A New Method for Nonlocal Means Image Denoising Using Multiple Images.
The basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two...
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Format: | Article |
Language: | English |
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Public Library of Science (PLoS)
2016-01-01
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Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC4961432?pdf=render |
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author | Xingzheng Wang Haoqian Wang Jiangfeng Yang Yongbing Zhang |
author_facet | Xingzheng Wang Haoqian Wang Jiangfeng Yang Yongbing Zhang |
author_sort | Xingzheng Wang |
collection | DOAJ |
description | The basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two main contributions of this paper: The first contribution is that we use two images to denoise the pixel. These two noised images are with the same noise deviation. Instead of using only one image, we calculate the weight from two noised images. After the first denoising process, we get a pre-denoised image and a residual image. The second contribution is combining the nonlocal property between residual image and pre-denoised image. The improved nonlocal means method pays more attention on the similarity than the original one, which turns out to be very effective in eliminating gaussian noise. Experimental results with simulated data are provided. |
first_indexed | 2024-12-10T11:24:51Z |
format | Article |
id | doaj.art-676fbbf14dc14e689aac4a38607c3849 |
institution | Directory Open Access Journal |
issn | 1932-6203 |
language | English |
last_indexed | 2024-12-10T11:24:51Z |
publishDate | 2016-01-01 |
publisher | Public Library of Science (PLoS) |
record_format | Article |
series | PLoS ONE |
spelling | doaj.art-676fbbf14dc14e689aac4a38607c38492022-12-22T01:50:47ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01117e015866410.1371/journal.pone.0158664A New Method for Nonlocal Means Image Denoising Using Multiple Images.Xingzheng WangHaoqian WangJiangfeng YangYongbing ZhangThe basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two main contributions of this paper: The first contribution is that we use two images to denoise the pixel. These two noised images are with the same noise deviation. Instead of using only one image, we calculate the weight from two noised images. After the first denoising process, we get a pre-denoised image and a residual image. The second contribution is combining the nonlocal property between residual image and pre-denoised image. The improved nonlocal means method pays more attention on the similarity than the original one, which turns out to be very effective in eliminating gaussian noise. Experimental results with simulated data are provided.http://europepmc.org/articles/PMC4961432?pdf=render |
spellingShingle | Xingzheng Wang Haoqian Wang Jiangfeng Yang Yongbing Zhang A New Method for Nonlocal Means Image Denoising Using Multiple Images. PLoS ONE |
title | A New Method for Nonlocal Means Image Denoising Using Multiple Images. |
title_full | A New Method for Nonlocal Means Image Denoising Using Multiple Images. |
title_fullStr | A New Method for Nonlocal Means Image Denoising Using Multiple Images. |
title_full_unstemmed | A New Method for Nonlocal Means Image Denoising Using Multiple Images. |
title_short | A New Method for Nonlocal Means Image Denoising Using Multiple Images. |
title_sort | new method for nonlocal means image denoising using multiple images |
url | http://europepmc.org/articles/PMC4961432?pdf=render |
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