Nonparametric image interpolation and dictionary learning using spatially-dependent dirichlet and beta process priors

John Paisley, Mingyuan Zhou, Guillermo Sapiro, Lawrence Carin

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

6 Scopus citations

Abstract

We present a Bayesian model for image interpolation and dictionary learning that uses two nonparametric priors for sparse signal representations: the beta process and the Dirichlet process. Additionally, the model uses spatial information within the image to encourage sharing of information within image subregions. We derive a hybrid MAP/Gibbs sampler, which performs Gibbs sampling for the latent indicator variables and MAP estimation for all other parameters. We present experimental results, where we show an improvement over other state-of-the-art algorithms in the low-measurement regime. © 2010 IEEE.
Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Image Processing, ICIP
Pages1869-1872
Number of pages4
DOIs
StatePublished - Dec 1 2010
Externally publishedYes

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