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Efficient Multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications

Efficient Multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications

Nirmala Paramanandham, Kishore Rajendiran
Copyright: © 2018 |Volume: 14 |Issue: 3 |Pages: 20
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522542803|DOI: 10.4018/IJIIT.2018070102
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MLA

Paramanandham, Nirmala, and Kishore Rajendiran. "Efficient Multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications." IJIIT vol.14, no.3 2018: pp.18-37. http://doi.org/10.4018/IJIIT.2018070102

APA

Paramanandham, N. & Rajendiran, K. (2018). Efficient Multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications. International Journal of Intelligent Information Technologies (IJIIT), 14(3), 18-37. http://doi.org/10.4018/IJIIT.2018070102

Chicago

Paramanandham, Nirmala, and Kishore Rajendiran. "Efficient Multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications," International Journal of Intelligent Information Technologies (IJIIT) 14, no.3: 18-37. http://doi.org/10.4018/IJIIT.2018070102

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Abstract

This article describes how image fusion has taken giant leaps and emerged as a promising field with diverse applications. A fused image provides more information than any of the source images and it is very helpful in surveillance applications. In this article, an efficient multi focus image fusion technique is proposed in cascaded wavelet transform domain using swarm intelligence and spatial frequency (SF). Spatial frequency is used for computing the activity level and consistency verification (CV) based decision map is employed for acquiring the final fused coefficients. Justification for employing SF and CV is also discussed. This technique performs well compared to existing techniques even when the source images are severely blurred. The proposed framework is evaluated using quantitative metrics, such as root mean square error, peak signal to noise ratio, mean absolute error, percentage fit error, structural similarity index, standard deviation, mean gradient, Petrovic metric, SF, feature mutual information and entropy. Experimental outcomes demonstrate that the proposed technique outperforms the state-of-the art techniques, in terms of visual impact as well as objective assessment.

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