Math @ Duke
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Publications [#265125] of Guillermo Sapiro
Papers Published
- Paisley, J; Zhou, M; Sapiro, G; Carin, L, Nonparametric image interpolation and dictionary learning using spatially-dependent dirichlet and beta process priors,
Proceedings - International Conference on Image Processing, ICIP
(December, 2010),
pp. 1869-1872, IEEE, ISSN 1522-4880 [doi]
(last updated on 2025/02/02)
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.
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