## 1 Introduction
The most commonly used reconstruction-based algorithms are projection onto convex sets (POCS), maximum a posteriori (MAP) estimation, Bayesian analysis (BA), iterative back projection (IBP), and iterative back projection (IBP). Despite the addition of constraints to the image super-resolution reconstruction process through the introduction of a priori information, these methods yield satisfactory reconstruction results. However, these algorithms are afflicted by significant shortcomings, including suboptimal convergence. In certain application scenarios, they are even outperformed by the results of interpolation methods. The current state-of-the-art algorithms are based on learning methods. [PERSON] et al. [5] were the first to propose a convolutional neural network super-segmentation model, SRCNN, which uses only a three-layer neural network (CNN) to perform super-resolution reconstruction. This method demonstrates superior performance compared to the traditional SISR method, proving that neural networks are well-suited for image restoration and enhancement. Subsequently, a rapid super-resolution network model (FSRCNN) was developed, employing up-sampling and inverse convolution to reconstruct high-resolution images. This approach reduced the computational complexity and enhanced the performance of the network. [PERSON] et al. [6] proposed Very Deep Super-Resolution (VDSR), based on VGG-net, which increased the depth of the network to 20 layers and introduced skip-connectivity. The VDSR network, based on the VGG-net architecture, increases the depth of the network to 20 layers and introduces skip connections to enhance the stability of the training process and improve the quality of the reconstructed images. The enhanced deep super-resolution network (EDSR) eliminates the batch normalization (BN) layer from ResNet and increases the number of residual blocks and convolution kernels. Deep Back Projection Networks (DBPNs) incorporate the iterative back projection (IBP) algorithm into deep learning. Very deep residual channel attention networks (RCANs) employ a channel attention mechanism to optimize network channels, prioritizing those containing crucial details. The phenomenon of distortion following image encoding can be mitigated by super-resolution reconstruction using convolutional neural networks (CNNs) [7]. However, CNNs face two problems: quality degradation depends on image characteristics, which affects model performance, and CNNs struggle with transmission distortion. Image distortion in video codecs is typically caused by compression, transmission, and decoding errors and manifests itself as loss of detail, blurring, mosaicking, or color anomalies. The use of efficient codecs and optimized parameters can reduce the distortion, but deep learning methods based on Generative Adversarial Networks (GANs) can be used to repair the image distortion and improve the video quality prior to codec output. GAN [8] is a deep learning framework that generates new data through adversarial competition between generators and discriminators. Its structure is shown in Figure 3. The generator mimics real data patterns and creates fake data with the goal of making it impossible for the discriminator to distinguish between the data it generates and the real data. The discriminator, on the other hand, recognizes the authenticity of the data and scores the real data high and the fake data low. During the training process, the generator and the discriminator continuously optimize their strategies and eventually reach a balance where the generator data are of extremely high quality and the discriminator cannot distinguish them. However, a single GAN can lead to a homogenized style of image reduction, which may not reflect the characteristics of the whole dataset in a diverse way. The challenge of image reduction is, therefore, to produce images that faithfully reflect the details of the content data and also show the diverse stylistic features in the dataset rather than a single copy of a particular style.