University of Isfahan Computational Intelligence in Electrical Engineering 2821-0689 6 3 2015 10 23 Optimization of Image Fusion Using Gravitational Search Algorithm Optimization of Image Fusion Using Gravitational Search Algorithm 0 67 15431 FA Mansour Sheikhan Amaj Chamankar Journal Article 2016 06 14 Because of photography limitations, it is sometimes impossible to achieve <br />an image of high quality and sufficient clarity by taking just one picture from <br />a scene. Therefore, various methods of image fusion have been proposed. On the <br />other hand, random population-based algorithms have been used extensively for <br />optimization. These algorithms are often inspired by the physical processes or <br />the behavior of the living beings. Gravitation search algorithm (GSA) is an <br />optimization algorithm that is based on the gravitation and mass concept and <br />the search agents in this algorithm are masses. In this study, GSA is used to <br />optimize the image fusion process when using images with different focuses. In <br />this way, spatial frequency measure is used. The performance of proposed method <br />is compared with two other methods: a peer approach when the particle swarm <br />optimization (PSO) algorithm is used instead of GSA, and a pixel-based fuzzy <br />approach. Experimental results show that the proposed method has a superior <br />performance and this method can be applied to colored images, as well. Because of photography limitations, it is sometimes impossible to achieve <br />an image of high quality and sufficient clarity by taking just one picture from <br />a scene. Therefore, various methods of image fusion have been proposed. On the <br />other hand, random population-based algorithms have been used extensively for <br />optimization. These algorithms are often inspired by the physical processes or <br />the behavior of the living beings. Gravitation search algorithm (GSA) is an <br />optimization algorithm that is based on the gravitation and mass concept and <br />the search agents in this algorithm are masses. In this study, GSA is used to <br />optimize the image fusion process when using images with different focuses. In <br />this way, spatial frequency measure is used. The performance of proposed method <br />is compared with two other methods: a peer approach when the particle swarm <br />optimization (PSO) algorithm is used instead of GSA, and a pixel-based fuzzy <br />approach. Experimental results show that the proposed method has a superior <br />performance and this method can be applied to colored images, as well. https://isee.ui.ac.ir/article_15431_e2697ac72b41b5bd0e9e4f785c102935.pdf