Multi-focus image fusion based on visual depth and fractional-order differentiation operators embedding convolution norm

Multi-focus image fusion technology integrates the focused regions of multiple source images to produce a single, all-in-focus image. However, existing methods have drawbacks, including image artifacts, color distortion, and ambiguous boundaries. In this paper, a spatial-domain two-stage fusion approach is proposed to address these challenges. In the first stage, a fractional-order differentiation operator embedding convolution norm is proposed to amplify pixel texture, while a weighted fusion is applied to obtain an initial fusion result. Here, the absolute difference map between initial fusion result and source images is used as the focus information, ensuring the accuracy of initial decision map. During the second stage, the source images and pseudo-depth information are jointly constructed the feature vector of K-nearest neighbors matting (KNNM) algorithm to refine the decision map, aiming to obtain final decision map with smoother boundaries. Experimental results indicate that the proposed method outperforms existing representative algorithms in both qualitative and quantitative evaluations . • Introducing convolution norm into the multi-focus image fusion field. • Discussing the feasibility of fractional-order differentiation embedding convolution norm. • The visual depth is crafted by depth information, aiming to fuse color information. • Combining depth information into KNNM algorithm to obtain a refined decision map.