Maximum likelihood estimation of regularisation parameters

Ana Fernandez Vidal, Marcelo Pereyra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
180 Downloads (Pure)


This paper presents an empirical Bayesian method to estimate regularisation parameters in imaging inverse problems. The method calibrates regularisation parameters directly from the observed data by maximum marginal likelihood estimation, and is useful for inverse problems that are convex. A main novelty is that maximum likelihood estimation is performed efficiently by using a stochastic proximal gradient algorithm that is driven by two proximal Markov chain Monte Carlo samplers, intimately combining modern optimisation and sampling techniques. The proposed methodology is illustrated with an application to total-variation image deconvolution, where it compares favourably to alternative Bayesian and non-Bayesian approaches from the state of the art.
Original languageEnglish
Title of host publication2018 25th IEEE International Conference on Image Processing (ICIP)
Number of pages5
ISBN (Electronic)9781479970612
Publication statusPublished - 6 Sept 2018
Event25th IEEE International Conference on Image Processing 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

NameIEEE International Conference on Image Processing (ICIP)
ISSN (Electronic)2381-8549


Conference25th IEEE International Conference on Image Processing 2018
Abbreviated titleIEEE ICIP 2018
Internet address


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