Unrolled expectation maximization algorithm for radio interferometric imaging in presence of non gaussian interferences

Abstract

<div><p>This paper proposes an unrolled Expectation Maximization (EM) algorithm tailored for robust radio interferometric imaging in presence of non gaussian radio interferences. We introduce a compound Gaussian model for the observation noise and derive an unrolled neural architecture based on the EM algorithm to tackle the reconstruction problem in a robust manner. This innovative approach aims to enhance image reconstruction by simultaneously incorporating model information and generalization for the case of non-gaussian heavy-tailed noise distribution, while leveraging the benefits of deep learning. Our experiments demonstrate significant improvements over state-of-the-art methods, highlighting the efficacy of our proposed scheme in handling the complexities of radiofrequency interference and improving image reconstruction accuracy.</p></div>

BibTeX

@article{arab202504966761,
  title       = {Unrolled expectation maximization algorithm for radio interferometric imaging in presence of non gaussian interferences},
  author      = {Nawel Arab and Yassine Mhiri and Isabelle Vin and Mohammed Nabil El Korso and Pascal Larzabal},
  journal     = {Signal Processing},
  year        = {2025},
  doi         = {10.1016/j.sigpro.2025.110035},
  url         = {https://hal.science/hal-04966761v2},
}