# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 1 Introduction  
The research of image fusion is of great significance, which can composite particular and complementary information of different sensors to generate the fused results of the same scene [1]. Fused images with compositive information help to deal with image pre-processing, target recognition, and image classification [2], and improve the stability and robustness of the system. Nowadays, image fusion has been used in various conditions, including multi-focus image fusion, multi-exposure image fusion, panchromatic and multi-spectral image fusion, multi-spectral and hyper-spectral image fusion, medical image fusion, and so on. Infrared and visible image fusion, which belongs to multi-modality image fusion, has already been used in image enhancement [3], surveillance [4], remote sensing [5], medical research, etc. The images taken by infrared tensors and visible light tensors focus on different characteristics. Infrared images reflect the thermal radiation of objects [6] and have strong detective capability, making it possible to find targets in bad weather or hidden behind obstructions. However, because of the limits of the imaging principle, infrared images usually have low resolution and few details. Visible images generally have high resolution, evident contrasts, and abundant textures andthey are defined in a manner consistent with the human visual system. But it is difficult to get a clear photograph when environmental conditions are not good. Therefore, many researchers have been committed to the subject of infrared and visible image fusion techniques to obtain comprehensive images for further applications. According to the fusion strategies and theories adopted [6], several representative infrared and visible image fusion algorithms have been proposed, including multi-scale transform- [7; 8; 9], sparse representation- [10; 11], neural network- [12; 13], subspace- [14; 15], and saliency-based [16; 17] methods, hybrid models [18; 19], and other methods [20; 21]. These methods are widely used and still studied by many researchers. Generally, these basic methods are almost conventional image fusion methods which consist of several critical components: image transform, activity level measurements, fusion rules, etc. [22]. As far as it goes, although there are many flexible transforms proposed, it is still hard to find the appropriate activity level measurements and fusion rules to obtain well-fused images. Meanwhile, all these components are designed in a manual way, and the implementation difficulty and computational cost have also become troublesome problems. In the last few years, research on deep learning (DL) has become more and more extensive, especially in the field of image processing. DL has already been used in image classification, recognition, detection, and fusion tasks. DL-based image fusion methods can extract deep features automatically to overcome the difficulties caused by manual measurements and assignments, making the whole fusion process become easier. [PERSON] et al. [23; 24] first proposed the methods to use convolutional neural networks (CNN) of DL for infrared and visible image fusion. Last year, [PERSON] et al. [2; 25] presented some other DL frameworks to fuse infrared and visible images. The above-mentioned DL-based methods are not end-to-end models and they all need to train on datasets with visible images in advance. No matter whether the network weights are generated by training or provided by mature feature extraction models, other transforms or operations are still needed to accomplish the final fusion process. Lately, [PERSON] et al. [26] came up with a novel infrared and visible image fusion method based on a generative adversarial network (GAN), a kind of DL-based method which has attracted much attention these two years. The fusion model works just like an adversarial game, between keeping the infrared thermal radiation information and preserving the visible appearance texture information [26], and the results are fine.