The K nearest neighbor geometry filter based on spatial domain

Nizhuan Wang, Weiming Zeng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)

Abstract

Based on the filter using the K nearest neighbor pixels and geometric mean grayscale value method, a derived image denoising algorithm is proposed in this paper. This approach firstly searches K-1 nearest grayscale neighbors of a central pixel covered by the mask. Then it calculates geometric mean gray value of the K pixels including the k-1 neighbors and the central pixel. Lastly, replaces the grayscale value of the central pixel with the geometric gray value. Experiment results show the proposed method has better performance on the mixed noise suppression in comparison to the classical Mean filter, the standard median filter and the KNN mean filter.

Original languageEnglish
Title of host publication5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
DOIs
Publication statusPublished - 31 May 2011
Externally publishedYes
Event5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011 - Wuhan, China
Duration: 10 May 201112 May 2011

Publication series

Name5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011

Conference

Conference5th International Conference on Bioinformatics and Biomedical Engineering, iCBBE 2011
Country/TerritoryChina
CityWuhan
Period10/05/1112/05/11

Keywords

  • Correlation
  • KNN geometry filter
  • KNN mean filter
  • Mixed noise
  • PSNR

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Fingerprint

Dive into the research topics of 'The K nearest neighbor geometry filter based on spatial domain'. Together they form a unique fingerprint.

Cite this