Graph-Based Representation Of Learned Images Super Resolution Reconstruction Algorithm And System

With the continuous advancement of science and technology and the times,people’s requirements for image quality are getting higher and higher.High-resolution images provide more detailed information and a better visual experience than low-resolution images.Image super-resolution reconstruction technology has garnered significant attention due to its extensive theoretical and practical applications and its status as a cutting-edge research area in computer vision.This technology finds wide application in medical imaging,satellite remote sensing,image compression,and other fields.The graph representation learning method is superior for addressing the image super-resolution reconstruction problem.While deep learning has been extensively studied and achieved significant performance on single image super-resolution(SISR),existing convolutional neural networks focus on broader and deeper architectural design,ignoring the potential relationship between details and features of the image itself.Several attempts have recently been made to solve SISR problems with graph representation learning.However,the existing GNN-based methods to deal with SISR problems are limited to the information processing of the whole image or between different features of the same layer.Ignoring the interdependence between the features of different layers is not conducive to extracting deeper layers’ features.In summary,this paper proposes a novel image super-resolution reconstruction algorithm,which combines the advantages of deep convolutional neural network and graph neural network in an innovative form.Finally,it is applied to the image super-resolution reconstruction system based on graph representation learning,and the specific research content is as follows:(1)With the aim of addressing the inadequacy of current algorithms that overlook the interdependence between different layers,a novel image super-resolution reconstruction method based on graph representation learning has been proposed,which is based on.Graph representation learning is proposed.It comprises a layer feature map representation learning module and a channel space attention module.The layer feature map indicates that the learning module mainly captures the interdependence between different layer features and can learn more fine-grained image detail features.In addition,we integrate both the channel and spatial attention modules into the model for a more streamlined approach.,considering the channel dimension and spatial scale information to improve the expression ability and achieve high-quality image details.A large number of experimental and ablation studies prove the superiority of the algorithm.(2)A system for image super-resolution reconstruction has been designed and implemented,utilizing graph representation learning.The system employs the image super-resolution reconstruction algorithm proposed in this paper,based on graph representation learning,to effectively reconstruct low-resolution images.The main functional modules of the system include the system function introduction module,data management module,algorithm introduction module,operation and visualization module,etc.In summary,our work focuses on addressing the challenges present in image super-resolution reconstruction.This paper presents an innovative algorithm for effectively reconstructing high-resolution images founded on graph representation learning principles.It provides a new model framework for the image super-resolution reconstruction algorithm and applies it to the system.