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Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications

by Srinivasa Rao D, Seetha M, Krishna Prasad Mhm
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 20
Year of Publication: 2012
Authors: Srinivasa Rao D, Seetha M, Krishna Prasad Mhm
10.5120/6222-8800

Srinivasa Rao D, Seetha M, Krishna Prasad Mhm . Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications. International Journal of Computer Applications. 43, 20 ( April 2012), 31-37. DOI=10.5120/6222-8800

@article{ 10.5120/6222-8800,
author = { Srinivasa Rao D, Seetha M, Krishna Prasad Mhm },
title = { Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 20 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number20/6222-8800/ },
doi = { 10.5120/6222-8800 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:56.381402+05:30
%A Srinivasa Rao D
%A Seetha M
%A Krishna Prasad Mhm
%T Comparison of Fuzzy and Neuro Fuzzy Image Fusion Techniques and its Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 20
%P 31-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image fusion is the process of integrating multiple images of the same scene into a single fused image to reduce uncertainty and minimizing redundancy while extracting all the useful information from the source images. Image fusion process is required for different applications like medical imaging, remote sensing, medical imaging, machine vision, biometrics and military applications where quality and critical information is required. In this paper, image fusion using fuzzy and neuro fuzzy logic approaches utilized to fuse images from different sensors, in order to enhance visualization. The proposed work further explores comparison between fuzzy based image fusion and neuro fuzzy fusion technique along with quality evaluation indices for image fusion like image quality index, mutual information measure, fusion factor, fusion symmetry, fusion index, root mean square error, peak signal to noise ratio, entropy, correlation coefficient and spatial frequency. Experimental results obtained from fusion process prove that the use of the neuro fuzzy based image fusion approach shows better performance in first two test cases while in the third test case fuzzy based image fusion technique gives better results.

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Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Logic Neuro Fuzzy Logic Image Quality Index Mutual Information Measure Fusion Factor Fusion Symmetry Fusion Index Root Mean Square Error Peak Signal To Noise Ratio Spatial Frequency