An Extensive Review of Developments and Methods in Super-Resolution Image Reconstruction

Abstract

Super-resolution (SR) image reconstruction plays a vital role in enhancing the resolution of low-resolution (LR) images, benefiting various fields such as remote sensing, medical imaging, and surveillance. The SR problem entails reconstructing high-resolution (HR) images from LR inputs, typically due to the loss of high-frequency information and the problem's ill-posed nature. This review study presents a comprehensive overview of recent developments and approaches in SR image reconstruction. This study primarily presents three approaches: methods based on learning, methods based on reconstruction, and methods based on interpolation. Despite the fact that interpolation-based approaches, such as bicubic interpolation, are straightforward and quick, they frequently result in blurring and loss of high-frequency features. Reconstruction-based methods leverage prior knowledge of image characteristics to recover HR images, often through optimization techniques. However, these methods may suffer from slow convergence and high computational cost. Because of their capacity to learn complicated mappings between LR and HR picture spaces, learning-based methods—and deep learning approaches in particular—have been the center of a lot of attention lately. These methods leverage large datasets to train convolutional neural networks (CNNs) for image super-resolution, achieving remarkable performance in terms of visual quality and computational efficiency. Furthermore, we discuss the challenges and future directions in SR research, including the development of more robust and efficient algorithms, handling noisy real-world data, and exploring novel architectures and loss functions to further improve SR performance. The purpose of this review paper is to provide a comprehensive overview of strategies for SR image reconstruction. It focuses on the progression from conventional interpolation methods to cutting-edge deep learning approaches. They hope that this publication will serve as a valuable resource for scholars and practitioners in the field of computer vision and image processing.