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

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.

Duke Scholars

Author Guillermo Sapiro Electrical and Computer Engineering

Author Lawrence Carin Electrical and Computer Engineering