  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.
