Fractal Dimension-Based Multi-Focus Image Fusion via AGPCNN and Consistency Verification in NSCT Domain

Multi-focus images are essential in various computer vision applications. To mitigate artifacts and information loss in multi-focus image fusion, we propose a novel algorithm based on AGPCNN and fractal dimension in the NSCT domain. The source images are decomposed into low- and high-frequency sub-bands via NSCT; the low-frequency components are fused using an averaging rule, while the high-frequency components are fused through fractal dimension and the AGPCNN model, followed by consistency verification to refine the results. Experiments on the Lytro and MFI-WHU datasets show that the proposed method outperforms existing approaches in terms of both visual quality and quantitative metrics. Furthermore, its successful application to multi-sensor and multi-modal image fusion tasks demonstrates the algorithm’s robustness and generality.