1. A method for performing super-resolution processing of a low resolution input data structure of digital 1D, 2D or 3D data, comprising steps of detecting one or more regions in the low resolution input data structure where aliasing is likely to occur, and generating a de-noising mask that indicates the detected one or more regions in the low resolution input data structure; splitting the input data structure into a low-frequency input data structure and a high-frequency input data structure complementary to the low-frequency input data structure, wherein a first low-pass filter is used; upscaling the input data structure and filtering the upscaled input data structure by a second low-pass filter, wherein a low-frequency upscaled data structure is obtained; locally de-noising one or more regions in the low-frequency upscaled data structure according to the de-noising mask, wherein a locally de-noised low-frequency upscaled data structure is obtained; generating a normalized high-frequency upscaled data Structure by extrapolating the high-frequency input data structure, wherein correspondences between the locally de-noised low-frequency upscaled data structure and the low-frequency input data structure are used, and wherein cross-scale block matching and normalization are performed; locally de-noising the normalized high-frequency upscaled data structure in a local regularization step, whereby the local de-noising is performed in said one or more regions according to the de-noising mask, and whereby a locally de-noised normalized high-frequency upscaled data structure is obtained; high-pass filtering the normalized high-frequency upscaled data structure, whereby a high-pass filtered normalized high-frequency upscaled data structure is obtained; and adding the high-pass filtered normalized high-frequency upscaled data structure to the low-frequency upscaled data structure, whereby a super-resolved data structure is obtained.