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Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
Mingyuan Zhou · Haojun Chen · John Paisley · Lu Ren · Guillermo Sapiro · Lawrence Carin

Tue Dec 08 07:00 PM -- 11:59 PM (PST) @ None #None

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 non-stationary. 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.

Author Information

Mingyuan Zhou (University of Texas at Austin)
Haojun Chen (Duke University)
John Paisley
Lu Ren (Duke University)
Guillermo Sapiro (Duke University)
Lawrence Carin (Duke University)

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