Image denoising by arithmetic means based on similarity

Yutaka Takagi, Masaaki Ikehara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a Non-Local Means algorithm-based denoising method. In conventional NLM, the weighting functions are acquired based on the similarity between target patch and its neighboring patches and then Gaussian-range kernel is calculated based on the similarity. Then, target patch is replaced by weighted means value of neighboring patches. In comparison, our method extracts similar patches by thresholding and only calculates simple arithmetic average. The method does not only outperform the conventional NLM but also implement with less computation. Finally, we compare the proposed and the conventional NLM, and validate the advantage.

Original languageEnglish
Title of host publication2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467372183
DOIs
Publication statusPublished - 2016 Apr 26
Event10th International Conference on Information, Communications and Signal Processing, ICICS 2015 - Singapore, Singapore
Duration: 2015 Dec 22015 Dec 4

Other

Other10th International Conference on Information, Communications and Signal Processing, ICICS 2015
CountrySingapore
CitySingapore
Period15/12/215/12/4

    Fingerprint

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Takagi, Y., & Ikehara, M. (2016). Image denoising by arithmetic means based on similarity. In 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015 [7459953] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICS.2015.7459953