Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images.

Journal Article

Nonparametric Bayesian methods are considered for recovery of imagery based upon compressive, incomplete, and/or noisy measurements. A truncated beta-Bernoulli process is employed to infer an appropriate dictionary for the data under test and also for image recovery. In the context of compressive sensing, significant improvements in image recovery are manifested using learned dictionaries, relative to using standard orthonormal image expansions. The compressive-measurement projections are also optimized for the learned dictionary. Additionally, we consider simpler (incomplete) measurements, defined by measuring a subset of image pixels, uniformly selected at random. Spatial interrelationships within imagery are exploited through use of the Dirichlet and probit stick-breaking processes. Several example results are presented, with comparisons to other methods in the literature.

Full Text

Duke Authors

Cited Authors

  • Zhou, M; Chen, H; Paisley, J; Ren, L; Li, L; Xing, Z; Dunson, D; Sapiro, G; Carin, L

Published Date

  • January 2012

Published In

Volume / Issue

  • 21 / 1

Start / End Page

  • 130 - 144

PubMed ID

  • 21693421

Electronic International Standard Serial Number (EISSN)

  • 1941-0042

Digital Object Identifier (DOI)

  • 10.1109/TIP.2011.2160072


  • eng

Conference Location

  • United States