Super Resolution Image

Super-resolution image techniques aim to enhance the resolution of low-resolution images, improving image quality and detail. Current research heavily utilizes deep learning, focusing on Generative Adversarial Networks (GANs), diffusion models, and transformer-based architectures to achieve this, with ongoing efforts to optimize computational efficiency and address artifacts. These advancements have significant implications across diverse fields, including medical imaging, remote sensing, surveillance, and material science, by enabling higher-resolution analysis and improved downstream tasks like object detection and classification. The development of robust, data-efficient methods and accurate no-reference quality assessment metrics remains a key focus.