Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework

Cheon Yang Lee; Jae Ho Han

doi:10.1109/IWW-BCI.2013.6506639

Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework

Cheon Yang Lee, Jae Ho Han

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

Abstract

We were able to efficiently remove the morphological artifact of the fiber bundle based endo-microscopy and improve the featured patterns within the object image acquired by using non-invasive near infrared optical coherence tomography. Our image reconstruction methodology starts to estimate the original shape from the regions that are directly damaged from the en face image which contains significant image degradation by the pixelation of numerous imaging fiber units. Then we have iteratively extended the neighbor areas from the initial status so that we can successfully estimate the original shape of the missing pattern.

Original language	English
Title of host publication	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Pages	84-85
Number of pages	2
DOIs	https://doi.org/10.1109/IWW-BCI.2013.6506639
Publication status	Published - 2013
Event	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of Duration: 2013 Feb 18 → 2013 Feb 20

Publication series

Name	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013

Other

Other	2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Country	Korea, Republic of
City	Gangwon Province
Period	13/2/18 → 13/2/20

Keywords

Endoscopy
Image restoration
Optical Coherence Tomography
Optical fibers
Pattern recognition

ASJC Scopus subject areas

Human-Computer Interaction

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10.1109/IWW-BCI.2013.6506639

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Lee, C. Y., & Han, J. H. (2013). Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework. In 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 (pp. 84-85). [6506639] (2013 International Winter Workshop on Brain-Computer Interface, BCI 2013). https://doi.org/10.1109/IWW-BCI.2013.6506639

Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework. / Lee, Cheon Yang; Han, Jae Ho.

2013 International Winter Workshop on Brain-Computer Interface, BCI 2013. 2013. p. 84-85 6506639 (2013 International Winter Workshop on Brain-Computer Interface, BCI 2013).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lee, CY & Han, JH 2013, Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework. in 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013., 6506639, 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013, pp. 84-85, 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013, Gangwon Province, Korea, Republic of, 13/2/18. https://doi.org/10.1109/IWW-BCI.2013.6506639

Lee CY, Han JH. Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework. In 2013 International Winter Workshop on Brain-Computer Interface, BCI 2013. 2013. p. 84-85. 6506639. (2013 International Winter Workshop on Brain-Computer Interface, BCI 2013). https://doi.org/10.1109/IWW-BCI.2013.6506639

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abstract = "We were able to efficiently remove the morphological artifact of the fiber bundle based endo-microscopy and improve the featured patterns within the object image acquired by using non-invasive near infrared optical coherence tomography. Our image reconstruction methodology starts to estimate the original shape from the regions that are directly damaged from the en face image which contains significant image degradation by the pixelation of numerous imaging fiber units. Then we have iteratively extended the neighbor areas from the initial status so that we can successfully estimate the original shape of the missing pattern.",

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Learning Markov random field image prior for pixelation removal of fiber microscopy using sparse coding based on Bayesian framework

Abstract

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Keywords

ASJC Scopus subject areas

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