Multi-focus image fusion is the activity of synthesizing multiple images of different focusing settings to construct a fully focused image. Many of the latest methods for image fusion rarely consider the structural differences between the guidance image and the input image, and do not retain well the important source image features while producing a fully focused image. To address this issue, a method exploiting a combination of static and dynamic filters (SDF) is proposed herein. This combination has good edge smoothing characteristics and strong robustness against artifacts such as gradient inversion and global strength migration. First, SDF is utilized in order to decompose the source image into structure and texture layers. Secondly, a morphological gradient operator filter is used to calculate the significance map of different levels of the source. Thirdly, the maximum pixel value of the significance map is used to construct the binary decision graph of the two source images. Then, the structure and texture layers are fused with the aid of the binary decision graph, and subsequently the final fusion image is created by combining the fused structure layer and texture layer. This process ensures that spatial consistency is preserved. Tests on grayscale and color multi-focus image sets show that the proposed method has better performance than that of any of the existing methods according to both objective and subjective evaluation.

多聚焦图像融合是合成具有不同聚焦设置的多幅图像以构建完全聚焦图像的活动。许多最新的图像融合方法很少考虑引导图像和输入图像之间的结构差异，并且在生成完全聚焦的图像时不能很好地保留重要的源图像功能。为了解决这个问题，本文提出了一种利用静态和动态滤波器（SDF）的组合的方法。这种组合具有良好的边缘平滑特性，并且对诸如梯度反演和整体强度偏移等伪影具有很强的鲁棒性。首先，利用SDF以便将源图像分解为结构层和纹理层。其次，形态学梯度算子滤波器用于计算源的不同级别的重要性图。第三，利用有效图的最大像素值构造两个源图像的二元决策图。然后，借助二进制决策图将结构层和纹理层融合，随后通过组合融合的结构层和纹理层创建最终融合图像。此过程可确保保留空间一致性。对灰度和彩色多焦点图像集的测试表明，根据客观和主观评估，该方法的性能优于任何现有方法。借助二元决策图将结构层和纹理层融合，然后通过组合融合的结构层和纹理层创建最终融合图像。此过程可确保保留空间一致性。对灰度和彩色多焦点图像集的测试表明，根据客观和主观评估，该方法的性能优于任何现有方法。借助二元决策图将结构层和纹理层融合，然后通过组合融合的结构层和纹理层创建最终融合图像。此过程可确保保留空间一致性。对灰度和彩色多焦点图像集的测试表明，根据客观和主观评估，该方法的性能优于任何现有方法。