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We propose a new regularization method for sparse representation and denoising of seismic data. Our approach is based on a sparse data representation in a learned dictionary and a similarity measure for image patches that is evaluated using the Laplacian matrix of a graph. We propose dictionary learning algorithms based on clustering and singular value decomposition. We also consider a similarity measure for the local geometric structures of the data.
This presentation is part of Minisymposium “MS2 - Interpolation and Approximation Methods in Imaging (4 parts)”
organized by: Alessandra De Rossi (University of Torino) , Costanza Conti (University of Firenze) , Francesco Dell'Accio (University of Calabria) .