A Review of Deep Learning-Based Image Super-Resolution Reconstruction Methods

Image Super-Resolution (SR) technology aims to reconstruct High-Resolution (HR) images from Low-Resolution (LR) images, holding significant application value in fields such as medical imaging analysis, satellite remote sensing, video enhancement, and security surveillance. In recent years, deep learning methods have significantly advanced the development of image super-resolution technology due to their powerful feature extraction capabilities. This paper systematically reviews the current research status of Single Image Super-Resolution (SISR) technology, focusing on three mainstream deep learning frameworks: Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Transformers, and summarizes their latest research progress. Firstly, the paper introduces the fundamental principles of traditional super-resolution methods and their limitations in complex scenarios. Secondly, it provides a detailed analysis of the network architectures, optimization strategies, and performance advantages of various deep learning-based super-resolution models. Finally, the paper discusses the challenges currently faced by deep learning-based super-resolution technology and outlines potential future research directions.