A Plurality of Sparse Representations Is Better Than the Sparsest One Alone

  title={A Plurality of Sparse Representations Is Better Than the Sparsest One Alone},
  author={Michael Elad and Irad Yavneh},
  journal={IEEE Transactions on Information Theory},
Cleaning of noise from signals is a classical and long-studied problem in signal processing. Algorithms for this task necessarily rely on an a priori knowledge about the signal characteristics, along with information about the noise properties. For signals that admit sparse representations over a known dictionary, a commonly used denoising technique is to seek the sparsest representation that synthesizes a signal close enough to the corrupted one. As this problem is too complex in general… CONTINUE READING
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