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