Algorithms for finding global minimizers of image segmentation and denoising models

T.F. Chan; S. Esedoglu; M. Nikolova

doi:10.1137/040615286

Algorithms for finding global minimizers of image segmentation and denoising models

T.F. Chan, S. Esedoglu, M. Nikolova

Research output: Contribution to journal › Article › peer-review

882 Scopus citations

Abstract

We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes. © 2006 Society for Industrial and Applied Mathematics.

Original language	English
Pages (from-to)	1632-1648
Number of pages	17
Journal	SIAM Journal on Applied Mathematics
Volume	66
Issue number	5
DOIs	https://doi.org/10.1137/040615286
State	Published - 2006
Externally published	Yes

Bibliographical note

cited By 644

Keywords

Convex minimization problems
Denoising
Denoising models
Nonconvex optimization, Algorithms
Computer vision
Global optimization
Problem solving, Image segmentation

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Cite this

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title = "Algorithms for finding global minimizers of image segmentation and denoising models",

abstract = "We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes. {\textcopyright} 2006 Society for Industrial and Applied Mathematics.",

keywords = "Convex minimization problems, Denoising, Denoising models, Nonconvex optimization, Algorithms, Computer vision, Global optimization, Problem solving, Image segmentation",

author = "T.F. Chan and S. Esedoglu and M. Nikolova",

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AU - Chan, T.F.

AU - Esedoglu, S.

AU - Nikolova, M.

N1 - cited By 644

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N2 - We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes. © 2006 Society for Industrial and Applied Mathematics.

AB - We show how certain nonconvex optimization problems that arise in image processing and computer vision can be restated as convex minimization problems. This allows, in particular, the finding of global minimizers via standard convex minimization schemes. © 2006 Society for Industrial and Applied Mathematics.

KW - Convex minimization problems

KW - Denoising

KW - Denoising models

KW - Nonconvex optimization, Algorithms

KW - Computer vision

KW - Global optimization

KW - Problem solving, Image segmentation

U2 - 10.1137/040615286

DO - 10.1137/040615286

M3 - Article

SN - 1095-712X

VL - 66

SP - 1632

EP - 1648

JO - SIAM Journal on Applied Mathematics

JF - SIAM Journal on Applied Mathematics

IS - 5

ER -

Algorithms for finding global minimizers of image segmentation and denoising models

Abstract

Bibliographical note

Keywords

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