EPFL | Biomedical Imaging Group | Bayesian Estimation

Bayesian Estimation for Continuous-Time Sparse Stochastic Processes

A. Amini, U.S. Kamilov, E. Bostan, M. Unser

IEEE Transactions on Signal Processing, vol. 61, no. 4, pp. 907-920, February 15, 2013.

We consider continuous-time sparse stochastic processes from which we have only a finite number of noisy/noiseless samples. Our goal is to estimate the noiseless samples (denoising) and the signal in-between (interpolation problem). By relying on tools from the theory of splines, we derive the joint a priori distribution of the samples and show how this probability density function can be factorized. The factorization enables us to tractably implement the maximum a posteriori and minimum mean-square error (MMSE) criteria as two statistical approaches for estimating the unknowns. We compare the derived statistical methods with well-known techniques for the recovery of sparse signals, such as the ℓ₁ norm and Log (ℓ₁-ℓ₀ relaxation) regularization methods. The simulation results show that, under certain conditions, the performance of the regularization techniques can be very close to that of the MMSE estimator.

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JOURNAL="{IEEE} Transactions on Signal Processing",
YEAR="2013",
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