An Efficient Network Model for Visible and Infrared Image Fusion

Visible and infrared image fusion (VIF) aims at remodeling an informative and panoramic image for subsequent image processing or human vision. Due to the widespread application in military and civil fields, the VIF technology has achieved considerable development in recent decades. However, the assignment of weights and the selection of fusion rules seriously restrict the performance improvement of most existing fusion algorithms. In response to this issue, an innovative and efficient VIF model based on convolutional neural network (CNN) is proposed in this paper. Firstly, multi-layer convolution kernel is performed on two source images with a multi-scale manner for extracting the salient image features. Secondly, the extracted feature maps are concatenated along the number of channels. Finally, the fusion feature maps are reconstructed to achieve the fusion images. The main innovation of this paper is to adequately preserve meaningful details and adaptively integrate features information driven by source image information in CNN learning model. In addition, in order to adequately train the network model, we generate a large-scale and high-resolution image training dataset based on COCO dataset. Compared with the existing fusion methods, experiment results indicate that the proposed method not only achieves universally outstanding visual quality and objective metrics but also has some advantages in terms of runtime efficiency compared to other neural network algorithms.