Infrared and Visible Image Fusion Based on Improved Dual Path Generation Adversarial Network

An end-to-end dual fusion path Generation Adversarial Network (GAN) is proposed to preserve more information from the source image. Firstly, in the generator, a double path dense connection network with the same structure and independent parameters is used to construct the infrared difference path and the visible difference path to improve the contrast of the fused image, and the channel attention mechanism is introduced to make the network focus more on the typical infrared targets and the visible texture details; Secondly, two source images are directly input into each layer of the network to extract more source image feature information; Finally, considering the complementarity between the loss functions, the difference intensity loss function, the difference gradient loss function and the structural similarity loss function are added to obtain a more contrast fused image. Experiments show that, compared with a Generative Adversarial Network with Multi-classification Constraints (GANMcC), Residual Fusion network for infrared and visible images (RFnest) and other related fusion algorithms, the fusion image obtained by this method not only achieves the best effect in multiple evaluation indicators, but also has better visual effect and is more in line with human visual perception.