@inproceedings{3bbb0c61135c43689af59379f5637394,
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.",
author = "Ye, {Jong Chul} and Y. Bresler and P. Moulin",
note = "Publisher Copyright: {\textcopyright} 2001 IEEE.; IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001 ; Conference date: 13-07-2001",
year = "2001",
doi = "10.1109/VLSM.2001.938896",
language = "English (US)",
series = "Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "171--178",
booktitle = "Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001",
address = "United States",
}