R2F-UGCGAN: a regional fusion factor-based union gradient and contrast generative adversarial network for infrared and visible image fusion

Abstract

ABSTRACT To efficiently preserve texture and target information in source images, an image fusion algorithm of Regional Fusion Factor-Based Union Gradient and Contrast Generative Adversarial Network (R2F-UGCGAN) is proposed. Firstly, an adaptive gradient diffusion (AGD) decomposition algorithm is designed to extract representative features. A pair of infrared (IR) and visible (VIS) images are decomposed by AGD to obtain low-frequency components with salient targets and high-frequency components with rich edge gradient information. Secondly, In the high-frequency components, principal component analysis (PCA) is used for fusion to obtain more detailed images with texture gradients. R2F-UGCGAN is used to fuse the low-frequency components, which can effectively ensure good consistency between the target region and the background region. Therefore, a fused image is produced, which inherits more thermal radiation information and important texture details. Finally, subjective and objective comparison experiments are performed on TNO and RoadScene datasets with state-of-the-art image fusion methods. The experimental results of R2F-UGCGAN are prominent and consistent compared to these fusion algorithms in terms of both subjective and objective evaluation.