TGV-based restoration of Poissonian images with automatic estimation of the regularization parameter

The problem of restoring images corrupted by Poisson noise is common in many application fields and, because of its intrinsic ill posedness, it requires regularization techniques for its solution. The effectiveness of such techniques depends on the value of the regularization parameter balancing dat...

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Bibliographic Details
Published in2021 21st International Conference on Computational Science and Its Applications (ICCSA) pp. 139 - 145
Main Authors Di Serafino, Daniela, Landi, Germana, Viola, Marco
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2021
Subjects
automatic regularization parameter estimation
Data models
Estimation
Image restoration
Numerical models
Parameter estimation
Phantoms
Poisson noise
Scientific computing
TGV regularization
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Summary:The problem of restoring images corrupted by Poisson noise is common in many application fields and, because of its intrinsic ill posedness, it requires regularization techniques for its solution. The effectiveness of such techniques depends on the value of the regularization parameter balancing data fidelity and regularity of the solution. Here we consider the Total Generalized Variation regularization introduced in [SIAM J. Imag. Sci, 3(3), 492-526, 2010], which has demonstrated its ability of preserving sharp features as well as smooth transition variations, and introduce an automatic strategy for defining the value of the regularization parameter. We solve the corresponding optimization problem by using a 3-block version of ADMM. Preliminary numerical experiments support the proposed approach.
DOI:10.1109/ICCSA54496.2021.00028