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conference paper
MAP Estimators for Self-Similar Sparse Stochastic Models
2013
Proceedings of the Tenth International Workshop on Sampling Theory and Applications (SampTA'13)
We consider the reconstruction of multi-dimensional signals from noisy samples. The problem is formulated within the framework of the theory of continuous-domain sparse stochastic processes. In particular, we study the fractional Laplacian as the whitening operator specifying the correlation structure of the model. We then derive a class of MAP estimators where the priors are confined to the family of infinitely divisible distributions. Finally, we provide simulations where the derived estimators are compared against total-variation (TV) denoising.
Type
conference paper
Authors
Publication date
2013
Publisher
Published in
Proceedings of the Tenth International Workshop on Sampling Theory and Applications (SampTA'13)
Issue
Bremen, Federal Republic of Germany
Start page
197
End page
199
Peer reviewed
REVIEWED
EPFL units
Available on Infoscience
September 18, 2015
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