A multi-focus image fusion method via region mosaicking on Laplacian pyramids

In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to...

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Detalles Bibliográficos
Autores principales: Kou, Liang, Zhang, Liguo, Zhang, Kejia, Sun, Jianguo, Han, Qilong, Jin, Zilong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5957432/
https://www.ncbi.nlm.nih.gov/pubmed/29771912
http://dx.doi.org/10.1371/journal.pone.0191085
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author Kou, Liang
Zhang, Liguo
Zhang, Kejia
Sun, Jianguo
Han, Qilong
Jin, Zilong
author_facet Kou, Liang
Zhang, Liguo
Zhang, Kejia
Sun, Jianguo
Han, Qilong
Jin, Zilong
author_sort Kou, Liang
collection PubMed
description In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduces the color distortion of the fused images on two datasets.
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spelling pubmed-59574322018-05-31 A multi-focus image fusion method via region mosaicking on Laplacian pyramids Kou, Liang Zhang, Liguo Zhang, Kejia Sun, Jianguo Han, Qilong Jin, Zilong PLoS One Research Article In this paper, a method named Region Mosaicking on Laplacian Pyramids (RMLP) is proposed to fuse multi-focus images that is captured by microscope. First, the Sum-Modified-Laplacian is applied to measure the focus of multi-focus images. Then the density-based region growing algorithm is utilized to segment the focused region mask of each image. Finally, the mask is decomposed into a mask pyramid to supervise region mosaicking on a Laplacian pyramid. The region level pyramid keeps more original information than the pixel level. The experiment results show that RMLP has best performance in quantitative comparison with other methods. In addition, RMLP is insensitive to noise and can reduces the color distortion of the fused images on two datasets. Public Library of Science 2018-05-17 /pmc/articles/PMC5957432/ /pubmed/29771912 http://dx.doi.org/10.1371/journal.pone.0191085 Text en © 2018 Kou et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Kou, Liang
Zhang, Liguo
Zhang, Kejia
Sun, Jianguo
Han, Qilong
Jin, Zilong
A multi-focus image fusion method via region mosaicking on Laplacian pyramids
title A multi-focus image fusion method via region mosaicking on Laplacian pyramids
title_full A multi-focus image fusion method via region mosaicking on Laplacian pyramids
title_fullStr A multi-focus image fusion method via region mosaicking on Laplacian pyramids
title_full_unstemmed A multi-focus image fusion method via region mosaicking on Laplacian pyramids
title_short A multi-focus image fusion method via region mosaicking on Laplacian pyramids
title_sort multi-focus image fusion method via region mosaicking on laplacian pyramids
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5957432/
https://www.ncbi.nlm.nih.gov/pubmed/29771912
http://dx.doi.org/10.1371/journal.pone.0191085
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