A Hyperprior Bayesian Approach for Solving Image Inverse Problems

Abstract : 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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Contributor : Cecilia Aguerrebere <>
Submitted on : Tuesday, January 20, 2015 - 7:31:54 PM
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  • HAL Id : hal-01107519, version 1


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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