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Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion
In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601651/ https://www.ncbi.nlm.nih.gov/pubmed/37420457 http://dx.doi.org/10.3390/e24101435 |
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author | Im, Chan-Gi Son, Dong-Min Kwon, Hyuk-Ju Lee, Sung-Hak |
author_facet | Im, Chan-Gi Son, Dong-Min Kwon, Hyuk-Ju Lee, Sung-Hak |
author_sort | Im, Chan-Gi |
collection | PubMed |
description | In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more. |
format | Online Article Text |
id | pubmed-9601651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96016512022-10-27 Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion Im, Chan-Gi Son, Dong-Min Kwon, Hyuk-Ju Lee, Sung-Hak Entropy (Basel) Article In this paper, to improve the slow processing speed of the rule-based visible and NIR (near-infrared) image synthesis method, we present a fast image fusion method using DenseFuse, one of the CNN (convolutional neural network)-based image synthesis methods. The proposed method applies a raster scan algorithm to secure visible and NIR datasets for effective learning and presents a dataset classification method using luminance and variance. Additionally, in this paper, a method for synthesizing a feature map in a fusion layer is presented and compared with the method for synthesizing a feature map in other fusion layers. The proposed method learns the superior image quality of the rule-based image synthesis method and shows a clear synthesized image with better visibility than other existing learning-based image synthesis methods. Compared with the rule-based image synthesis method used as the target image, the proposed method has an advantage in processing speed by reducing the processing time to three times or more. MDPI 2022-10-09 /pmc/articles/PMC9601651/ /pubmed/37420457 http://dx.doi.org/10.3390/e24101435 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Im, Chan-Gi Son, Dong-Min Kwon, Hyuk-Ju Lee, Sung-Hak Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion |
title | Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion |
title_full | Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion |
title_fullStr | Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion |
title_full_unstemmed | Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion |
title_short | Tone Image Classification and Weighted Learning for Visible and NIR Image Fusion |
title_sort | tone image classification and weighted learning for visible and nir image fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9601651/ https://www.ncbi.nlm.nih.gov/pubmed/37420457 http://dx.doi.org/10.3390/e24101435 |
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