Multifocus image fusion using superpixel segmentation and superpixel-based mean filtering

To achieve better performance in multifocus image fusion problems, a new regional approach based on super-pixels 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. (C) 2016 Optical Society of America