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A Sensor Image Dehazing Algorithm Based on Feature Learning

To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by...

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Detalles Bibliográficos
Autores principales: Liu, Kun, He, Linyuan, Ma, Shiping, Gao, Shan, Bi, Duyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111301/
https://www.ncbi.nlm.nih.gov/pubmed/30096891
http://dx.doi.org/10.3390/s18082606
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author Liu, Kun
He, Linyuan
Ma, Shiping
Gao, Shan
Bi, Duyan
author_facet Liu, Kun
He, Linyuan
Ma, Shiping
Gao, Shan
Bi, Duyan
author_sort Liu, Kun
collection PubMed
description To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images.
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spelling pubmed-61113012018-08-30 A Sensor Image Dehazing Algorithm Based on Feature Learning Liu, Kun He, Linyuan Ma, Shiping Gao, Shan Bi, Duyan Sensors (Basel) Article To solve the problems of color distortion and structure blurring in images acquired by sensors during bad weather, an image dehazing algorithm based on feature learning is put forward to improve the quality of sensor images. First, we extracted the multiscale structure features of the haze images by sparse coding and the various haze-related color features simultaneously. Then, the generative adversarial network (GAN) was used for sample training to explore the mapping relationship between different features and the scene transmission. Finally, the final haze-free image was obtained according to the degradation model. Experimental results show that the method has obvious advantages in its detail recovery and color retention. In addition, it effectively improves the quality of sensor images. MDPI 2018-08-09 /pmc/articles/PMC6111301/ /pubmed/30096891 http://dx.doi.org/10.3390/s18082606 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Kun
He, Linyuan
Ma, Shiping
Gao, Shan
Bi, Duyan
A Sensor Image Dehazing Algorithm Based on Feature Learning
title A Sensor Image Dehazing Algorithm Based on Feature Learning
title_full A Sensor Image Dehazing Algorithm Based on Feature Learning
title_fullStr A Sensor Image Dehazing Algorithm Based on Feature Learning
title_full_unstemmed A Sensor Image Dehazing Algorithm Based on Feature Learning
title_short A Sensor Image Dehazing Algorithm Based on Feature Learning
title_sort sensor image dehazing algorithm based on feature learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111301/
https://www.ncbi.nlm.nih.gov/pubmed/30096891
http://dx.doi.org/10.3390/s18082606
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