4.3 Article

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

期刊

EAST ASIAN JOURNAL ON APPLIED MATHEMATICS
卷 11, 期 2, 页码 276-300

出版社

GLOBAL SCIENCE PRESS
DOI: 10.4208/eajam.200520.191020

关键词

Euler's elastica; augmented Lagrangian; image denoising

资金

  1. National Natural Science Foundation of China [61971292, 61827809, 61871275]
  2. Key Research Project of the Academy for Multidisciplinary Studies, Capital Normal University
  3. Beijing Higher Institution Engineering Research Centre of Testing and Imaging
  4. Beijing Advanced Innovation Center for Imaging Theory and Technology

向作者/读者索取更多资源

The introduced cutting-off strategy improves the efficiency of minimizing Euler's elastica energy, resolves internal inconsistencies in the model, and decouples complex dependencies between auxiliary splitting variables more effectively, leading to faster convergence and higher quality image restorations.
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 themodel 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.

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