On semismooth Newton's methods for total variation minimization

Michael K. Ng; Liqun Qi; Yu Fei Yang; Yu Mei Huang

doi:10.1007/s10851-007-0650-0

On semismooth Newton's methods for total variation minimization

Michael K. Ng, Liqun Qi, Yu Fei Yang, Yu Mei Huang

Research output: Journal article publication › Journal article › Academic research › peer-review

73 Citations (Scopus)

Abstract

In [2], Chambolle proposed an algorithm for minimizing the total variation of an image. In this short note, based on the theory on semismooth operators, we study semismooth Newton's methods for total variation minimization. The convergence and numerical results are also presented to show the effectiveness of the proposed algorithms.

Original language	English
Pages (from-to)	265-276
Number of pages	12
Journal	Journal of Mathematical Imaging and Vision
Volume	27
Issue number	3
DOIs	https://doi.org/10.1007/s10851-007-0650-0
Publication status	Published - 1 Apr 2007

Keywords

Denoising
Regularization
Semismooth Newton's methods
Total variation

ASJC Scopus subject areas

Control and Systems Engineering
Artificial Intelligence
Computer Graphics and Computer-Aided Design
Software
Applied Mathematics
Computer Vision and Pattern Recognition

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10.1007/s10851-007-0650-0

Cite this

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On semismooth Newton's methods for total variation minimization

Abstract

Keywords

ASJC Scopus subject areas

More information

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