Non-parametric Bayesian dictionary learning for sparse<br/><br/>image representations

Tuesday, October 6, 2009 - 3:30pm - 4:00pm
Lind 305
Lawrence Carin (Duke University)
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.
MSC Code: