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方法难以准确保留不确定过渡区域并检测大散焦区域中的小聚焦区域。针对这一问题，本研究提出了一种新的小区域感知多聚焦图像融合算法，以增强目标检测能力。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，获得用于判别聚焦像素分布的预融合结果。为确保像素聚焦的准确性，我们将源图像视为聚焦区域、散焦区域和不确定区域的组合，并提出了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来生成分割决策图，并得到最终的融合结果。实验表明，所提方法能够准确检测小而平滑的聚焦区域，同时提升目标检测性能，在主观与客观评价中均优于现有方法。源代码见 https://github.com/ixilai/SAMF。