscenedehazingnetworkusingmultiplescatteringmodel AT linlingwang unsupervisedwaterscenedehazingnetworkusingmultiplescatteringmodel AT zhangjingzheng unsupervisedwaterscenedehazingnetworkusingmultiplescatteringmodel AT lewang unsupervisedwaterscenedehazingnetworkusingmultiplescatteringmodel
_version_	1718375076064133120

Unsupervised water scene dehazing network using multiple scattering model.

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