Rethinking data-driven point spread function modeling with a differentiable optical model

DOI10.1088/1361-6420/acb664OpenAlexW4318189481MaRDI QIDQ5877572

Tobias Liaudat, Pierre-Antoine Frugier, Martin Kilbinger, Jean-Luc Starck

Publication date: 14 February 2023

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2203.04908

zbMATH Keywords

automatic differentiationmatrix factorizationsuper-resolutionchromatic variationspoint spread function modeling

Mathematics Subject Classification ID

Computational learning theory (68Q32) Trees (05C05) Stochastic programming (90C15) Smoothness and regularity of solutions to PDEs (35B65) Inverse problems in geophysics (86A22) Diffraction, scattering (78A45) Meteorology and atmospheric physics (86A10) Numerical differentiation (65D25) Waves and radiation in optics and electromagnetic theory (78A40) Numerical methods for inverse problems for initial value and initial-boundary value problems involving PDEs (65M32) Inverse problems (including inverse scattering) in optics and electromagnetic theory (78A46) Optimization problems in optics and electromagnetic theory (78M50) Impulsive optimal control problems (49N25) Numerical radial basis function approximation (65D12) Mathematical modeling or simulation for problems pertaining to optics and electromagnetic theory (78-10)

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Uses Software

• Jupyter
• SExtractor

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