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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...
Nonparametric Bayesian techniques are considered for learning dictionaries for sparse data representations, with applications in sparse rendering of sensor data. The beta process is employed as a prior for learning the dictionary, and this nonparametric method naturally infers an appropriate dictionary size. The proposed method can learn a sparse dictionary, and may also be used to denoise a signal...
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