Grey wolf optimiser-based feature selection for feature-level multi-focus image fusion

This paper proposes optimal ensemble-individual-features (OEIF) for multi-focus image fusion through combining the decision information of individual features. This proposed system consists of three stages. In the first stage, the different types of features such as spatial, texture and frequency are extracted from every block on input blurred images. In the second step, grey wolf optimiser (GWO)-based features validation method is proposed to find suitable features from source images. This method is based on an iterative process, in which each individual represents a candidate solution for validating/invalidating the features. In the final step, the ensemble decision based on optimal individual features is utilised to fuse blurred images. We prove that OEIF method is better in comparison to the noisy feature-based individual pixel-level and the feature-level fusion methods with different multi-focus images and it reveals that OGWO-based proposed method performs better visual quality than other methods.