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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

A METROPOLIS-HASTINGS METHOD FOR LINEAR INVERSE PROBLEMS WITH POISSON LIKELIHOOD AND GAUSSIAN PRIOR

Volume 6, Numéro 1, 2016, pp. 35-55
DOI: 10.1615/Int.J.UncertaintyQuantification.2016013678
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RÉSUMÉ

Poisson noise models arise in a wide range of linear inverse problems in imaging. In the Bayesian setting, the Poisson likelihood function together with a Gaussian prior yields a posterior density that is not of a well-known form and is thus difficult to sample from, especially for large-scale problems. In this work, we present a method for computing samples from posterior density functions with Poisson likelihood and Gaussian prior, using a Gaussian approximation of the posterior as an independence proposal within a Metropolis−Hastings framework. We consider a class of Gaussian priors, some of which are edge-preserving, and which we motivate using Markov random fields. We present two sampling algorithms: one which samples the unknown image alone, leaving the prior scaling (or regularization) parameter alone, and another which samples both the unknown image and the prior scaling parameter. For this paper, we make the assumption that our unknown image is sufficiently positive that proposed samples are always positive, allowing us to ignore the nonnegativity constraint. Results are demonstrated on synthetic data−including a synthetic X-ray radiograph generated from a radiation transport code−and on real images used to calibrate a pulsed power high-energy X-ray source at the U.S. Department of Energy's Nevada National Security Site.

CITÉ PAR
  1. Joyce Kevin T., Bardsley Johnathan M., Luttman Aaron, Point Spread Function Estimation in X-Ray Imaging with Partially Collapsed Gibbs Sampling, SIAM Journal on Scientific Computing, 40, 3, 2018. Crossref

  2. Brown D. Andrew, Saibaba Arvind, Vallélian Sarah, Low-Rank Independence Samplers in Hierarchical Bayesian Inverse Problems, SIAM/ASA Journal on Uncertainty Quantification, 6, 3, 2018. Crossref

  3. Arridge Simon R, Ito Kazufumi, Jin Bangti, Zhang Chen, Variational Gaussian approximation for Poisson data, Inverse Problems, 34, 2, 2018. Crossref

  4. Vono Maxime, Dobigeon Nicolas, Chainais Pierre, Bayesian Image Restoration under Poisson Noise and Log-concave Prior, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019. Crossref

  5. Zhou Qingping, Yu Tengchao, Zhang Xiaoqun, Li Jinglai, Bayesian Inference and Uncertainty Quantification for Medical Image Reconstruction with Poisson Data, SIAM Journal on Imaging Sciences, 13, 1, 2020. Crossref

  6. Adams Jesse, Morzfeld Matthias, Joyce Kevin, Howard Marylesa, Luttman Aaron, A blocking scheme for dimension-robust Gibbs sampling in large-scale image deblurring, Inverse Problems in Science and Engineering, 29, 12, 2021. Crossref

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