Multi-focus image fusion (MFF) is a popular technique to generate an all-in-focus image, where all objects in the scene are sharp. However, existing methods pay little attention to defocus spread effects of the real-world multi-focus images. Consequently, most of the methods perform badly in the areas near focus map boundaries. According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects. Firstly, a new MFF assessment metric is presented by combining the principle of structure similarity and detected focus maps. Then, MFF problem is cast into maximizing this metric. The optimization is solved by gradient ascent. Experiments conducted on the real-world dataset verify superiority of the proposed model. The codes are available at https://github.com/xsxjtu/MFF-SSIM.

中文翻译：

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通过基于优化的策略减少多焦点图像融合的严重散焦扩散效应

多焦点图像融合 (MFF) 是一种流行的技术，用于生成全焦点图像，其中场景中的所有对象都是清晰的。然而，现有方法很少关注现实世界多焦点图像的散焦扩散效应。因此，大多数方法在焦点图边界附近的区域中表现不佳。根据融合图像中的每个局部区域应该与源图像中最清晰的区域相似的想法，本文提出了一种基于优化的方法来减少散焦扩散效应。首先，结合结构相似性原理和检测到的焦点图，提出了一种新的MFF评估指标。然后，将 MFF 问题转化为最大化该指标。优化是通过梯度上升来解决的。在真实世界数据集上进行的实验验证了所提出模型的优越性。