A divide-and-conquer stochastic alterable direction image denoising method

A novel image denoising method based on stochastic technique is proposed in this paper. The procedure is divided into two phases: the appropriate random sampling strategy is adopted to search for similar patches, then the original image is estimated by these patches. Specifically, in order to reduce...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Signal processing Jg. 108; S. 90 - 101
Hauptverfasser: Feng, Xiang-chu, Luo, Liang, Jia, Xi-xi, Wang, Wei-wei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2015
Schlagworte:
Computer simulation
Image denoising
Markov-Chain Monte Carlo sampling
Monte Carlo methods
Noise reduction
Non-local means
Sampling
Searching
Stochasticity
Two-directional method
Wavelet transforms
Non-local means
Two-directional method
Markov-Chain Monte Carlo sampling
Image denoising
ISSN:0165-1684, 1872-7557
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A novel image denoising method based on stochastic technique is proposed in this paper. The procedure is divided into two phases: the appropriate random sampling strategy is adopted to search for similar patches, then the original image is estimated by these patches. Specifically, in order to reduce the sampling rejection rate, the observed image is decomposed into different frequency bands by 2D wavelet transform, then the similar patches are collected by alterable direction Markov-Chain Monte Carlo (MCMC) sampling with a properly chosen rejection criterion. Rather than taking the weighted average of similar patches, we use two-directional non-local (TDNL) method in order to take full use of the similarity between similar patches collected. The simulation results show that the proposed method improves the efficiency of searching similar patches. Compared with the NLM and BM3D method, our approach has lower computational complexity, better performance in protecting image details and higher visual quality, respectively. •This paper presents a divide-and-conquer technique to search the similar patches in an image.•The original image patches are approximated by TDNL approximation method.•An effective image denoising algorithm is proposed.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2014.08.036