MMSE Denoising of Sparse and Non-Gaussian AR(1) Processes

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.


Published in:
Proceedings of the Forty-First IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'16), 4333–4337
Presented at:
IEEE International Conference on Acoustics, Speech, and Signal Processing, Shanghai, People's Republic of China, March 20-25, 2016
Year:
2016
Publisher:
IEEE
Keywords:
Laboratories:




 Record created 2017-02-02, last modified 2018-03-17

External links:
Download fulltextURL
Download fulltextURL
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)