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 language||English (US)|
|Title of host publication||Proceedings - International Conference on Image Processing, ICIP|
|Number of pages||4|
|State||Published - Dec 1 2010|