# Generative Adversarial Network for Image Super-Resolution Combining Texture Loss
## 1 Introduction  
With the popularization of Internet and the development of information technology, the amount of information accepted by human is growing at an explosive rate. Images, videos and audio are the main carriers of information transmission. Related research [1] has pointed out that the information humans receive through vision accounts for 60%-80% of all media information, so visible images are an important way to obtain information. However, the quality of an image is often restricted by hardware equipment such as imaging system and the bandwidth during image transmission process. A low-resolution (LR) image with missing details is eventually presented. The reduction of image resolution will cause a serious decrease in image quality. It will greatly affect people's visual experience and cannot meet the requirements for image quality performance indicators in industrial production. Therefore, how to obtain high-resolution (HR) images has become an urgent issue. At present, there are mainly two ways to improve image resolution. The first is to upgrade hardware devices such as image sensors and optics, but this method is too costly and difficult to promote in practical applications. The other is Images Super-Resolution Reconstruction (ISRR) technology which inputs LR images and generates HR images by using machine learning algorithms and digital image processing technology. It has been widely used in fields such as the medical field, communication field, public safety field and remote sensing imaging field for their low cost and practical application values. The core of original ISRR algorithms are to use the information of neighboring pixels to estimate the pixels of HR images. Typical algorithms include nearest-neighbor interpolation [2], bilinear interpolation [3] and bicubic interpolation [4]. Their disadvantage is that they do not take into account the semantics of entire image, resulting in the lack of high-frequency details in the reconstructed images. Subsequently, the reconstruction-based ISRR algorithm has been researched and developed. It introduces image priors or constraints between HR and LR images and uses sample information to infer the distribution of real data. Common ISRR algorithms based on reconstruction include convex set projection method [5], iterative back projection method [6] and maximum posterior probability estimation method [7]. Such methods are subject to computational resources and prior conditions when reconstructing images and are unable to produce satisfactory high-quality images. In order to obtain higher quality reconstructed images, the learning-based ISRR algorithm has been proposed and developed rapidly. It makes full use of information in image sample library to learn the mapping relationship between HR and LR image. According to different design strategies, it is mainly divided into ISRR algorithms based on sparse representation and deep learning. [PERSON] et al. [8] have applied sparse representation theory to ISRR. [PERSON] et al. [9] have proposed a refined local learning scheme to reduce the image artifacts and further improve the image visual quality. Similar algorithms for reconstructing images by learning mapping relationships include Bayesian process estimation [10], statistical learning [11] and linear regression representation algorithm [12]. The matrix or tensor decomposition algorithms that yield low-rank approximations have been developed for various image completion and resolution up-scaling problems. [PERSON] et al. [13] have introduced tensor-factorization-based approach which offers a fast solution without the use of known image pairs or strict prior assumptions to solve ISRR task. To tackle the obstacles of low-rank completion methods, [PERSON] et al. [14] have proposed to model the incomplete images with overlapping blocks of Tucker decomposition representations. In recent years, methods based on deep learning have developed rapidly. Since [PERSON] et al.