Improved Nonlocal Means Based on Pre-Classification and Invariant Block Matching

Yan, Ruomei, Shao, Ling, Cvetkovic, Sascha and Klijn, Jan (2012) Improved Nonlocal Means Based on Pre-Classification and Invariant Block Matching. Journal of Display Technology, 8 (4). pp. 212-218. ISSN 1551-319X

Full text not available from this repository. (Request a copy)
Official URL:


One of the most popular image denoising methods based on self-similarity is called nonlocal means (NLM). Though it can achieve remarkable performance, this method has a few shortcomings, e.g., the computationally expensive calculation of the similarity measure, and the lack of reliable candidates for some nonrepetitive patches. In this paper, we propose to improve NLM by integrating Gaussian blur, clustering, and rotationally invariant block matching (RIBM) into the NLM framework. Experimental results show that the proposed technique can perform denoising better than the original NLM both quantitatively and visually, especially when the noise level is high.

Item Type: Article
Uncontrolled Keywords: Gaussian blur, image denoising, K-means clustering, moment invariants, nonlocal means (NLM), rotationally invariant block matching (RIBM)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: Paul Burns
Date Deposited: 10 Jun 2015 15:07
Last Modified: 12 Oct 2019 22:30

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics