# SAMF: Small-Area-Aware Multi-focus Image Fusion for Object Detection

arXiv: https://arxiv.org/abs/2401.08357 · HTML: https://mlsanity.com/paper/2401.08357/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Fast and High-Quality Blind Multi-Spectral Image Pansharpening](https://mlsanity.com/paper/2103.09943/) (2021)

## Built on by (most cited first, 8 of 8)

- [UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2402.02096/) (2024)
- [FlexiD-Fuse: Flexible number of inputs multi-modal medical image fusion based on diffusion model](https://mlsanity.com/paper/2509.09456/) (2025)
- [Quaternion Sparse Decomposition for Multi-focus Color Image Fusion](https://mlsanity.com/paper/2505.02365/) (2025)
- [Neurodynamics-Driven Coupled Neural P Systems for Multi-Focus Image Fusion](https://mlsanity.com/paper/2509.17704/) (2025)
- [Gradient-based multi-focus image fusion with focus-aware saliency enhancement](https://mlsanity.com/paper/2509.22392/) (2025)
- [A Luminance-Aware Multi-Scale Network for Polarization Image Fusion with a Multi-Scene Dataset](https://mlsanity.com/paper/2510.24379/) (2025)
- [Generative Multi-Focus Image Fusion](https://mlsanity.com/paper/2512.21495/) (2025)
- [ReDiffuse: Rotation Equivariant Diffusion Model for Multi-focus Image Fusion](https://mlsanity.com/paper/2603.21129/) (2026)

## Abstract (arXiv)

Existing multi-focus image fusion (MFIF) methods often fail to preserve the uncertain transition region and detect small focus areas within large defocused regions accurately. To address this issue, this study proposes a new small-area-aware MFIF algorithm for enhancing object detection capability. First, we enhance the pixel attributes within the small focus and boundary regions, which are subsequently combined with visual saliency detection to obtain the pre-fusion results used to discriminate the distribution of focused pixels. To accurately ensure pixel focus, we consider the source image as a combination of focused, defocused, and uncertain regions and propose a three-region segmentation strategy. Finally, we design an effective pixel selection rule to generate segmentation decision maps and obtain the final fusion results. Experiments demonstrated that the proposed method can accurately detect small and smooth focus areas while improving object detection performance, outperforming existing methods in both subjective and objective evaluations. The source code is available at https://github.com/ixilai/SAMF.