Title: Nonparametric image interpolation and dictionary learning using spatially-dependent Dirichlet and beta process priors
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.
Publication Year: 2010
Publication Date: 2010-09-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 6
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot