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

Publication ,  Journal Article
Zhou, M; Chen, H; Paisley, J; Ren, L; Li, L; Xing, Z; Dunson, D; Sapiro, G; Carin, L
Published in: IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
January 2012

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.

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Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

January 2012

Volume

21

Issue

1

Start / End Page

130 / 144

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Data Interpretation, Statistical
  • Bayes Theorem
  • Artificial Intelligence & Image Processing
  • Artifacts
  • Algorithms
 

Citation

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Zhou, M., Chen, H., Paisley, J., Ren, L., Li, L., Xing, Z., … Carin, L. (2012). Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images. IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, 21(1), 130–144. https://doi.org/10.1109/tip.2011.2160072
Zhou, Mingyuan, Haojun Chen, John Paisley, Lu Ren, Lingbo Li, Zhengming Xing, David Dunson, Guillermo Sapiro, and Lawrence Carin. “Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society 21, no. 1 (January 2012): 130–44. https://doi.org/10.1109/tip.2011.2160072.
Zhou M, Chen H, Paisley J, Ren L, Li L, Xing Z, et al. Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2012 Jan;21(1):130–44.
Zhou, Mingyuan, et al. “Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images.IEEE Transactions on Image Processing : A Publication of the IEEE Signal Processing Society, vol. 21, no. 1, Jan. 2012, pp. 130–44. Epmc, doi:10.1109/tip.2011.2160072.
Zhou M, Chen H, Paisley J, Ren L, Li L, Xing Z, Dunson D, Sapiro G, Carin L. Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2012 Jan;21(1):130–144.

Published In

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society

DOI

EISSN

1941-0042

ISSN

1057-7149

Publication Date

January 2012

Volume

21

Issue

1

Start / End Page

130 / 144

Related Subject Headings

  • Sensitivity and Specificity
  • Reproducibility of Results
  • Pattern Recognition, Automated
  • Image Interpretation, Computer-Assisted
  • Image Enhancement
  • Data Interpretation, Statistical
  • Bayes Theorem
  • Artificial Intelligence & Image Processing
  • Artifacts
  • Algorithms