Title
A self-referencing level-set method for image reconstruction from sparse Fourier samples
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. Furthermore, the trade-off between the stability and the speed of evolution can be easily controlled by the number of the conjugate gradient steps, hence removing the re-initialization steps in conventional level set approaches
Year
DOI
Venue
2002
10.1109/VLSM.2001.938896
International Journal of Computer Vision
Keywords
DocType
Volume
inverse problems,Fourier imaging,medical imaging,partial-data,level-set,geometry-driven diffusion
Journal
50
Issue
ISBN
Citations 
3
0-7695-1278-X
8
PageRank 
References 
Authors
1.05
19
3
Name
Order
Citations
PageRank
Jong Chul Ye171579.99
Yoram Bresler21104119.17
P. Moulin327034.41