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Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
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BibTeX
@MISC{Zhou_non-parametricbayesian,
author = {Mingyuan Zhou and Haojun Chen and John Paisley and Lu Ren and Guillermo Sapiro and Lawrence Carin},
title = {Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations },
year = {}
}
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Abstract
Non-parametric Bayesian techniques are considered for learning dictionaries for sparse image representations, with applications in denoising, inpainting and compressive sensing (CS). The beta process is employed as a prior for learning the dictionary, and this non-parametric method naturally infers an appropriate dictionary size. The Dirichlet process and a probit stick-breaking process are also considered to exploit structure within an image. The proposed method can learn a sparse dictionary in situ; training images may be exploited if available, but they are not required. Further, the noise variance need not be known, and can be nonstationary. Another virtue of the proposed method is that sequential inference can be readily employed, thereby allowing scaling to large images. Several example results are presented, using both Gibbs and variational Bayesian inference, with comparisons to other state-of-the-art approaches.
Keyphrases
sparse image representation non-parametric bayesian dictionary learning probit stick-breaking process non-parametric bayesian technique variational bayesian inference sequential inference noise variance dirichlet process sparse dictionary state-of-the-art approach appropriate dictionary size several example result beta process compressive sensing training image large image non-parametric method
