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 languageEnglish
Title of host publication2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
Pages84-85
Number of pages2
DOIs
Publication statusPublished - 2013
Event2013 International Winter Workshop on Brain-Computer Interface, BCI 2013 - Gangwon Province, Korea, Republic of
Duration: 2013 Feb 18 → 2013 Feb 20

Publication series

Name2013 International Winter Workshop on Brain-Computer Interface, BCI 2013

Other

Other2013 International Winter Workshop on Brain-Computer Interface, BCI 2013
CountryKorea, Republic of
CityGangwon Province
Period13/2/18 → 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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