The Fusion of Unmatched Infrared and Visible Images Based on Generative Adversarial Networks
Yuqing Zhao, Guangyuan Fu, Hongqiao Wang, Shaolei Zhang
Abstract
Visible images contain clear texture information and high spatial resolution but are unreliable under nighttime or ambient occlusion conditions. Infrared images can display target thermal radiation information under day, night, alternative weather, and ambient occlusion conditions. However, infrared images often lack good contour and texture information. Therefore, an increasing number of researchers are fusing visible and infrared images to obtain more information from them, which requires two completely matched images. However, it is difficult to obtain perfectly matched visible and infrared images in practice. In view of the above issues, we propose a new network model based on generative adversarial networks (GANs) to fuse unmatched infrared and visible images. Our method generates the corresponding infrared image from a visible image and fuses the two images together to obtain more information. The effectiveness of the proposed method is verified qualitatively and quantitatively through experimentation on public datasets. In addition, the generated fused images of the proposed method contain more abundant texture and thermal radiation information than other methods.