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MMSE denoising of sparse and non-gaussian AR(1) processes

Tohidi, P ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1109/ICASSP.2016.7472495
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2016
  4. Abstract:
  5. We propose two minimum-mean-square-error (MMSE) estimation methods for denoising non-Gaussian first-order autoregressive (AR(1)) processes. The first one is based on the message passing framework and gives the exact theoretic MMSE estimator. The second is an iterative algorithm that combines standard wavelet-based thresholding with an optimized non-linearity and cycle-spinning. This method is more computationally efficient than the former and appears to provide the same optimal denoising results in practice. We illustrate the superior performance of both methods through numerical simulations by comparing them with other well-known denoising schemes
  6. Keywords:
  7. Auto-regressive ; Consistent cycle spinning ; De-noising ; Message passing ; Minimum mean square error ; Non-gaussian ; Operator-like wavelets
  8. Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 20 March 2016 through 25 March 2016 ; Volume 2016-May , 2016 , Pages 4333-4337 ; 15206149 (ISSN) ; 9781479999880 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/7472495