An end-to-end sea fog removal network using multiple scattering model.

An end-to-end sea fog removal network using multiple scattering model was proposed. In this network, the atmospheric multiple scattering model was re-formulated and used for sea fog removal. Compared with the atmospheric single scattering model, the atmospheric multiple scattering model could more c...

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Main Authors: Shunmin An, Xixia Huang, Zhangjing Zheng, Linling Wang
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0251337
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spelling doaj-2277633914e344dfa2a0e50112e1dd752021-05-29T04:31:29ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01165e025133710.1371/journal.pone.0251337An end-to-end sea fog removal network using multiple scattering model.Shunmin AnXixia HuangZhangjing ZhengLinling WangAn end-to-end sea fog removal network using multiple scattering model was proposed. In this network, the atmospheric multiple scattering model was re-formulated and used for sea fog removal. Compared with the atmospheric single scattering model, the atmospheric multiple scattering model could more comprehensively consider the effect of multiple scattering, which was important to the dense fog scenes, such as in ocean scene. Therefore, we used the atmospheric multiple scattering model to avoid image blurring. The model can directly generate the dehazing results, and unify the three parameters of the transmission map, the atmospheric light and the blur kernel into one formula. The latest smooth dilation and sub-pixel techniques were used in the network model. The latest techniques can avoid the gridding artifacts and the halo artifacts, the multi-scale sub-network was used to consider the features of multi-scale. In addition, multiple loss functions were used in end-to-end network. In the experimental results, the model was superior to the state-of-the-art models in terms of quantitatively and qualitatively.https://doi.org/10.1371/journal.pone.0251337
collection DOAJ
language English
format Article
sources DOAJ
author Shunmin An
Xixia Huang
Zhangjing Zheng
Linling Wang
spellingShingle Shunmin An
Xixia Huang
Zhangjing Zheng
Linling Wang
An end-to-end sea fog removal network using multiple scattering model.
PLoS ONE
author_facet Shunmin An
Xixia Huang
Zhangjing Zheng
Linling Wang
author_sort Shunmin An
title An end-to-end sea fog removal network using multiple scattering model.
title_short An end-to-end sea fog removal network using multiple scattering model.
title_full An end-to-end sea fog removal network using multiple scattering model.
title_fullStr An end-to-end sea fog removal network using multiple scattering model.
title_full_unstemmed An end-to-end sea fog removal network using multiple scattering model.
title_sort end-to-end sea fog removal network using multiple scattering model.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2021-01-01
description An end-to-end sea fog removal network using multiple scattering model was proposed. In this network, the atmospheric multiple scattering model was re-formulated and used for sea fog removal. Compared with the atmospheric single scattering model, the atmospheric multiple scattering model could more comprehensively consider the effect of multiple scattering, which was important to the dense fog scenes, such as in ocean scene. Therefore, we used the atmospheric multiple scattering model to avoid image blurring. The model can directly generate the dehazing results, and unify the three parameters of the transmission map, the atmospheric light and the blur kernel into one formula. The latest smooth dilation and sub-pixel techniques were used in the network model. The latest techniques can avoid the gridding artifacts and the halo artifacts, the multi-scale sub-network was used to consider the features of multi-scale. In addition, multiple loss functions were used in end-to-end network. In the experimental results, the model was superior to the state-of-the-art models in terms of quantitatively and qualitatively.
url https://doi.org/10.1371/journal.pone.0251337
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