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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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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. |
format | Online Article Text |
id | pubmed-6111301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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