Image Fusion Based on Deep Learning with Different Locations and Sizes of Objects

Abstract

Multi-focus image fusion combines partially focused images into a single all-in-focus composite. Existing object-based methods assume precise spatial and scale alignment across source images, an assumption that frequently fails in Misaligned Multi-Focus Dataset scenarios due to camera displacement and focal length variation. This paper proposes a novel training-free, object-aware fusion framework to address this limitation through a five-stage pipeline: YOLOv8x detection, SAM2-L segmentation, LoFTR correspondence matching, a novel Scale-Aware Area Resize Algorithm, and GLCM-guided MSB/LSB bit-level fusion. The framework was evaluated on the EDMF benchmark (20 image pairs, synthetically modified to simulate Misaligned Multi-Focus Dataset shifts) and a Misaligned Multi-Focus Dataset (10 image pairs). Results show consistent improvements over the best source in Average Gradient by 5.02% on the EDMF dataset and 6.31% on the Misaligned Multi-Focus Dataset, Mutual Information by 2.17% and 2.52%, and Gradient-based Quality by 3.10% and 3.53%, respectively. Ablation study confirms the MSB/LSB fusion strategy contributes +7.41% (AG) and +1.03% (MI) for mismatched objects. Qualitative results validate visual coherence and boundary fidelity of the fused outputs.