Publication

MAP Estimators for Self-Similar Sparse Stochastic Models

Michaël Unser, Julien René Fageot
2013
Conference Papers

Abstract

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.

Official source

https://infoscience.epfl.ch/entities/publication/218483a8-fad5-48b9-bbb5-df1dcdabd99e

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MAP Estimators for Self-Similar Sparse Stochastic Models

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