Hybrid local intensity variation and edge features map-based multi-focus image fusion using Genetic algorithm

Abstract

This study proposes a multi-focus image fusion approach that considers Genetic Algorithm (GA) optimization to achieve the selection of the most appropriate fusion weights that are used to increase the quality of the resulting fused image. local intensity variations with a standard deviation filter used to extract texture features, edge detection with the Sobel operator is defined, and a variance feature is also determined. The optimized weights are used to identify the best combination of feature maps of these three extracted features to achieve an accurate fusion process. Experiments demonstrate that this approach successfully retains texture, edges, and variation, resulting in a fused image with improved visual quality and information richness.