Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy
<|reference_start|>Algorithmic unfolding for image reconstruction and localization problems in fluorescence microscopy: We propose an unfolded accelerated projected-gradient descent procedure to estimate model and algorithmic parameters for image super-resolution and molecule localization problems in image microscopy. The variational lower-level constraint enforces sparsity of the solution and encodes different noise statistics (Gaussian, Poisson), while the upper-level cost assesses optimality w.r.t.~the task considered. In more detail, a standard $\ell_2$ cost is considered for image reconstruction (e.g., deconvolution/super-resolution, semi-blind deconvolution) problems, while a smoothed $\ell_1$ is employed to assess localization precision in some exemplary fluorescence microscopy problems exploiting single-molecule activation. Several numerical experiments are reported to validate the proposed approach on synthetic and realistic ISBI data.<|reference_end|>
@article{bonettini2024algorithmic,
    title={Algorithmic unfolding for image reconstruction and localization problems
  in fluorescence microscopy},
    author={Silvia Bonettini, Luca Calatroni, Danilo Pezzi, Marco Prato},
    journal={arXiv preprint arXiv:2403.17506},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17506},
    primaryClass={math.NA cs.NA}
}