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

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4 Author(s)
Paisley, J. ; Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA ; Mingyuan Zhou ; Sapiro, G. ; Carin, L.

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.

Published in:

Image Processing (ICIP), 2010 17th IEEE International Conference on

Date of Conference:

26-29 Sept. 2010

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