A Visible and Infrared Image Fusion Method based on Generative Adversarial Networks and Attention Mechanism

A new fusion method for visible and infrared images based on generative adversarial networks is proposed to solve the problem of recognizing targets in low-light images; the method can be directly applied to the fusion of RGB three-channel visible images and infrared images. In generative adversarial networks, the generator adopts a U-Net structure with encoding and decoding layers. The discriminator adopts a Markovian discriminator, and the attention mechanism is introduced to force the fused image to pay more attention to the high-intensity information on infrared images. The experimental results show that the proposed method not only maintains the detailed texture information of visible images but also introduces the main target information of infrared images to generate fusion images with good visual effects and high target identification, and it performs well in information entropy, structural similarity, and other objective indexes.