Combination of closest space and closest structure to ameliorate non-local means method

Author

Do, Quoc Bao ; Beghdadi, Azeddine ; Luong, Marie

Author_Institution

L2TI, Univ. Paris 13, Villetaneuse, France

fYear

2011

fDate

11-15 April 2011

Firstpage

134

Lastpage

141

Abstract

Recently non-local means (NLM) has been known to be one of the most attractive denoising algorithms. It alters each pixel by a weighted average of pixels in the image. The weights express the level of similarity between two small patches defined for two involved pixels. There are many propositions to ameliorate the performance of this method. One of branches is to seek the whole image the most similar patches for a given one. In this paper, we investigate this approach and show that it is suitable for only highly textured images. Moreover, we show that combination of this approach and the original NLM yields better result for all image types.

Keywords

filtering theory; image classification; image denoising; image restoration; image texture; learning (artificial intelligence); closest space; closest structure; denoising algorithm; filtering; highly textured image; image patch; image pixel; image restoration; k-nearest neighbor algorithm; nonlocal means method; similarity level; Fingerprint recognition; Image restoration; Noise measurement; Noise reduction; PSNR; Pixel; denoising; filtering; image restoration; kNN;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence for Multimedia, Signal and Vision Processing (CIMSIVP), 2011 IEEE Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-9913-7

Type

conf

DOI

10.1109/CIMSIVP.2011.5949253

Filename

5949253