Gradient-based multi-focus image fusion with focus-aware saliency enhancement

• URL: http://arxiv.org/abs/2509.22392v1
• Date: Fri, 26 Sep 2025 14:20:44 GMT
• Title: Gradient-based multi-focus image fusion with focus-aware saliency enhancement
• Authors: Haoyu Li, XiaoSong Li,
• Abstract summary: Multi-focus image fusion (MFIF) aims to yield an all-focused image from multiple partially focused inputs.<n>We propose a MFIF method based on significant boundary enhancement, which generates high-quality fused boundaries.<n>Our method consistently outperforms 12 state-of-the-art methods in both subjective and objective evaluations.
• Score: 18.335216974790754
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-focus image fusion (MFIF) aims to yield an all-focused image from multiple partially focused inputs, which is crucial in applications cover sur-veillance, microscopy, and computational photography. However, existing methods struggle to preserve sharp focus-defocus boundaries, often resulting in blurred transitions and focused details loss. To solve this problem, we propose a MFIF method based on significant boundary enhancement, which generates high-quality fused boundaries while effectively detecting focus in-formation. Particularly, we propose a gradient-domain-based model that can obtain initial fusion results with complete boundaries and effectively pre-serve the boundary details. Additionally, we introduce Tenengrad gradient detection to extract salient features from both the source images and the ini-tial fused image, generating the corresponding saliency maps. For boundary refinement, we develop a focus metric based on gradient and complementary information, integrating the salient features with the complementary infor-mation across images to emphasize focused regions and produce a high-quality initial decision result. Extensive experiments on four public datasets demonstrate that our method consistently outperforms 12 state-of-the-art methods in both subjective and objective evaluations. We have realized codes in https://github.com/Lihyua/GICI

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