Research On Image Super-Resolution Reconstruction Based On Attention Mechanism And Generative Adversarial Networks

Image super-resolution refers to a state-of-the-art technology that utilizes software methods to enhance the resolution of an image,resulting in a substantial increase in its size.This sophisticated technique not only enhances the image sharpness but also amplifies the image details,leading to a clearer,more natural,and visually appealing image.The technology is of significant practical importance in various fields,such as satellite remote sensing,digital forensics,and video surveillance.This paper focuses on the development of super-resolution reconstruction techniques for single images.The primary research objectives of current image super-resolution algorithms are discussed,which include improving the reconstruction quality and enhancing the visual appearance of the resulting images.To achieve these goals,this paper surveys the current state of research in image super-resolution techniques,categorizing them into deep learning-based approaches and adversarial network-based approaches.Deep learning-based techniques aim to improve the quality of the reconstruction metrics and ensure network stability,while adversarial network-based techniques excel at capturing high-frequency details and texture patterns.Existing challenges are identified,including the need to improve the ability of deep networks to extract information from feature maps and to avoid over-smoothness and poor visual quality of the output images generated by pixel-based loss algorithms.To address these challenges,this paper investigates the use of attention mechanisms and adversarial networks in image super-resolution reconstruction techniques.The research in this paper can be divided into two main aspects.(1)The first part of our study aims to propose an improved network model for enhancing reconstruction quantization index in image super-resolution reconstruction.An integrated method that employs the attention mechanism and iterative feedback is presented.Specifically,the method utilizes frequency decomposition at the feature extraction stage of the model and introduces an attention mechanism that weights specific parts of the feature map to concentrate the network on the more significant areas of the feature map.Moreover,an iterative feedback mechanism is incorporated into the feature fusion process,exploiting the interdependence between LR and HR images to enhance high-frequency information recovery in high-magnification super-resolution tasks.Experimental evaluations conducted on various standard datasets demonstrate that the proposed method can achieve superior reconstruction results,in terms of both quantitative metrics and visual effects,compared to state-of-the-art algorithms on benchmark datasets.(2)The second part of our study proposes a method to enhance the visual effect of the reconstructed images and address the problems of losing high-frequency texture information and overly smooth output images with the pixel-by-pixel loss-dominated reconstruction method.To achieve this,the authors improve the structure of the loss function and model discriminator while introducing an adversarial network.The hybrid gradient loss is used to maintain the original structure and form of the image,while a perceptual loss is added to complement the high-frequency information and recover the detailed information while maintaining the image structure.Furthermore,a fine-grained discriminator is used to better preserve the information in the original image.The proposed method is experimentally evaluated on multiple datasets and the results demonstrate that it produces naturally realistic and visually appealing images that outperform multiple comparison algorithms in capturing texture information and producing visually realistic images.