Abstract
Image fusion is a technique to combine the registered images to increase the spatial resolution of acquired low detail multi-sensor images and preserving their spectral information. In fusing panchromatic and multispectral images, the objective is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information. Different fusion methods provide different results for different applications, medical imaging, automatic target guidance system, remote sensing, machine vision, automatic change detection, and biometrics. In this paper, we utilize a fuzzy logic approach to fuse images from different sensors, in order to enhance visualization. The work here further explores the comparison between image fusion using wavelet transform and fuzzy logic approach along with performance/quality evaluation measures like image quality index, entropy, mutual information measure, root mean square error, peak signal to noise ratio, fusion factor, fusion symmetry and fusion index. Experimental results prove that the use of the proposed method can efficiently preserve the spectral information while improving the spatial resolution of the remote sensing images.
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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. 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, 9–103 (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)
Li, H., Manjunath, B.S, Mitra, S.K.: Multi-Sensor Image Fusion Using the Wavelet Transform. In: IEEE International Conference on Image Processing, Austin, pp. 51–55 (1994)
Kannan, K., Perumal, S.A., Arulmozhi, K.: Performance Comparision of various levels of Fusion of Multi-Focused Images using Wavelet Transform. I. J. Computer Applications (2010) ISSN 0975–8887
Susmitha, V., Pancham, S.: A Novel Architecture for Wavelet based Image Fusion. World Academy of Science, Engineering and Technology, 372–377 (2009)
Krista, A., Zhang, Y., Dare, P.: Wavelet based Image Fusion Techniques- An Introduction Review and Comparision. J. Photogrammetry & Remote Sensing, 249–263 (2007)
Maruthi, R., Sankarasubramanian, K.: Pixel Level Multifocus Image Fusion Based on Fuzzy Logic Approach. J. Information Technology 7(4), 168–171 (2008)
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 based 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)
Seetha, M., MuraliKrishna, I.V., Deekshatulu, B.L.: Data Fusion Performance Analysis Based on Conventional and Wavelet Transform Techniques. In: IEEE Proc. Geoscience and Remote Sensing Symposium, Seoul, pp. 284–2845 (2005)
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Dammavalam, S.R., Maddala, S., Krishna Prasad, M.H.M. (2011). Quality Evaluation Measures of Pixel - Level Image Fusion Using Fuzzy Logic. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_59
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DOI: https://doi.org/10.1007/978-3-642-27172-4_59
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