Multi-Focus Image Fusion Using the Local Fractal Dimension

Abstract With the development of sensor and image-processing technology, image fusion has become a promising research field. Multi-focus image fusion is an important issue in multi-sensor image fusion. To make full use of the texture features of an image and take into account the inherent advantages...

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Main Authors: Qingping Li, Junping Du, Liang Xu
Format: Article
Language:English
Published: SAGE Publishing 2013-05-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.5772/56322
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spelling doaj-526c0dad197e4d4780458eba1817504f2020-11-25T03:32:43ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142013-05-011010.5772/5632210.5772_56322Multi-Focus Image Fusion Using the Local Fractal DimensionQingping Li0Junping Du1Liang Xu2 Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, ChinaAbstract With the development of sensor and image-processing technology, image fusion has become a promising research field. Multi-focus image fusion is an important issue in multi-sensor image fusion. To make full use of the texture features of an image and take into account the inherent advantages of fractal theory in multi-focus image fusion, a new image fusion algorithm using the local fractal dimension (LFD) is proposed. The algorithm first calculates the LFD of each source image pixel-wise by using a blanket method and generates LFD maps of the source images. Then the local energy of each LFD is calculated to generate a decision map, in order to decide pixels of the fused image are from which source image. Finally, the fused image is reconstructed from the source images according to the decision map. Experimental results show that the proposed algorithm outperforms classic LP-based and DWT-based methods according to both visual and objective evaluations.https://doi.org/10.5772/56322
collection DOAJ
language English
format Article
sources DOAJ
author Qingping Li
Junping Du
Liang Xu
spellingShingle Qingping Li
Junping Du
Liang Xu
Multi-Focus Image Fusion Using the Local Fractal Dimension
International Journal of Advanced Robotic Systems
author_facet Qingping Li
Junping Du
Liang Xu
author_sort Qingping Li
title Multi-Focus Image Fusion Using the Local Fractal Dimension
title_short Multi-Focus Image Fusion Using the Local Fractal Dimension
title_full Multi-Focus Image Fusion Using the Local Fractal Dimension
title_fullStr Multi-Focus Image Fusion Using the Local Fractal Dimension
title_full_unstemmed Multi-Focus Image Fusion Using the Local Fractal Dimension
title_sort multi-focus image fusion using the local fractal dimension
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2013-05-01
description Abstract With the development of sensor and image-processing technology, image fusion has become a promising research field. Multi-focus image fusion is an important issue in multi-sensor image fusion. To make full use of the texture features of an image and take into account the inherent advantages of fractal theory in multi-focus image fusion, a new image fusion algorithm using the local fractal dimension (LFD) is proposed. The algorithm first calculates the LFD of each source image pixel-wise by using a blanket method and generates LFD maps of the source images. Then the local energy of each LFD is calculated to generate a decision map, in order to decide pixels of the fused image are from which source image. Finally, the fused image is reconstructed from the source images according to the decision map. Experimental results show that the proposed algorithm outperforms classic LP-based and DWT-based methods according to both visual and objective evaluations.
url https://doi.org/10.5772/56322
work_keys_str_mv AT qingpingli multifocusimagefusionusingthelocalfractaldimension
AT junpingdu multifocusimagefusionusingthelocalfractaldimension
AT liangxu multifocusimagefusionusingthelocalfractaldimension
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