Infrared and visible light image fusion based on jump- connected convolutional layers

Infrared images have the advantages of clear object outlines and rich thermal information, but the vision is blurred and detailed information is lacking. Visible light images are the opposite. The two are complementary, and more valuable images can be obtained through fusion, making subsequent judgments easier. This paper proposes a fusion method based on jump connection convolution layer aiming at the problem of the low utilization rate of feature map and bad restoration effect of the original image of current fusion methods based on deep learning. By designing the network structure and loss function, this method guides the generated result graph to move closer to the source image with more gradient information. Experiments show that the fusion network can generate ideal results and has obvious advantages in the restoration of the source image.