  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.
