From Early Models to Modern Techniques: A Deep Learning Survey on Single Image Super-Resolution

Faculty of Arts, McGill University, Montreal, H8N 0H5 Quebec, Canada

^* Corresponding author: lihaorui917@gmail.com

Abstract

The primary goal of Single Image Super-Resolution (SISR), a fundamental yet challenging computer vision task with several practical applications in domains such as surveillance, medical imaging, and remote sensing, is to reconstruct a high-resolution (HR) image from a single low- resolution (LR) input. The performance of SISR has been greatly improved by the advent of deep learning, specifically Convolutional Neural Networks (CNNs) and Transformer architectures. An extensive review of deep learning-based SISR techniques is presented in this study. Begin by formulating the SISR problem and discussing prevalent evaluation metrics that balance distortion (e.g., PSNR) and perceptual quality (e.g., SSIM, LPIPS). Subsequently, classifying and analyzing key methodologies across five categories: interpolation-based and traditional models, CNN-based architectures (e.g., SRCNN, VDSR, EDSR), GAN-based frameworks (e.g., SRGAN, ESRGAN, Real-ESRGAN), attention-enhanced networks (e.g., RCAN), and Transformer-based approaches (e.g., SwinIR, HAT). In each category, the theoretical framework, design innovations, and corresponding advantages and limitations are explored. By showing architectural design strategies and training paradigms, this review highlights a structured understanding of the significant evolution from early CNNs to sophisticated GANs and Transformers in SISR, serving as a reference for future model development and practical deployment.