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PARAMETER ESTIMATION IN SPARSE INVERSE PROBLEMS USING BERNOULLI-GAUSSIAN PRIOR

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1
Pierre Barbault
Matthieu Kowalski
Charles Soussen

Abstract

Sparse coding is now one of the state-of-art approaches for solving inverse problems. In combination with (Fast) Iterative Shrinkage Thresholding Algorithm (ISTA), among other algorithms, one can efficiently get a nice estimator of the sought sparse signal. However, the major drawback of these methods is the tuning of the so-called hyperparameter. In this paper, we first provide an Expectation-Maximization (EM) algorithm to estimate the parameters of a Bernoulli-Gaussian model for denoising a sparse signal corrupted by a white Gaussian noise. Then, building on the Expectation-Maximization interpretation of ISTA, we provide a simple iterative algorithm to blindly estimate all the model parameters in the linear inverse problem context, including the hyperparameter involved in the popular 0 regularized minimization. Moreover, the algorithm directly yields an estimator of the sparse signal.
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Dates and versions

hal-03576005 , version 1 (15-02-2022)

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  • HAL Id : hal-03576005 , version 1

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Pierre Barbault, Matthieu Kowalski, Charles Soussen. PARAMETER ESTIMATION IN SPARSE INVERSE PROBLEMS USING BERNOULLI-GAUSSIAN PRIOR. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing( ICASSP 2022 ), May 2022, Singapore, Singapore. ⟨hal-03576005⟩
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