Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...

Dupé, François-Xavier Fadili, Jalal M. Starck, Jean-Luc

In this paper, we propose two algorithms to solve a large class of linear inverse problems when the observations are corrupted by various types of noises. A proper data fidelity term (log-likelihood) is introduced to reflect the statistics of the noise (e.g. Gaussian, Poisson, multiplicative, etc.) independently of the degradation operator. On the ...