Abstract Hyper-Resolution, a new technique for super-resolution reconstruction of images, is based on matching low-resolution target image details to their high-resolution counterparts from an image database. Central to the algorithm is a novel transform of image content from the orthogonal pixel space to a parametric space structured around edges. This approach offers improved quality, more flexibility and significantly faster performance than previous work in the field. Implementation strategies for achieving this efficiency are carefully outlined. The algorithm is evaluated by controlled assessment, qualitative evaluation, and applications to facial detail reconstruction and identification. The algorithm is finally analyzed through the comparison with alternative techniques.