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...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
26.03.2024
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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DOI: | 10.48550/arxiv.2403.17506 |