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Empirical Wavelet Transform for Multifocus Image Fusion

Publication Type : Conference Proceedings

Publisher : International Conference on Soft Computing Systems (2015), Advances in Intelligent Systems and Computing, AISC Springer Series

Source : International Conference on Soft Computing Systems (2015), Advances in Intelligent Systems and Computing, AISC Springer Series, Volume 397, Noorul Islam Centre for Higher Education, Kumaracoil; India, p.257-263 (2016)

Url : http://link.springer.com/chapter/10.1007/978-81-322-2671-0_25

Keywords : DWT, EWT, FIHS, Image fusion, MSVD, Quality metric evaluation, Simple average

Campus : Coimbatore

School : School of Engineering

Center : Computational Engineering and Networking

Department : Electronics and Communication

Year : 2016

Abstract : Image fusion has enormous applications in the fields of satellite imaging, remote sensing, target tracking, medical imaging, and much more. This paper aims to demonstrate the application of empirical wavelet transform for the fusion of multifocus images incorporating the simple average fusion rule. The method proposed in this paper is experimented on benchmark datasets used for fusing images of different focuses. The effectiveness of the proposed method is evaluated across the existing techniques. The performance comparison of the proposed method is done by visual perception and assessment of standard quality metrics which includes root mean squared error, relative average spectral error, universal image quality index, and spatial information. The experimental result analysis shows that the proposed technique based on the empirical wavelet transform (EWT) outperforms the existing techniques.

Cite this Research Publication : S. Moushmi, Sowmya, and Dr. Soman K. P., “Empirical Wavelet Transform for Multifocus Image Fusion”, International Conference on Soft Computing Systems (2015), Advances in Intelligent Systems and Computing, vol. 397. AISC Springer Series, Noorul Islam Centre for Higher Education, Kumaracoil; India, pp. 257-263, 2016.


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