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 (SSN College of Engineering, Chennai, India) and Kishore Rajendiran (SSN College of Engineering, Chennai, India)
Copyright: © 2018 |Pages: 20
DOI: 10.4018/IJIIT.2018070102

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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Xia Xiaohua et al. (2014) have used a global mapping model for initial registration as it one of the essential pre-processing step of image fusion. Focus measure operator was used for searching the best focused pixels in the sequence of images. Probability filtering is used for enhancing the robustness of focus measure and an accurate registration is performed for extracting the common features of the focused regions. Even though this method yields better results, it has high complexity due to repeated registration. Chandrakanth et al. (2014) have discussed an image fusion system for multi sensor and multiband remote sensing data. This image fusion system focuses on selection of data, pre-processing, registration and fusion for multi sensor and multiband images. A robust image registration method and appropriate fusion techniques are analyzed. The discussed technique yields better performance when compared to well-known techniques. Bai Xiangzhi et al. (2015) have presented a quad tree based decomposition methodology. In this strategy, the input images are decomposed into blocks with various sizes and focused regions are detected by using Sum of the Weighted Modified Laplacian (SWML). Akinlar Mehmet Ali et al. (2013) have analyzed a hybrid method for deformable matching of magnetic resonance (MR) images using the benefits of both wavelet and variational calculus. This method is implemented based on the Gabor wavelet energy maps of MR images. Experimental results proved that this method yields better results compared to the variation-based technique and wavelet based technique.

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