1. A method for performing super-resolution on an input image having low resolution, comprising steps of generating a training data set of descriptors, or retrieving from a storage a previously generated training data set of descriptors, the descriptors being extracted from regions of training images, the training images comprising low-resolution and corresponding high-resolution images, wherein each descriptor comprises a region identifier and a geometrical feature; dividing the input image into a plurality of input image patches, wherein the patches are smaller than the regions; for each input image patch, performing the steps of determining a defined number of nearest neighbor regions, the nearest neighbor regions being low-resolution regions from the training data set that have geometrical features that are most similar to a current input image patch; from each nearest neighbor region, extracting a plurality of example patches by dense sampling, wherein the dense sampling comprises sampling in regular intervals of r pixels and is independent from the image contents within the region, r being an integer, and collecting the example patches in an example patch data base that is specific for the current input image; determining from the example patch data base a low-resolution example patch, or a combination of two or more low-resolution example patches, that optimally match geometrical features of the current input image patch; and constructing a target high-resolution patch from one or more high-resolution patches corresponding to said one or more low-resolution example patches, according to the determined combination, wherein the target high-resolution patches for all input patches form a super-resolved image.