Deep Learning for Image Super-Resolution: Techniques and Applications

Abstract

Image super-resolution (SR) refers to the process of enhancing the resolution of an image, thereby improving its quality and visual fidelity. With the advent of deep learning, SR techniques have significantly advanced, offering more powerful and accurate models than traditional interpolation methods. This paper explores the various deep learning techniques used for image super-resolution, including convolutional neural networks (CNNs), generative adversarial networks (GANs), and reinforcement learning. The applications of these models span across multiple domains, such as medical imaging, satellite imagery, and video enhancement. Furthermore, this article highlights challenges in implementing these models, such as computational cost and the trade-off between speed and quality. The paper concludes with an exploration of future directions and open challenges in the field of image SR..