Journal
INVERSE PROBLEMS AND IMAGING
Volume 12, Issue 3, Pages 607-634Publisher
AMER INST MATHEMATICAL SCIENCES-AIMS
DOI: 10.3934/ipi.2018026
Keywords
Inverse problems; Kullback-Leibler divergence; Morozov principle
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Funding
- LABEX PRIMES of Universite de Lyon, within the program 'Investissements d'Avenir [ANR-11-LABX-0063, ANR-11-IDEX-0007]
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We study the properties of a regularization method for inverse problems corrupted by Poisson noise with Kullback-Leibler divergence as data term. The regularization parameter is chosen according to a Morozov type principle. We show that this method of choice of the parameter is well-defined. This a posteriori choice leads to a convergent regularization method. Convergences rates are obtained for this a posteriori choice of the regularization parameter when some source condition is satisfied.
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