Non-local mean value image de-noising algorithm based on self-adaption
- 10.2991/asei-15.2015.274How to use a DOI?
- Image de-noising; Non-local means; Information of directional structure; filter parameters; search neighbor-hood.
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.
- © 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/).
Cite this article
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 -