superrésolution

Super-resolution refers to a class of techniques used to enhance the resolution of an image or video. Essentially, it's about reconstructing a high-resolution image from one or more low-resolution inputs. This is particularly useful in fields where obtaining high-resolution data is expensive or impossible, such as satellite imagery, medical imaging, and surveillance.

There are two main approaches to super-resolution: interpolation-based and reconstruction-based. Interpolation methods are simpler and faster, often involving algorithms that estimate missing pixel values based on neighboring pixels. Common examples include bicubic interpolation. While they can increase image size, they often result in blurred or softened details. Reconstruction-based methods, on the other hand, aim to recover lost high-frequency information by leveraging multiple low-resolution images or by using prior knowledge about the scene.

More recently, deep learning, particularly convolutional neural networks (CNNs), has revolutionized super-resolution. These data-driven approaches learn complex mappings between low-resolution and high-resolution image patches from vast datasets. Deep learning models can achieve significantly sharper results and recover finer details than traditional methods, often producing visually impressive enhancements. However, they can be computationally intensive and require large amounts of training data. The ultimate goal of super-resolution is to produce images that are not only larger but also contain more discernible detail, improving visual quality and enabling more accurate analysis.