Infrared and visible image fusion with edge detail implantation

Abstract

Infrared and visible image fusion aims to integrate complementary information from the same scene images captured by different types of sensors into one image to obtain a fusion image with richer information. Recently, deep learning-based infrared and visible image fusion methods have been widely used. However, it is still a difficult problem how to maintain the edge detail information in the source images more effectively. To address this problem, we propose a novel infrared and visible image fusion method with edge detail implantation. The proposed method no longer improves the performance of edge details in the fused image through making the extracted features contain edge detail information like traditional methods, but by processing source image information and edge detail information separately, and supplementing edge details to the main framework. Technically, we propose a two-branch feature representation framework. One branch is used to directly extract features from the input source image, while the other is utilized to extract features of edge map. The edge detail branch mainly provides edge detail features for the source image input branch, ensuring that the output features contain rich edge detail information. In the fusion of multi-source features, we respectively fuse the source image features and the edge detail features, and use the fusion results of edge details to guide and enhance the fusion results of source image features so that they contain richer edge detail information. A large number of experimental results demonstrate the effectiveness of the proposed method.