A Restricted Linearised Augmented Lagrangian Method for Euler's Elastica Model

A Restricted Linearised Augmented Lagrangian Method for Euler's Elastica Model

Year:    2021

Author:    Yinghui Zhang, Xiaojuan Deng, Xing Zhao, Hongwei Li

East Asian Journal on Applied Mathematics, Vol. 11 (2021), Iss. 2 : pp. 276–300

Abstract

A simple cutting-off strategy for the augmented Lagrangian formulation for minimising the Euler's elastica energy is introduced. It is connected to a discovered internal inconsistency of the model and helps to decouple the tricky dependence between auxiliary splitting variables, thus fixing the problem mentioned. Numerical experiments show that the method converges much faster than conventional algorithms, provides a better parameter-tuning and ensures the higher quality of image restorations.

You do not have full access to this article.

Already a Subscriber? Sign in as an individual or via your institution

Journal Article Details

Publisher Name:    Global Science Press

Language:    English

DOI:    https://doi.org/10.4208/eajam.200520.191020

East Asian Journal on Applied Mathematics, Vol. 11 (2021), Iss. 2 : pp. 276–300

Published online:    2021-01

AMS Subject Headings:   

Copyright:    COPYRIGHT: © Global Science Press

Pages:    25

Keywords:    Euler's elastica augmented Lagrangian image denoising.

Author Details

Yinghui Zhang

Xiaojuan Deng

Xing Zhao

Hongwei Li