DOI: 10.4018/IJIIT.2018070102 International Journal of Intelligent Information Technologies Volume 14 • Issue 3 • July-September 2018 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 18 Efcient Multi Focus Image Fusion Technique Optimized Using MOPSO for Surveillance Applications Nirmala Paramanandham, SSN College of Engineering, Chennai, India Kishore Rajendiran, SSN College of Engineering, Chennai, India 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. KEywORdS Consistency Verification, Discrete Wavelet Transform, Image Fusion, Multi Objective Particle Swarm Optimization, Spatial Frequency, Stationary Wavelet Packet Transform 1. INTROdUCTION Image fusion is the method of combining multiple images into a single image and making it appropriate for visual perception and further tasks such as feature extraction, segmentation and target detection (Drajic & Cvejic, 2007) (Huafing et al., 2013). Effective focusing of all objects in a scene is not feasible due to limited depth of the cameras in sensors. A popular way to solve this problem is image fusion, in which one can acquire a set of images of the same scene from diverse viewpoints and fuse them for obtaining a more informative image (Zhang, & Blum, 1999). In the recent years a lot of algorithms have been developed by researchers for multifocus image fusion. These algorithms can be classified into spatial domain and transform domain (Mitianoudis & Stathaki, 2007). In spatial domain technique, the images are fused by measuring the spatial features, such as mean, variance and standard derivation. For transform domain techniques, the images are