CFP last date
20 March 2024
Reseach Article

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.

References
  1. Yi, Z. , Ping, Z. 2010 Multisensor Image Fusion Using Fuzzy Logic for Surveillance Systems. IEEE Seventh International Conference on Fuzzy Systems and Discovery, pp. 588-592, shanghai (2010)
  2. Yang, X. H. , Huang, F. Z. , Liu,G. 2009. Urban Remote Image Fusion Using Fuzzy Rules. IEEE Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, pp. 101-109, (2009)
  3. Mengyu, Z. , Yuliang, Y. 2008 . A New image Fusion Algorithm Based on Fuzzy Logic. IEEE International Conference on Intelligent Computation Technology and Automation, pp. 83-86. Changsha (2008)
  4. Ranjan, R. , Singh, H. , Meitzler, T. , Gerhart, G. R. Iterative Image Fusion technique using Fuzzy and Neuro fuzzy Logic and Applications. IEEE Fuzzy Information Processing Society, pp. 706-710, Detroit, USA (2005)
  5. Zhao, L. , Xu, B. , Tang, W. , Chen, Z. A Pixel-Level Multisensor Image Fusion Algorithm based on Fuzzy Logic. LNCS, vol. 3613, pp. 717-720. Springer, Heidelberg (2005)
  6. Wang, Y. P. , Dang, J. W. , Li, Q. , Li, S. Multimodal Medical Image fusion using Fuzzy Radial Basis function Neural Networks, IEEE International Conference on Wavelet Analysis and Pattern Recognition, pp. 778-782. Beijing (2007)
  7. Tanish, Z. , Ishit, M. , Mukesh, Z. Novel hybrid Multispectral Image Fusion Method using Fuzzy Logic. I. J. Computer Information Systems and Industrial Management Applications. 096-103 (2010)
  8. Bushra, N. K. , Anwar, M. M. , Haroon, I. Pixel & Feature Level Multi-Resolution Image Fusion based on Fuzzy Logic. ACM Proc. of the 6th WSEAS International Conference on Wavelet analysis & Multirate Systems, pp. 88-91. Romania (2006)
  9. Jionghua,Teng. , Suhuan,Wang. ,Jingzhou,Zhang. , Xue, Wang. Neuro-fuzzy logic based fusion algorithm of medical images. Image and Signal Processing (CISP), pp. 1552 – 1556, 2010
  10. Harpreet, Singh. , Jyoti, Raj. , Gulsheen, Kaur. , Thomas ,Meitzler. , Image Fusion using Fuzzy Logic and Applications, IEEE Proceedings International Conference on Fuzzy Systems, 2004
  11. S. R. Dammavalam, S. Maddala, M. H. M. Krishna Prasad. , Quality Evaluation Measures of Pixel - Level Image Fusion Using Fuzzy Logic. LNCS 7076,pp. 485-493,2011
  12. Praveena, S. M. Multiresolution Optimization of Image Fusion. National Conference on Recent Trends in Communication and Signal Processing, pp. 111-118. Coimbatore (2009) [ 13] Zadeh, L. A. . Fuzzy Sets. J. Information and Control. 8, 338-353 (1965)
  13. Maruthi, R. , Sankarasubramanian, K. Pixel Level Multifocus Image Fusion Based on Fuzzy Logic Approach. J. Information Technology. 7(4), 168-171 (2008)
  14. S. R. Dammavalam, S. Maddala,MHM. KrishnaPrasad. Quality Assessment of Pixel-Level Image Fusion Using Fuzzy Logic, IJSC, Vol. 3, No. 1, pp. 13-25,February 2012.
  15. Jionghua Teng Suhuan Wang Jingzhou Zhang Xue Wang Coll. of Autom. , Northwestern Polytech. Univ. (NPU), Xi'an, China Neuro-fuzzy logic based fusion algorithm of medical images
  16. Mumtaz, A. , Masjid, A. Genetic Algorithms and its Applications to Image Fusion. In: IEEE International Conference on Emerging Technologies, Rawalpindi, pp. 6–10 (2008)
  17. Seetha M, MuraliKrishna I. V & Deekshatulu, B. L, (2005) "Data Fusion Performance Analysis Based on Conventional and Wavelet Transform Techniques", IEEE Proceedings on Geoscience and Remote Sensing Symposium, Vol 4, pp. 2842-2845.
  18. X. H. Yang. , F. Z. Huang, G. Liu. Urban Remote Image fusion using Fuzzy Rules. International Conference on Machine Learning and Cybernetics, pp. 101-109,2009
  19. V. P. S. Naidu, J. R. Rao. Pixel-level Image Fusion using Wavelets and Principal Component Analysis. Defence Science Journal, pp. 338 -352, 2008.
  20. The Online Resource for Research in Image Fusion, http://www. imagefusion. org
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