EdgeFusion: Infrared and Visible Image Fusion Algorithm in Low Light

Under low-light conditions, visible light imaging technology exhibits poor imaging performance, whereas infrared thermal imaging technology can effectively detect and identify targets. To solve the target imaging problem in low-light environments, multimodal image fusion technology can combine the advantages of both aforementioned methods. Existing fusion methods focus excessively on information from infrared images, obscuring the original texture details of the targets and resulting in low-quality images. Therefore, in this study, we propose a multiscale edge-fusion network for infrared and visible images called EdgeFusion, which can produce an edge-fusion image. Specifically, the network utilises infrared multiscale gradient information to enhance the edges of the thermal targets, thereby improving the ability to identify them. By designing a balanced loss, EdgeFusion suppresses the global information from infrared images that obscured the fine texture details of the original images. In addition, a residual gradient method is introduced to enhance the textural details of the generated images. After extensive experimentation on the public datasets LLVIP and TNO, the results indicate that EdgeFusion outperforms existing state-of-the-art methods in preserving fine-grained infrared edges and enhanced image texture details.