Abstract
Image fusion is the process of reducing 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, machine vision, biometrics and military applications. In this paper, an iterative fuzzy logic approach utilized to fuse images from different sensors, in order to enhance visualization. The proposed workfurther explores comparison between fuzzy based image fusion and iterative fuzzy fusion technique along with quality evaluation indices for image fusion like image quality index, mutual information measure, root mean square error, peak signal to noise ratio, entropy and correlation coefficient. Experimental results obtained from fusion process prove that the use of the proposed iterative fuzzy fusion can efficiently preserve the spectral information while improving the spatial resolution of the remote sensing images and medical imaging.
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References
Yi, Z., Ping, Z.: Multisensor Image Fusion Using Fuzzy Logic for Surveillance Systems. In: IEEE Seventh International Conference on Fuzzy Systems and Discovery, Shanghai, pp. 588–592 (2010)
Yang, X.H., Huang, F.Z., Liu, G.: Urban Remote Image Fusion Using Fuzzy Rules. In: IEEE Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, pp. 101–109 (2009)
Mengyu, Z., Yuliang, Y.: A New image Fusion Algorithm Based on Fuzzy Logic. In: IEEE International Conference on Intelligent Computation Technology and Automation, Changsha, pp. 83–86 (2008)
Ranjan, R., Singh, H., Meitzler, T., Gerhart, G.R.: Iterative Image Fusion technique using Fuzzy and Neuro fuzzy Logic and Applications. In: IEEE Fuzzy Information Processing Society, Detroit, USA, pp. 706–710 (2005)
Zhao, L., Xu, B., Tang, W., Chen, Z.: A Pixel-Level Multisensor Image Fusion Algorithm Based on Fuzzy Logic. In: Wang, L., Jin, Y. (eds.) FSKD 2005, Part I. LNCS (LNAI), vol. 3613, pp. 717–720. Springer, Heidelberg (2005)
Wang, Y.P., Dang, J.W., Li, Q., Li, S.: Multimodal Medical Image fusion using Fuzzy Radial Basis function Neural Networks. In: IEEE International Conference on Wavelet Analysis and Pattern Recognition, Beijing, pp. 778–782 (2007)
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)
Bushra, N.K., Anwar, M.M., Haroon, I.: Pixel & Feature Level Multi-Resolution Image Fusion based on Fuzzy Logic. In: ACM Proc. of the 6th WSEAS International Conference on Wavelet analysis & Multirate Systems, Romania, pp. 88–91 (2006)
Zadeh, L.A.: Fuzzy Sets. J. Information and Control 8, 338–353 (1965)
Praveena, S.M.: Multiresolution Optimization of Image Fusion. In: National Conference on Recent Trends in Communication and Signal Processing, Coimbatore, pp. 111–118 (2009)
Maruthi, R., Sankarasubramanian, K.: Pixel Level Multifocus Image Fusion Based on Fuzzy Logic Approach. J. Information Technology 7(4), 168–171 (2008)
Dammavalam, S.R., Maddala, S., Krishna Prasad, M.H.M.: Quality Evaluation Measures of Pixel – Level Image Fusion Using Fuzzy Logic, pp. 485–493 (2011)
Thomas, M., David, B., Sohn, E.J., Kimberly, L., Darryl, B., Gulshecn, K., Harpreet, S., Samuel, E., Grmgory, S., Yelena, R., James, R.: Fuzzy Logic bascd Image Fusion Aerosense, Orlando (2002)
Mumtaz, A., Masjid, A.: Genetic Algorithms and its Applicatio to Image Fusion. In: IEEE International Conference on Emerging Technologies, Rawalpindi, pp. 6–10 (2008)
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Dammavalam, S.R., Maddala, S., Krishna Prasad, M.H.M. (2013). Iterative Image Fusion Using Fuzzy Logic with Applications. In: Meghanathan, N., Nagamalai, D., Chaki, N. (eds) Advances in Computing and Information Technology. Advances in Intelligent Systems and Computing, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31552-7_16
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DOI: https://doi.org/10.1007/978-3-642-31552-7_16
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