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

Research output: Contribution to journalArticle

Abstract

We address an ill-posed inverse problem of image estimation from sparse samples of its Fourier transform. 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. Furthermore, the trade-off between the stability and the speed of evolution can be easily controlled by the number of the conjugate gradient steps, thus avoiding the re-initialization steps in conventional level set approaches.

Original languageEnglish (US)
Pages (from-to)253-270
Number of pages18
JournalInternational Journal of Computer Vision
Volume50
Issue number3
DOIs
StatePublished - Dec 1 2002

Keywords

  • Fourier imaging
  • Geometry-driven diffusion
  • Inverse problems
  • Level-set
  • Medical imaging
  • Partial-data

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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