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Models and algorithms for phase estimation in differential interference contrast microscopy

Abstract : In this paper we address the problem of estimating the phase from color images acquired with differential-interference-contrast microscopy. In particular, we consider the nonlinear and nonconvex optimization problem obtained by regularizing a least-squares-like discrepancy term with a total variation functional, possibly smoothed with the introduction of a positive constant. We deeply investigate the analytical properties of the resulting objective function, proving the existence of minimum points, and several optimization methods able to address the minimization problem. Besides revisiting the conjugate gradient method proposed in the literature for this problem and comparing it with standard conjugate gradient approaches, we introduce more recent effective optimization tools able to obtain both in the smooth and in the nonsmooth case accurate reconstructions with a reduced computational demand.
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Contributor : Lola Bautista Connect in order to contact the contributor
Submitted on : Wednesday, January 4, 2017 - 12:01:26 PM
Last modification on : Tuesday, December 7, 2021 - 4:05:10 PM
Long-term archiving on: : Wednesday, April 5, 2017 - 1:48:44 PM


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  • HAL Id : hal-01426174, version 1



Simone Rebegoldi, Lola Bautista, Laure Blanc-Féraud, Marco Prato, Luca Zanni, et al.. Models and algorithms for phase estimation in differential interference contrast microscopy. 2017. ⟨hal-01426174⟩



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