A self-referencing level-set method for image reconstruction from sparse Fourier samples

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address image estimation from sparse Fourier samples. The problem is formulated as joint estimation of the supports of unknown sparse objects in the image, and pixel values on these supports. The domain and the pixel values are alternately estimated using the level-set method and the conjugate gradient method, respectively. Our level-set evolution shows a unique switching behavior, which stabilizes the level-set evolution and removes the re-initialization steps in conventional level set approaches.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Image Processing
Pages33-36
Number of pages4
Volume2
StatePublished - 2001
EventIEEE International Conference on Image Processing (ICIP) - Thessaloniki, Greece
Duration: Oct 7 2001Oct 10 2001

Other

OtherIEEE International Conference on Image Processing (ICIP)
CountryGreece
CityThessaloniki
Period10/7/0110/10/01

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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  • Cite this

    Ye, J. C., Bresler, Y., & Moulin, P. (2001). A self-referencing level-set method for image reconstruction from sparse Fourier samples. In IEEE International Conference on Image Processing (Vol. 2, pp. 33-36)