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

Author

Ye, Jong ; Bresler, Yoram ; Moulin, Pierre

Author_Institution

Coordinated Sci. Lab., Illinois Univ., Urbana, IL, USA

Volume

2

fYear

2001

Firstpage

33

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

Keywords

Fourier transforms; conjugate gradient methods; image reconstruction; object recognition; optimisation; parameter estimation; set theory; Fourier transform; conjugate gradient method; image estimation; image reconstruction; nonlinear optimization technique; self-referencing level-set method; sparse Fourier samples; Character generation; Fourier transforms; Gradient methods; Image reconstruction; Image sampling; Level set; Magnetic resonance imaging; Paramagnetic resonance; Pixel; Synthetic aperture radar;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 2001. Proceedings. 2001 International Conference on

Conference_Location

Thessaloniki

Print_ISBN

0-7803-6725-1

Type

conf

DOI

10.1109/ICIP.2001.958417

Filename

958417