Multi-focus Images Fusion Based on Data Assimilation and Genetic Algorithm

The traditional fusion algorithms, such as principal component analysis, wavelet transform, Gauss-Laplacian pyramids, Brovey transform, curvelet transform and so on, set down the fusion rules before fusion process. However, the rules which determine the attributes of fusion results cannot be adjusted according to different application. In this paper, a framework based on data assimilation and genetic algorithm for multi-focus image fusion is proposed. Data assimilation is to combine the observational data and simulative data to obtain more objective result which is firstly used in weather field. Under this framework, weights of different attributes according to the application are determined and object function constituted by the weighted sum of each evaluation index is constructed to obtain the proper fusion image. The experiments validate the feasibility of the framework.