Nature-inspired Algorithms based Multispectral Image Fusion

Image Fusion is the combining process of relevant information from one, two or more images to create a single image which is more complete than any of the input ones. Image fusion is used in medical diagnosis in case of multi-modal images and also for multispectral images processing. Considering that the result of the image fusion process must maximize an evaluation measure, the fusion can be seen as an optimization procedure. In this paper, it is proposed an image fusion approach based on the usage of three nature-inspired optimization metaheuristics: Particle swarming, Cuckoo Search and Fireworks algorithms. As fusion technique, the weighted average in both spatial and transformed domain is used. The weights which maximize the fusion result evaluation measure are approximated using the nature-inspired algorithms. The proposed approach is applied for multispectral image fusion and the results obtained using the three optimization metaheuristics are compared.