Efficient and Parallel Separable Dictionary Learning
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Publication:6344638
arXiv2007.03800MaRDI QIDQ6344638
Author name not available (Why is that?)
Publication date: 7 July 2020
Abstract: 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.
Has companion code repository: https://github.com/pirofti/ParallelSeparableDL
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