Duan Junwei, Chen Long, Philip Chen C L
Appl Opt. 2016 Dec 20;55(36):10352-10362. doi: 10.1364/AO.55.010352.
To achieve better performance in multifocus image fusion problems, a new regional approach based on superpixels and superpixel-based mean filtering is proposed in this paper. First, a fast and effective segmentation method is adopted to generate the superpixels over a clarity-enhanced average image. By averaging the clarity information in each superpixel, we make the initial decision map of fusion by regionally selecting sharper superpixels in different source images. Then a novel superpixel-based mean filtering technique is introduced to make full use of spatial consistency in images and the final post-processed decision map is produced. The fused image is constructed by selecting pixels from different source images according to the final decision map. Experimental results demonstrate the proposed method's competitive performance in comparison with state-of-the-art multifocus image fusion approaches.
为了在多聚焦图像融合问题中获得更好的性能，本文提出了一种基于超像素和基于超像素的均值滤波的新区域方法。首先，采用一种快速有效的分割方法在清晰度增强的平均图像上生成超像素。通过对每个超像素中的清晰度信息进行平均，我们通过在不同源图像中区域选择更清晰的超像素来生成融合的初始决策图。然后引入一种新颖的基于超像素的均值滤波技术，以充分利用图像中的空间一致性，并生成最终的后处理决策图。根据最终决策图从不同源图像中选择像素来构建融合图像。实验结果表明，与现有最先进的多聚焦图像融合方法相比，该方法具有竞争力。
