Image fusion technology combines information from different source images of the same target and performs extremely effective information complementation, which is widely used for the transportation field, medicine field, and surveillance field. Specifically, due to the limitation of depth of field in imaging device, images cannot focus on all objects and miss partial details. To deal with this problem, an effective multi-focus image fusion method is proposed in this paper. We interpret the production of the focus map as a two-class classification task and solve this problem by using a method based on the cascade-forest model. Firstly, we extract the specific features from overlapping patches to represent the clarity level of source images. To obtain the focus map, feature vectors are fed into the pre-trained cascade-forest model. Then, we utilize consistency check to acquire the initial decision map. Afterward, guided image filtering is used for edge-reservation to refine the decision map. Finally, the result is obtained through pixel-wise weighted average strategy. Extensive experiments demonstrate that the proposed method achieves outstanding visual performance and excellent objective indicators.

中文翻译：

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级联森林模型改进成像系统的多焦点图像融合新方法

图像融合技术将来自同一目标的不同源图像的信息进行组合，并执行极其有效的信息补充，广泛应用于交通运输，医学和监视领域。具体地，由于成像装置中景深的限制，图像不能聚焦在所有物体上并且丢失部分细节。针对这一问题，提出了一种有效的多焦点图像融合方法。我们将焦点图的生成解释为两类分类任务，并通过使用基于级联森林模型的方法来解决此问题。首先，我们从重叠的补丁中提取特定特征，以表示源图像的清晰度。为了获得焦点图，将特征向量输入到预先训练的级联森林模型中。然后，我们利用一致性检查来获取初始决策图。之后，将引导图像过滤用于边缘保留以细化决策图。最后，通过像素加权平均策略获得结果。大量的实验表明，该方法具有出色的视觉效果和出色的客观指标。