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Improved recovery of analysis sparse vectors in presence of prior information

Daei, S ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1109/LSP.2018.2886141
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2019
  4. Abstract:
  5. In this letter, we consider the problem of recovering analysis-sparse signals from under-sampled measurements when some prior information about the support is available. We incorporate such information in the recovery stage by suitably tuning the weights in a weighted ℓ1-analysis optimization problem. Indeed, we try to set the weights such that the method succeeds with minimum number of measurements. For this purpose, we exploit the upper-bound on the statistical dimension of a certain cone to determine the weights. Our numerical simulations confirm that the introduced method with tuned weights outperforms the standard ℓ1-analysis technique. © 1994-2012 IEEE
  6. Keywords:
  7. Conic integral geometry ; Prior information ; ℓ1 analysis ; Numerical methods ; Analysis techniques ; Integral geometry ; l1 analysis ; Optimization problems ; Recovery stages ; Sparse signals ; Sparse vectors ; Recovery
  8. Source: IEEE Signal Processing Letters ; Volume 26, Issue 2 , 2019 , Pages 222-226 ; 10709908 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/8571294