IEEE Trans Image Process - Nonparametric Bayesian dictionary learning for analysis of noisy and incomplete images.

Tópicos

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{ imag(1947) propos(1133) code(1026) }
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{ medic(1828) order(1363) alert(1069) }
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Resumo

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.

Resumo Limpo

nonparametr bayesian method consid recoveri imageri base upon compress incomplet andor noisi measur truncat betabernoulli process employ infer appropri dictionari data test also imag recoveri context compress sens signific improv imag recoveri manifest use learn dictionari relat use standard orthonorm imag expans compressivemeasur project also optim learn dictionari addit consid simpler incomplet measur defin measur subset imag pixel uniform select random spatial interrelationship within imageri exploit use dirichlet probit stickbreak process sever exampl result present comparison method literatur

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