# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 1 Introduction  
Super-Resolution (SR) has become a hot topic in the computer vision research community, since it reconstructs a high-resolution (HR) image from the low-resolution (LR) information provided. SR has a wide range of applications in medical imaging, and in security and surveillance, where high-frequency details are required on demand [1]. The dilemma of SR is that it is an ill-posed inverse problem, since multiple HR patches are consistent with the given LR patch. To address this issue, additional prior knowledge has to be made regarding the formation of the desired HR images. Recently, deep-learning methods have exhibited excellent performance in SR tasks. The data-driven method has also been used with large improvements in accuracy; this includes convolutional neural network (CNN)-based methods [2]. For example, the pioneer CNN model for SR has gained considerable attention because of its portable architecture. This method [3], termed Super-Resolution Convolutional Neural Network (SRCNN), provides compelling quality and outperforms traditional non-deep-learning algorithms. Subsequently, many follow-up methods have shown their advantages for SR tasks. [PERSON] et al. [4] deployed gradient clipping and residuals-learning to predict the residuals instead of actual pixels. [PERSON] et al. [5] proposed the Deep Laplacian Pyramid Network (LapSRN) by upscaling from a small to a large upscaling factor. They progressively predicted the sub-band residuals ranging from coarse to fine levels. With the further development of CNNs, they deployed some skip-connections to improve the image quality. The Deep Recursive Residual Network (DRRN) jointly utilizes skip-connections to fully exploit the latentinformation [6]. At present, the real-time performances of existing networks are poor, the sample data are difficult to obtain, and the restoration effect is not ideal. To date, convolutional neural networks have become increasingly powerful in computer vision applications. However, there has been less attention paid to CNNs that discriminate whether the extracted features are robust, based on their potential to train high-dimensional, complex real data [7]. There are two outstanding algorithms, Variational AutoEncoders (VAE) and Generative Adversarial Networks (GANs), that have excellent performances compared with state-of-the-art algorithms for image processing. The VAE [8] is an attractive model, since it learns complex probability distributions from training data. However, the high-quality images significantly depend on the expressiveness of the inference model. In other words, the VAE is not expressive enough when it is trained on true posterior distributions. The GANs [9] mimic the target distribution through the construction of a generative model. The networks represent two parts of the generator and discriminator to extract features for SR tasks. The algorithm of the GAN may be characterized as a two-player minimax game between the generative model, which tries to produce counterfeit data without detection, and the discriminative model, which learns to distinguish between the synthesized images from the generator and the real images from data distribution. On the one hand, a noise variable z can be defined as the generative model input. [PERSON] et al. then incorporated the noise z into the model G to generate the synthesized images G(z). Furthermore, the parameters of network G can be optimized constantly based on the feedback information given by the discriminator. On the other hand, the discriminative model D can be viewed as mappings from the data distribution: \(D(x)\underset{\text{discriminator}}{\longrightarrow}(0,1)\). It determines whether the images are from the generator (false, close to 0) or from the data distribution (true, close to 1). For the discriminative model D, training the parameters of D via fixing the generator can classify images. Specifically, they achieve this strategy with the utility of two joint adversarial networks.