Linked Life Data

17952185

Source:http://linkedlifedata.com/resource/pubmed/id/17952185

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
30
pubmed:dateCreated
2007-10-22
pubmed:abstractText
We extend and refine previous work [Appl. Opt. 46, 2907 (2007)]. Combining the coupled nonlinear partial differential equations (PDEs) denoising model with the ordinary differential equations enhancement method, we propose the new denoising and enhancing model for electronic speckle pattern interferometry (ESPI) fringe patterns. Meanwhile, we propose the backpropagation neural networks (BPNN) method to obtain unwrapped phase values based on a skeleton map instead of traditional interpolations. We test the introduced methods on the computer-simulated speckle ESPI fringe patterns and experimentally obtained fringe pattern, respectively. The experimental results show that the coupled nonlinear PDEs denoising model is capable of effectively removing noise, and the unwrapped phase values obtained by the BPNN method are much more accurate than those obtained by the well-known traditional interpolation. In addition, the accuracy of the BPNN method is adjustable by changing the parameters of networks such as the number of neurons.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
0003-6935
pubmed:author
pubmed:issnType
Print
pubmed:day
20
pubmed:volume
46
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
7475-84
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Denoising by coupled partial differential equations and extracting phase by backpropagation neural networks for electronic speckle pattern interferometry.
pubmed:affiliation
Department of Applied Physics, University of Tianjin, China. tangchen@tju.edu.cn
pubmed:publicationType
Journal Article