MLFFusion: Multi-level feature fusion network with region illumination retention for infrared and visible image fusion

Abstract

Infrared image and visible light image fusion is widely used in night vision, surveillance, military and other fields. The focus of the fusion task is to integrate complementary information in visible and infrared light images and eliminate redundant information. In addition, most of the fusion tasks are performed in the harsh environment of low light, and it is worth studying how to maintain the lighting information of the fusion results. In order to solve the problems existing, firstly, we designs a multi-level feature module to fusion multi-source information. Different from the parallel layer fusion strategy of the traditional network, we proposed a fusion strategy combine parallel layers and depth layers. Secondly, we add attention computing to the feature extraction network to improve the performance of the feature extraction network. Thirdly, in order to make the fusion image have good illumination information, we design the area illumination retention module, improved the performance of the fusion algorithm in low-light environments. A large number of experiments show that the proposed method has excellent performance and will perform better in low light environment. In addition, the proposed algorithm also shows great potential in multi modal object detection.