Open AccessA New Method for Nonlocal Means Image Denoising Using Multiple Images
Author(s) -
Xingzheng Wang,
Haoqian Wang,
Jiangfeng Yang,
Yongbing Zhang
Publication year - 2016
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0158664
Subject(s) - pixel , non local means , artificial intelligence , residual , noise reduction , pattern recognition (psychology) , image (mathematics) , computer science , noise (video) , similarity (geometry) , computer vision , gaussian , gaussian noise , mathematics , property (philosophy) , algorithm , image denoising , physics , quantum mechanics , philosophy , epistemology
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.