Non-local mean value image de-noising algorithm based on self-adaption

DOI

10.2991/asei-15.2015.274How to use a DOI?

Keywords

Image de-noising; Non-local means; Information of directional structure; filter parameters; search neighbor-hood.

Abstract

In traditional non-local mean value algorithm, both filter parameters and measurement of search neighborhood are constant globally and it does not show diverse structural features of different areas in images, which makes the weight of similarity of image blocks distributed unreasonably, and thus de-noising can be not effective. In order to avoid that, a non-local mean value image de-noising algorithm based on self-adaption is pro-posed. With analysis of the content of image blocks, image blocks from different areas will obtain different filter parameters and search fields, which cause the similarity weights of image blocks a more proper distribution. Experimental results showed that the new algorithm, after de-noising, achieved an increase of peak signal to noise ratio of images and, at the same time, reserved details of images and marginal information effectively.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bin Yang
AU  - Mingming Guo
AU  - Xinhua Dou
PY  - 2015/05
DA  - 2015/05
TI  - Non-local mean value image de-noising algorithm based on self-adaption
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 1387
EP  - 1390
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.274
DO  - 10.2991/asei-15.2015.274
ID  - Yang2015/05
ER  -