Image Fusion Algorithm using Grey Wolf optimization with Shuffled Frog Leaping Algorithm
Keywords:Etaheuristic optimization, grey wolf optimization, Shuffled Frog Leaping Algorithm, prey, Image fusion
Data fusion is a “formal framework in which are expressed the means and tools for the alliance of data originating from different sources.” It aims at obtaining information of greater quality; the exact definition of 'greater quality will depend upon the application. It is a famous technique in digital image processing and is very important in medical image representation for clinical diagnosis. Previously many researchers used many meta-heuristic optimization techniques in image fusion, but the problem of local optimization restricted their searching flow to find optimum search results. In this paper, the Grey Wolf Optimization (GWO) algorithm with the help of the Shuffled Frog Leaping Algorithm (SFLA) has been proposed. That helps to find the object and allows doctors to take some action. The optimization algorithm is examined with a demonstrated example in order to simplify its steps. The result of the proposed algorithm is compared with other optimization algorithms. The proposed method's performance was always the best among them.