Efficient and Parallel Separable Dictionary Learning

7 Jul 2020  ·  Cristian Rusu, Paul Irofti ·

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost... We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising. read more

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