Nonparametric bayesian dictionary learning for analysis of noisy and incomplete images

Mingyuan Zhou, Haojun Chen, John Paisley, Lu Ren, Lingbo Li, Zhengming Xing, David Dunson, Guillermo Sapiro, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

273 Scopus citations

Abstract

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. © 2011 IEEE.
Original languageEnglish (US)
Pages (from-to)130-144
Number of pages15
JournalIEEE Transactions on Image Processing
Volume21
Issue number1
DOIs
StatePublished - Jan 1 2012
Externally publishedYes

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