In most traditional multi-focus image fusion algorithms, the focus measures used to detect the focused areas of multiple images are unstable and sensitive to noise. Although many modified methods implement more sophisticated strategies to cope with this problem, the complexity of these methods turns out to be a problem for mobile devices. In order to cope with these problems, a novel multi-focus image fusion method based on image matting technique is put forward in this paper. This method is simple yet effective. Firstly, a new focus measure named the sum of the Gaussian-based modified Laplacian (SGML) is proposed to estimate the focus map of source images. Then, the initial segmentation map can be obtained via a novel sliding window strategy. Further, with the rough segmentation map as an input, learning based image matting technique is performed to extract the exact boundaries between focused and defocused regions. Finally, by combining the information of the focus areas, an all-in-focus image can be obtained. The numerous experiments have revealed that the proposed approach yielded better results in comparison with some competing techniques both in subjective and objective evaluation.

在大多数传统的多焦点图像融合算法中，用于检测多个图像的焦点区域的焦点度量不稳定且对噪声敏感。尽管许多修改的方法实施了更复杂的策略来解决此问题，但是这些方法的复杂性对于移动设备来说是一个问题。为了解决这些问题，提出了一种基于图像消光技术的新型多焦点图像融合方法。此方法简单但有效。首先，提出了一种新的聚焦度量，即基于高斯的改进拉普拉斯算子（SGML）的总和，以估计源图像的聚焦图。然后，可以通过新颖的滑动窗口策略获得初始分割图。此外，以粗略分割图作为输入，执行基于学习的图像消光技术以提取聚焦区域和散焦区域之间的确切边界。最后，通过组合聚焦区域的信息，可以获得全聚焦图像。大量实验表明，与主观和客观评估中的某些竞争技术相比，该方法产生了更好的结果。