# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 2 Related Work  
The interpolation method is an early technique utilized for image super-resolution reconstruction. The method involves using known low-resolution discrete pixel points as input and calculating the values of unknown pixel points for extension. Traditional linear interpolation algorithms, such as Nearest Interpolation [1], Bilinear Interpolation [2], and Bicubic Interpolation [3], are commonly employed. The interpolation method is straightforward and easy to implement; however, these methods can cause issues, such as excessive image smoothing and loss of high-frequency information. This is because the interpolation method solely focuses on the change in pixel value and does not consider the relationships between pixels. [PERSON] et al. [4]. proposed the SRCNN (Super-Resolution Convolutional Neural Network) algorithm in 2014, marking the first utilization of a CNN network model for super-resolution tasks. The SRCNN consists of only three convolutional layers. Authors employed the Bicubic interpolation method to downsample the target images, generating the preprocessed low-resolution images. These images were then upscaled using bicubic interpolation before being fed into the SRCNN network for image recovery. CNNs are primarily adept at processing local information and are less effective at modeling long-term dependencies [5]. Furthermore, utilizing the same convolutional kernel to process texture information across different regions of an image may not facilitate optimal image recovery. Unlike CNNs, the transformer model's self-attention mechanism places a stronger emphasis on capturing global information connections [6]. In 2021, MSRA proposed Swin Transformer [7], which incorporates a shifted window mechanism to partition the image into multiple overlapping windows for attention computation. The method enhances the locality of convolution and improves the model's ability to comprehend global information through multiple layers of attention computations. Building upon this advancement, in 2021, researchers proposed SwinIR [8], a super-resolution model based on the Swin Transformer. SwinIR utilizes convolutional networks to extract shallow features from low-resolution images, which are then passed through residual Swin Transformer blocks for deep feature extraction. The model subsequently employs an image reconstruction module to generate high-resolution images. Compared to previous methods, SwinIR demonstrates superior feature extraction and image reconstruction capabilities, leading to enhanced quality for the super-resolution images. Based on SwinIR, researchers proposed the HAT [9] model, which had shown an improved performance in super-resolution tasks in 2022. Drawing inspiration from SwinIR, the model proposed in this paper employs residual Swin Transformer blocks (RSTBs) to extract deep features from images. By introducing RSTBs, the generator benefits from the ability of CNNs to process large-size images and capture long-term dependencies within the model. To help focus high-frequency information in the iris region and address the issue of information loss during upsampling, sub-pixel convolutional progressive upsampling [10; 11] is utilized. This approach gradually increases the resolution of the images by a factor of two at a time. However, this scheme provides insufficient constraints on texture recovery and still suffers from high-frequency detail loss. Super-resolution models based on adversarial generative networks (GAN) offer enhanced generative capabilities [12], resulting in images with improved visual quality and higher-frequency details. The SRGAN [13] model was the first to introduce GANs into the super-resolution field. Building upon SRGAN, [PERSON] et al. proposed the ESRGAN model [14], aiming to further improve image quality and realism. ESRGAN employs a deep residual network to extract image features from low-resolution images and utilizes a relative discriminator (RaGAN) to assess image realism.