Multi-Focus Image Fusion Using Soft Decision Maps and Dual Fusion Rules

Multi-Focus Image Fusion is a technique that combines the in-focus regions from multiple images of the same scene, captured at different focal planes, to generate a single image with an extended Depth of Field compared to the source images. Despite improved fusion quality, recent approaches are constrained by high computational cost and the need for large training datasets, reducing their feasibility in resource-limited scenarios. In this work, a multi-focus fusion method based on a soft decision map is proposed, where the map is generated from local focus measurements in each scene. Subsequently, two Fusion Rules are employed: the Additive Fusion Rule, commonly used in the state of the art, and the Multiplicative Fusion Rule, which, to the best of our knowledge, has not been previously reported in the MFIF context. The method is evaluated using the public MFI-WHU and Lytro datasets through both reference-based and non-reference metrics. The results demonstrate that the proposed method produces visually coherent fused images and achieves competitive performance in terms of structural similarity, edge preservation, and information content, while maintaining low computational complexity.