A Novel Approach of Image Fusion Techniques using Ant Colony Optimization
Jyoti S. Kulkarni1, Rajankumar S. Bichkar2

1Jyoti S. Kulkarni*, Assistant Professor, Pimpri Chinchwad College of Engineering, Pune (Maharashtra), India.
2Rajankumar S. Bichkar, Principal, Vidya Pratishthan’s Kamalnayan Bajaj Institute of Engineering and Technology, Baramati (Maharashtra), India.

Manuscript received on May 23, 2021. | Revised Manuscript received on May 29, 2021. | Manuscript published on June 30, 2021. | PP: 92-97 | Volume-10, Issue-8, June 2021 | Retrieval Number: 100.1/ijitee.H92410610821 | DOI: 10.35940/ijitee.H9241.0610821
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Ant Colony Optimization (ACO) is a relatively high approach for finding a relatively strong solution to the problem of optimization. The ACO based image fusion technique is proposed. The objective function and distance matrix is designed for image fusion. ACO is used to fuse input images at the feature-level by learning the fusion parameters. It is used to select the fusion parameters according to the user-defined cost functions. This algorithm transforms the results into the initial pheromone distribution and seeks the optimal solution by using the features. As to relevant parameters for the ACO, three parameters (α, β, ρ ) have the greatest impact on convergence. If the values of α, β are appropriately increased, convergence can speed up. But if the gap between these two is too large, the precision of convergence will be negatively affected. Since the ACO is a random search algorithm, its computation speed is relatively slow. 
Keywords: Convergence, Heuristic, Pheromone.