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New dictionary learning methods for two-dimensional signals

Shahriari Mehr, F ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.23919/Eusipco47968.2020.9287479
  3. Publisher: European Signal Processing Conference, EUSIPCO , 2021
  4. Abstract:
  5. By growing the size of signals in one-dimensional dictionary learning for sparse representation, memory consumption and complex computations restrict the learning procedure. In applications of sparse representation and dictionary learning in two-dimensional signals (e.g. in image processing), if one opts to convert two-dimensional signals to one-dimensional ones, and use the existing one-dimensional dictionary learning and sparse representation techniques, too huge signals and dictionaries will be encountered. Two-dimensional dictionary learning has been proposed to avoid this problem. In this paper, we propose two algorithms for two-dimensional dictionary learning. According to our simulations, the proposed algorithms have noticeable improvement in both convergence rate and computational load in comparison to one-dimensional methods. © 2021 European Signal Processing Conference, EUSIPCO. All rights reserved
  6. Keywords:
  7. Image processing ; One dimensional ; Complex computation ; Computational loads ; Dictionary learning ; Learning procedures ; Memory consumption ; One dimensional method ; Sparse representation ; Two-dimensional signals ; Learning systems
  8. Source: 28th European Signal Processing Conference, EUSIPCO 2020, 24 August 2020 through 28 August 2020 ; Volume 2021-January , 2021 , Pages 2021-2025 ; 22195491 (ISSN); 9789082797053 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/9287479