Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion

Empirical mode decomposition (EMD) is good at analyzing nonstationary and nonlinear signals while support vector machines (SVMs) are widely used for classification. In this paper, a combination of EMD and SVM is proposed as an improved method for fusing multifocus images. Experimental results show t...

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Detalles Bibliográficos
Autores principales: Chen, Shaohui, Su, Hongbo, Zhang, Renhua, Tian, Jing, Yang, Lihu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2008
Materias:
Full Research Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673427/
https://www.ncbi.nlm.nih.gov/pubmed/27879831
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author Chen, Shaohui
Su, Hongbo
Zhang, Renhua
Tian, Jing
Yang, Lihu
author_facet Chen, Shaohui
Su, Hongbo
Zhang, Renhua
Tian, Jing
Yang, Lihu
author_sort Chen, Shaohui
collection PubMed
description Empirical mode decomposition (EMD) is good at analyzing nonstationary and nonlinear signals while support vector machines (SVMs) are widely used for classification. In this paper, a combination of EMD and SVM is proposed as an improved method for fusing multifocus images. Experimental results show that the proposed method is superior to the fusion methods based on à-trous wavelet transform (AWT) and EMD in terms of quantitative analyses by Root Mean Squared Error (RMSE) and Mutual Information (MI).
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spelling pubmed-36734272013-07-02 Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion Chen, Shaohui Su, Hongbo Zhang, Renhua Tian, Jing Yang, Lihu Sensors (Basel) Full Research Paper Empirical mode decomposition (EMD) is good at analyzing nonstationary and nonlinear signals while support vector machines (SVMs) are widely used for classification. In this paper, a combination of EMD and SVM is proposed as an improved method for fusing multifocus images. Experimental results show that the proposed method is superior to the fusion methods based on à-trous wavelet transform (AWT) and EMD in terms of quantitative analyses by Root Mean Squared Error (RMSE) and Mutual Information (MI). Molecular Diversity Preservation International (MDPI) 2008-04-08 /pmc/articles/PMC3673427/ /pubmed/27879831 Text en © 2008 by MDPI (http://www.mdpi.org). Reproduction is permitted for noncommercial purposes.
spellingShingle Full Research Paper
Chen, Shaohui
Su, Hongbo
Zhang, Renhua
Tian, Jing
Yang, Lihu
Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion
title Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion
title_full Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion
title_fullStr Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion
title_full_unstemmed Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion
title_short Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion
title_sort improving empirical mode decomposition using support vector machines for multifocus image fusion
topic Full Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673427/
https://www.ncbi.nlm.nih.gov/pubmed/27879831
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AT zhangrenhua improvingempiricalmodedecompositionusingsupportvectormachinesformultifocusimagefusion
AT tianjing improvingempiricalmodedecompositionusingsupportvectormachinesformultifocusimagefusion
AT yanglihu improvingempiricalmodedecompositionusingsupportvectormachinesformultifocusimagefusion

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