Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images.
Journal Article (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
Pubmed Central ID
- PMC3601051
Electronic International Standard Serial Number (EISSN)
- 1941-0042
International Standard Serial Number (ISSN)
- 1057-7149
Digital Object Identifier (DOI)
- 10.1109/tip.2011.2160072
Language
- eng