Abstract: The present disclosure relates to a computer implemented method for representing a data set comprising at least one n dimensional data element representing visual information, said method comprising obtaining (210) said data set, obtaining (220) a dictionary ensemble comprising a plurality of dictionaries each comprising at least one basis function (102), assigning (230) each at least one data element to a dictionary, wherein a set of basis functions represents an m dimensional transformation domain, transforming (240) the at least one data element with the corresponding dictionary of basis functions to the transformation domain wherein each data element is defined by an associated coefficient set, sparsifying (250) the coefficient sets, forming (260) the representation of the visual information comprising a coefficient data set and the corresponding dictionaries.

Abstract: The present disclosure relates to a computer-implemented method for r configuration of a sensor system, said method (500) comprising providing (510,520), for a training dataset X consisting of at least one data element Xi, for a measurement of the sensor system (200) relating to an initial set-up of the sensor system (200), a dictionary D forming a signal model comprising at least one basis function and at least one set of sparse coefficients S. Each set of sparse coefficients Si and the dictionary D represents one of the data elements Xi of said training dataset X. Determining (530) an optimal sub-sampling operator ? based on the dictionary D and said at least one set of sparse coefficients S, wherein the optimal sub-sampling operator ? is a selection of a true subset of the sample positions of the training dataset X, wherein the true subset corresponds to the sample positions containing the most important samples of the training dataset X.

Abstract: The present disclosure relates to a computer implemented method for representing a data set comprising at least one n dimensional data element representing visual information, said method comprising obtaining (210) said data set, obtaining (220) a dictionary ensemble comprising a plurality of dictionaries each comprising at least one basis function (102), assigning (230) each at least one data element to a dictionary, wherein a set of basis functions represents an m dimensional transformation domain, transforming (240) the at least one data element with the corresponding dictionary of basis functions to the transformation domain wherein each data element is defined by an associated coefficient set, sparsifying (250) the coefficient sets, forming (260) the representation of the visual information comprising a coefficient data set and the corresponding dictionaries.