Abstract:

Multi-focus image fusion is a technique that integrates multiple images of the same scene with different focus regions to generate a fully focused and clear image featuring both distinct details and complete structural information. It has found widespread applications in fields such as consumer electronics, medical imaging, and satellite remote sensing. To address the prevalent issues such as information loss, artifacts, insufficient datasets, and high spatiotemporal overhead in deep learning-based image fusion methods, a novel fusion model based on Three-Dimensional (3D) manifold fitting and frequency-separated guided attention mechanism was proposed. The model adopted a new paradigm of feature decomposition-fusion-reconstruction. During the encoding phase, background structures and detail information were effectively identified and separated, significantly reducing the loss of structural information and the introduction of artifacts. Innovatively, 3D manifold fitting was employed to extract common features of multi-focus images, thereby reducing the model’s dependency on large datasets and lowers spatiotemporal overhead. In the feature fusion stage, a frequency-separated guided attention mechanism was introduced to accurately characterize high-frequency details and low-frequency backgrounds of images, enabling adaptive weighted fusion of cross-frequency domain features and alleviating problems such as blurred complex textures and missing details. Furthermore, to ensure the global visual quality and local detail preservation of the fused image, a weighted composite loss function was designed by integrating multiple loss constraints. Experimental results on public classical test datasets Lytro and MFFW demonstrated that the proposed method achieved state-of-the-art performance across six commonly used evaluation metrics, fully verifying its effectiveness.

Key words: multi-focus image fusion, manifold fitting, feature extraction, cross-attention, frequency domain