Super Resolution

Super-resolution (SR) aims to enhance the resolution of images or other data, improving detail and clarity from lower-resolution inputs. Current research focuses on developing efficient and effective SR models, employing various architectures such as convolutional neural networks, transformers, and diffusion models, often incorporating techniques like self-supervised learning and multi-scale processing to improve performance and reduce computational cost. These advancements have significant implications across diverse fields, including medical imaging (improving diagnostic accuracy), remote sensing (enhancing spatial detail), and computer vision (improving the quality of generated images and videos). The development of robust and efficient SR methods is crucial for numerous applications where high-resolution data is desirable but acquisition is costly or impractical.