Convergence Guarantees for Unmixing PSFs over a Manifold with Non-Convex Optimization

Santos Michelena, Maxime Ferreira Da Costa, and José Picheral

IEEE Statistical Signal Processing Workshop (SSP 2025).

Abstract

The problem of recovering the parameters of a mixture of spike signals convolved with different PSFs is considered. Herein, the spike support is assumed to be known, while the PSFs lie on a manifold. A non-linear least squares estimator of the mixture parameters is formulated. In the absence of noise, a lower bound on the radius of the strong basin of attraction i.e., the region of convergence, is derived. Key to the analysis is the introduction of coherence and interference functions, which capture the conditioning of the PSF manifold in terms of the minimal separation of the support. Numerical experiments validate the theoretical findings. Finally, the practicality and efficacy of the non-linear least squares approach are showcased on real-world experimental spectral data from laser-induced breakdown spectroscopy.

Cite

@inproceedings{michelena2025ConvergenceGuaranteesa,
  author    = {Michelena, Santos and Ferreira Da Costa, Maxime and Picheral, José},
  title     = {Convergence Guarantees for Unmixing {{PSFs}} over a Manifold with Non-Convex Optimization},
  booktitle = {2025 {{IEEE Statistical Signal Processing Workshop}} ({{SSP}})},
  pages     = {161--165},
  year      = {2025},
  doi       = {10.1109/SSP64130.2025.11073360}
}