Nonlocal total variation regularization with Shape Adaptive Patches for image denoising via Split Bregman method

Shuli Ma*, Huiqian Du, Wenbo Mei, Luliang Jia

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

Non-Local or patch-based approaches are used in most of the state-of-the-art methods for image denoising. Denoising with regular square patches may cause noise halos around edges, particularly high contrasted edges. In order to overcome this drawback, this work presents an extension of the Non-Local TV framework that effectively exploits the potential local geometric features of the image by replacing the simple square patches with different oriented shapes. We first solve the denoised models of ROF-NLTV with different oriented patch shapes by Split Bregman algorithm. Then, use exponentially weighted aggregation method based on Stein's unbiased risk estimate to combine the estimators obtained in the first step. The numerical results show our method outperforms some previous ones. Moreover, common noise halos around edges usually observed by denoising with Non-Local TV method are reduced thanks our approach.

Original languageEnglish
Article number012008
JournalJournal of Physics: Conference Series
Volume1914
Issue number1
DOIs
Publication statusPublished - 24 May 2021
Event2021 International Conference on Electrical, Electronics and Computing Technology, EECT 2021 - Xiamen, Virtual, China
Duration: 26 Mar 202128 Mar 2021

Keywords

  • Image denoising
  • Nonlocal total variation
  • Split Bregman algorithm
  • aggregation

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