Getting a complete description of scene with all the relevant objects in focus is a hot research area in surveillance, medicine and machine vision applications. In this work, transform based fusion method called as NSCT-FMO, is introduced to integrate the image pairs having different focus features. The NSCT-FMO approach basically contains four steps. Initially, the NSCT is applied on the input images to acquire the approximation and detailed structural information. Then, the approximation sub band coefficients are merged by employing the novel Focus Measure Optimization (FMO) approach. Next, the detailed sub-images are combined using Phase Congruency (PC). Finally, an inverse NSCT operation is conducted on synthesized sub images to obtain the initial synthesized image. To optimize the initial fused image, an initial decision map is first constructed and morphological post-processing technique is applied to get the final map. With the help of resultant map, the final synthesized output is produced by the selection of focused pixels from input images. Simulation analysis show that the NSCT-FMO approach achieves fair results as compared to traditional MST based methods both in qualitative and quantitative assessments.

中文翻译：

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基于NSCT和焦点测量优化的多焦点图像融合

获得所有相关对象的完整场景描述是监控、医学和机器视觉应用中的一个热门研究领域。在这项工作中，引入了称为 NSCT-FMO 的基于变换的融合方法来整合具有不同焦点特征的图像对。NSCT-FMO 方法基本上包含四个步骤。最初，NSCT 应用于输入图像以获取近似和详细结构信息。然后，通过采用新颖的聚焦测量优化 (FMO) 方法合并近似子带系数。接下来，使用相位一致性 (PC) 组合详细的子图像。最后，对合成的子图像进行逆NSCT运算，得到初始合成图像。为了优化初始融合图像，首先构建初始决策图，并应用形态学后处理技术来获得最终图。在合成地图的帮助下，最终的合成输出是通过从输入图像中选择聚焦像素来产生的。模拟分析表明，与传统的基于 MST 的方法相比，NSCT-FMO 方法在定性和定量评估方面都取得了公平的结果。