LPGAN: A LBP-Based Proportional Input Generative Adversarial Network for Image Fusion

Abstract

Image fusion is the process of combining multiple input images from single or multiple imaging modalities into a fused image, which is expected to be more informative for human or machine perception as compared to any of the input images. In this paper, we propose a novel method based on deep learning for fusing infrared images and visible images, named the local binary pattern (LBP)-based proportional input generative adversarial network (LPGAN). In the image fusion task, the preservation of structural similarity and image gradient information is contradictory, and it is difficult for both to achieve good performance at the same time. To solve this problem, we innovatively introduce LBP into GANs, enabling the network to have stronger texture feature extraction and utilization capabilities, as well as anti-interference capabilities. In the feature extraction stage, we introduce a pseudo-Siamese network for the generator to extract the detailed features and the contrast features. At the same time, considering the characteristic distribution of different modal images, we propose a 1:4 scale input mode. Extensive experiments on the publicly available TNO dataset and CVC14 dataset show that the proposed method achieves the state-of-the-art performance. We also test the universality of LPGAN by fusing RGB and infrared images on the RoadScene dataset and medical images. In addition, LPGAN is applied to multi-spectral remote sensing image fusion. Both qualitative and quantitative experiments demonstrate that our LPGAN can not only achieve good structural similarity, but also retain richly detailed information.

Keywords

• image fusion
• generative adversarial network (GAN)
• local binary patterns (LBP)
• multi-modal images