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

Jong Chul Ye; Y. Bresler; P. Moulin

doi:10.1109/VLSM.2001.938896

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

Research output: Chapter in Book/Report/Conference proceeding › Conference 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. 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.

Original language	English (US)
Title of host publication	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
Number of pages	8
ISBN (Electronic)	076951278X, 9780769512785
DOIs	https://doi.org/10.1109/VLSM.2001.938896
State	Published - 2001
Event	IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001 - Vancouver, Canada Duration: Jul 13 2001 → …

Publication series

Name	Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001

Other

Other	IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001
Country	Canada
City	Vancouver
Period	7/13/01 → …

ASJC Scopus subject areas

Computational Theory and Mathematics
Computer Vision and Pattern Recognition

Access to Document

10.1109/VLSM.2001.938896

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Ye, J. C., Bresler, Y., & Moulin, P. (2001). A self-referencing level-set method for image reconstruction from sparse Fourier samples. In Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001 (pp. 171-178). [938896] (Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VLSM.2001.938896

A self-referencing level-set method for image reconstruction from sparse Fourier samples. / Ye, Jong Chul; Bresler, Y.; Moulin, P.

Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001. Institute of Electrical and Electronics Engineers Inc., 2001. p. 171-178 938896 (Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ye, JC, Bresler, Y & Moulin, P 2001, A self-referencing level-set method for image reconstruction from sparse Fourier samples. in Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001., 938896, Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001, Institute of Electrical and Electronics Engineers Inc., pp. 171-178, IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001, Vancouver, Canada, 7/13/01. https://doi.org/10.1109/VLSM.2001.938896

Ye JC, Bresler Y , Moulin P. A self-referencing level-set method for image reconstruction from sparse Fourier samples. In Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001. Institute of Electrical and Electronics Engineers Inc. 2001. p. 171-178. 938896. (Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001). https://doi.org/10.1109/VLSM.2001.938896

Ye, Jong Chul ; Bresler, Y. ; Moulin, P. / A self-referencing level-set method for image reconstruction from sparse Fourier samples. Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001. Institute of Electrical and Electronics Engineers Inc., 2001. pp. 171-178 (Proceedings - IEEE Workshop on Variational and Level Set Methods in Computer Vision, VLSM 2001).

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