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Pré-Publication, Document De Travail Année : 2014

A Hyperprior Bayesian Approach for Solving Image Inverse Problems

Résumé

Patch models have proven successful to solve a variety of inverse problems in image restoration. Recent methods, combining patch models with a Bayesian approach, achieve state-of-the-art results in several restoration problems. Dif-ferent strategies are followed to determine the patch mod-els, such as a fixed number of models to describe all im-age patches or a locally determined model for each patch. Local model estimation has proven very powerful for im-age denoising, but it becomes seriously ill-posed for other inverse problems such as interpolation of random missing pixels or zooming. In this work, we present a new frame-work for image restoration that combines these two power-ful approaches: Bayesian restoration and a local charac-terization of image patches. By making use of a prior on the model parameters, we overcome the ill-posedness of the local estimation and obtain state-of-the-art results in prob-lems such as interpolation, denoising and zooming. Exper-iments conducted on synthetic and real data show the effec-tiveness of the proposed approach.
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Dates et versions

hal-01107519 , version 1 (20-01-2015)
hal-01107519 , version 2 (17-01-2016)
hal-01107519 , version 3 (04-03-2016)
hal-01107519 , version 4 (01-12-2016)
hal-01107519 , version 5 (15-05-2017)

Identifiants

  • HAL Id : hal-01107519 , version 1

Citer

Cecilia Aguerrebere, Andrés Almansa, Yann Gousseau, Julie Delon, Pablo Musé. A Hyperprior Bayesian Approach for Solving Image Inverse Problems. 2014. ⟨hal-01107519v1⟩
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