An augmented Lagrangian method for fast gradient vector flow computation

Jianfeng Li; Wangmeng Zuo; Xiaofei Zhao; David Zhang

doi:10.1109/ICIP.2011.6115735

An augmented Lagrangian method for fast gradient vector flow computation

Jianfeng Li^*
, Wangmeng Zuo
, Xiaofei Zhao
, David Zhang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

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

8 Scopus citations

Abstract

Gradient vector flow (GVF) and its generalization have been widely applied in many image processing applications. The high cost of GVF computation, however, has restricted their potential applications to images with large size. In this paper, motivated by progress in fast image restoration algorithms, we reformulate the GVF computation problem as a convex optimization model with an equality constraint, and solve it using a fast algorithm, inexact augmented Lagrangian method (ALM). With fast Fourier transform (FFT), we provide a novel simple and efficient algorithm for GVF computation. Experimental results show that the proposed method can improve the computational speed by an order of magnitude, and is even more efficient for images with large sizes.

Original language	English
Title of host publication	ICIP 2011
Subtitle of host publication	2011 18th IEEE International Conference on Image Processing
Pages	1525-1528
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2011.6115735
State	Published - 2011
Externally published	Yes
Event	2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium Duration: 11 Sep 2011 → 14 Sep 2011

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
ISSN (Print)	1522-4880

Conference

Conference	2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/Territory	Belgium
City	Brussels
Period	11/09/11 → 14/09/11

Keywords

Gradient vector flow
augmented Lagrange multiplier
convex optimization
fast Fourier transform

Access to Document

10.1109/ICIP.2011.6115735

Cite this

@inproceedings{f1c1b984f93a40cdb3ae12776e0d870e,

title = "An augmented Lagrangian method for fast gradient vector flow computation",

abstract = "Gradient vector flow (GVF) and its generalization have been widely applied in many image processing applications. The high cost of GVF computation, however, has restricted their potential applications to images with large size. In this paper, motivated by progress in fast image restoration algorithms, we reformulate the GVF computation problem as a convex optimization model with an equality constraint, and solve it using a fast algorithm, inexact augmented Lagrangian method (ALM). With fast Fourier transform (FFT), we provide a novel simple and efficient algorithm for GVF computation. Experimental results show that the proposed method can improve the computational speed by an order of magnitude, and is even more efficient for images with large sizes.",

keywords = "Gradient vector flow, augmented Lagrange multiplier, convex optimization, fast Fourier transform",

author = "Jianfeng Li and Wangmeng Zuo and Xiaofei Zhao and David Zhang",

year = "2011",

doi = "10.1109/ICIP.2011.6115735",

language = "英语",

isbn = "9781457713033",

series = "Proceedings - International Conference on Image Processing, ICIP",

pages = "1525--1528",

booktitle = "ICIP 2011",

note = "2011 18th IEEE International Conference on Image Processing, ICIP 2011 ; Conference date: 11-09-2011 Through 14-09-2011",

}

Li, J, Zuo, W, Zhao, X & Zhang, D 2011, An augmented Lagrangian method for fast gradient vector flow computation. in ICIP 2011: 2011 18th IEEE International Conference on Image Processing., 6115735, Proceedings - International Conference on Image Processing, ICIP, pp. 1525-1528, 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, 11/09/11. https://doi.org/10.1109/ICIP.2011.6115735

An augmented Lagrangian method for fast gradient vector flow computation. / Li, Jianfeng; Zuo, Wangmeng; Zhao, Xiaofei et al.
ICIP 2011: 2011 18th IEEE International Conference on Image Processing. 2011. p. 1525-1528 6115735 (Proceedings - International Conference on Image Processing, ICIP).

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

TY - GEN

T1 - An augmented Lagrangian method for fast gradient vector flow computation

AU - Li, Jianfeng

AU - Zuo, Wangmeng

AU - Zhao, Xiaofei

AU - Zhang, David

PY - 2011

Y1 - 2011

N2 - Gradient vector flow (GVF) and its generalization have been widely applied in many image processing applications. The high cost of GVF computation, however, has restricted their potential applications to images with large size. In this paper, motivated by progress in fast image restoration algorithms, we reformulate the GVF computation problem as a convex optimization model with an equality constraint, and solve it using a fast algorithm, inexact augmented Lagrangian method (ALM). With fast Fourier transform (FFT), we provide a novel simple and efficient algorithm for GVF computation. Experimental results show that the proposed method can improve the computational speed by an order of magnitude, and is even more efficient for images with large sizes.

AB - Gradient vector flow (GVF) and its generalization have been widely applied in many image processing applications. The high cost of GVF computation, however, has restricted their potential applications to images with large size. In this paper, motivated by progress in fast image restoration algorithms, we reformulate the GVF computation problem as a convex optimization model with an equality constraint, and solve it using a fast algorithm, inexact augmented Lagrangian method (ALM). With fast Fourier transform (FFT), we provide a novel simple and efficient algorithm for GVF computation. Experimental results show that the proposed method can improve the computational speed by an order of magnitude, and is even more efficient for images with large sizes.

KW - Gradient vector flow

KW - augmented Lagrange multiplier

KW - convex optimization

KW - fast Fourier transform

UR - https://www.scopus.com/pages/publications/84863048023

U2 - 10.1109/ICIP.2011.6115735

DO - 10.1109/ICIP.2011.6115735

M3 - 会议稿件

AN - SCOPUS:84863048023

SN - 9781457713033

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 1525

EP - 1528

BT - ICIP 2011

T2 - 2011 18th IEEE International Conference on Image Processing, ICIP 2011

Y2 - 11 September 2011 through 14 September 2011

ER -

An augmented Lagrangian method for fast gradient vector flow computation

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this