Super-resolution is a technique used in image processing to enhance the resolution of images beyond their original quality. By analyzing and combining multiple low-resolution images, algorithms can reconstruct a higher-resolution version, revealing more detail and clarity. This process is particularly useful in fields like medical imaging, satellite imagery, and digital photography. The technology behind super-resolution often involves advanced methods such as machine learning and deep learning. These methods allow computers to learn patterns in images and predict what higher-resolution details should look like. As a result, super-resolution can significantly improve the quality of images in various applications.