Fusion of visible and infrared images using GE-WA model and VGG-19 network
Weiqiang Fan, Xiaoyu Li, Zhongchao Liu
Abstract
For the low computational efficiency, the existence of false targets, blurred targets, and halo occluded targets of existing image fusion models, a novel fusion method of visible and infrared images using GE-WA model and VGG-19 network is proposed. First, Laplacian is used to decompose the visible and infrared images into basic images and detail content. Next, a Gaussian estimation function is constructed, and a basic fusion scheme using the GE-WA model is designed to obtain a basic fusion image that eliminates halo of visible image. Then, the pre-trained VGG-19 network and the multi-layer fusion strategy are used to extract the fusion of different depth features of the visible and infrared images, and also obtain the fused detail content with different depth features. Finally, the fusion image is reconstructed by the basic image and detail content after fusion. The experiments show that the comprehensive evaluation FQ of the proposed method is better than other comparison methods, and has better performance in the aspects of image fusion speed, halo elimination of visible image, and image fusion quality, which is more suitable for visible and infrared image fusion in complex environments.