A multifocus image fusion method based on boundary-aware and adaptive fusion of experts

The purpose of multi-focus image fusion is to extract all the features of complementary images to obtain a fusion image with complete image information. The fusion method based on decision map is widely used in multi-focus image fusion, because it can retain the information of the original image to the maximum extent. However, the method based on decision map often has the problem of misjudgment at the edge of clear region and fuzzy region, which affects the accuracy of fusion image. In order to solve the problem of misjudgment in the edge region based on the decision map scheme, we introduce differential convolution to improve the sensitivity of the network to the edge gradient, improve the judgment ability of the network in the edge region, and improve the accuracy of the decision map. Moreover, an efficient feature fusion method is essential for improving network performance. To ensure the quality of feature fusion results, we introduce a mixture fusion expert to guide the image feature fusion process, whose unique gating mechanism guarantees the efficiency of feature fusion. Extensive experiments have demonstrated that our proposed method achieves superior results compared to most existing state-of-the-art methods.