Image denoising by arithmetic means based on similarity

Yutaka Takagi; Masaaki Ikehara

doi:10.1109/ICICS.2015.7459953

Image denoising by arithmetic means based on similarity

Yutaka Takagi, Masaaki Ikehara

Department of Electronics and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467372183
DOIs	https://doi.org/10.1109/ICICS.2015.7459953
Publication status	Published - 2016 Apr 26
Event	10th International Conference on Information, Communications and Signal Processing, ICICS 2015 - Singapore, Singapore Duration: 2015 Dec 2 → 2015 Dec 4

Publication series

Name	2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015

Other

Other	10th International Conference on Information, Communications and Signal Processing, ICICS 2015
Country/Territory	Singapore
City	Singapore
Period	15/12/2 → 15/12/4

ASJC Scopus subject areas

Computer Networks and Communications
Information Systems
Signal Processing

Access to Document

10.1109/ICICS.2015.7459953

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] (2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICS.2015.7459953

Image denoising by arithmetic means based on similarity. / Takagi, Yutaka; Ikehara, Masaaki.
2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015. Institute of Electrical and Electronics Engineers Inc., 2016. 7459953 (2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015, Institute of Electrical and Electronics Engineers Inc., 10th International Conference on Information, Communications and Signal Processing, ICICS 2015, Singapore, Singapore, 15/12/2. https://doi.org/10.1109/ICICS.2015.7459953

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

@inproceedings{dfc64bb3dbe94b9396230f86cb6323e2,

title = "Image denoising by arithmetic means based on similarity",

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.",

author = "Yutaka Takagi and Masaaki Ikehara",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th International Conference on Information, Communications and Signal Processing, ICICS 2015 ; Conference date: 02-12-2015 Through 04-12-2015",

year = "2016",

month = apr,

day = "26",

doi = "10.1109/ICICS.2015.7459953",

language = "English",

series = "2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015",

}

TY - GEN

T1 - Image denoising by arithmetic means based on similarity

AU - Takagi, Yutaka

AU - Ikehara, Masaaki

PY - 2016/4/26

Y1 - 2016/4/26

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84973596632&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84973596632&partnerID=8YFLogxK

U2 - 10.1109/ICICS.2015.7459953

DO - 10.1109/ICICS.2015.7459953

M3 - Conference contribution

AN - SCOPUS:84973596632

T3 - 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015

BT - 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th International Conference on Information, Communications and Signal Processing, ICICS 2015

Y2 - 2 December 2015 through 4 December 2015

ER -

Image denoising by arithmetic means based on similarity

Abstract

Publication series

Other

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this