With the advent of biomedical imaging systems and the rapid advancements in light microscopy, multi-focus image fusion has provided significant attention in creating a fused image with improved depth-of-field (DOF). The fused image plays an imperative role in different computer vision based applications. Firstly, the proposed algorithm decomposes the source multi-focus images into base layer (BL) and detail layer (DL) using Guided Filter (GF). BL retains large-scale intensity variation and DL is the residual approximation of the source images. Then, a criterion function is developed for in-focus region detection across a set of source multi-focus images. In particular, edge-aware filtering based texture analysis is utilized to detect in-focus saliency maps. These saliency maps are further refined using a guided co-occurrence filter (GCOF) to decide fusion rules. Separate fusion rules for BL fusion and DL fusion are computed adaptively that improve the capability of the proposed fusion process. Finally, using a weighted average fusion (WAF) technique the fusion rules are utilized to construct all-in-focus fused image. The quantitative and qualitative analysis of experimental results for various microscopy stacks and standard photographic multi-focus data sets are discussed to demonstrate the performance of the proposed method.

随着生物医学成像系统的出现和光学显微镜的快速发展，多焦点图像融合在创建具有改进景深（DOF）的融合图像方面引起了极大的关注。融合的图像在不同的基于计算机视觉的应用程序中起着至关重要的作用。首先，该算法利用导引滤波器（GF）将源多焦点图像分解为基础层（BL）和细节层（DL）。BL保留了大规模的强度变化，而DL是源图像的残差近似值。然后，开发用于在一组源多焦点图像上进行对焦区域检测的标准功能。特别地，基于边缘感知过滤的纹理分析被用于检测聚焦显着图。使用引导共现滤波器（GCOF）进一步完善这些显着图，以决定融合规则。自适应地计算了BL融合和DL融合的单独融合规则，从而提高了所提出融合过程的能力。最后，使用加权平均融合（WAF）技术，融合规则可用于构建全焦点融合图像。讨论了各种显微镜堆栈和标准照相多焦点数据集的实验结果的定量和定性分析，以证明该方法的性能。