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.

翻译：现有的大多数多聚焦图像融合（MFIF）方法往往难以准确保留不确定过渡区域，并检测大散焦区域内的微小聚焦区域。针对这一问题，本研究提出了一种新型小区域感知MFIF算法，以提升目标检测能力。首先，我们增强小聚焦区域和边界区域的像素属性，随后将其与视觉显著性检测相结合，获取用于判别聚焦像素分布的预融合结果。为精确保证像素聚焦，我们将源图像视为聚焦、散焦和不确定区域的组合，并提出一种三区域分割策略。最后，我们设计了一种有效的像素选择规则，用于生成分割决策图并得到最终融合结果。实验表明，所提方法能够准确检测微小且平滑的聚焦区域，同时提升目标检测性能，在主客观评价中均优于现有方法。源代码已发布于https://github.com/ixilai/SAMF。