Superauflösungsverfahren

Superauflösungsverfahren, also known as super-resolution techniques, are a class of methods used in digital image processing to enhance the resolution of an image beyond its original captured detail. Essentially, these techniques aim to reconstruct a high-resolution (HR) image from one or more low-resolution (LR) images. This is achieved by exploiting the fact that the LR images, even if they appear blurry or less detailed, contain some underlying information that can be leveraged to infer finer details.

There are broadly two main categories of super-resolution techniques: single-image super-resolution (SISR) and multi-image super-resolution (MISR). SISR methods attempt to increase resolution using only a single LR image. They often rely on learning relationships between LR and HR image patches from a large dataset of training images. Algorithms might learn statistical priors about natural images or utilize deep learning models, particularly convolutional neural networks (CNNs), trained to predict HR details from LR inputs.

MISR, on the other hand, utilizes information from multiple LR images of the same scene, often captured with slight variations in viewpoint or sensor positioning. By aligning these LR images and combining their complementary information, MISR can achieve a higher resolution than is possible with any single LR image. This approach can lead to more accurate and visually pleasing results, as it avoids the potential artifacts that can arise from inferring details solely from a single, limited source. Applications of super-resolution are widespread, including in fields like medical imaging, surveillance, satellite imagery, and enhancing old photographs.