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Open AccessJournal Article

Improved Non-local Means Algorithm for Image Denoising

Liu Xiao-ming, +3 more
- 01 Jan 2012 - 
- Vol. 38, Iss: 4, pp 199-201
TLDR
Experimental results show the algorithm has a superior denoising performance than the original one, especially with detail information in the image, and PSNR can be improved by 1.6 dB at most.
Abstract
Aiming at the problem of the over-smoothness and blurs the details,which are caused by exponential kernel function used in original non-local means algorithm,this paper proposes a cosine Gaussian kernel function based on exponential kernel function and combined with a cosine coefficient and Gaussian kernelIt is used in the weight-computing of the improved algorithmExperimental results show the algorithm has a superior denoising performance than the original one,especially with detail information in the image,and PSNR can be improved by 16 dB at most

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Citations
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Overview of Image Denoising Based on Deep Learning

TL;DR: Three kinds of models, such as convolutional neural network, pulse coupled neural network and wavelet neural network are introduced, which are commonly used in image denoising, to clearly understand the latest developments in deep learning in the field of image noise reduction.
Proceedings ArticleDOI

An improved non-local means algorithm for image denoising

TL;DR: An improved nonlocal mean filter image denoising algorithm is designed by analyzing the shortcomings of Gaussian weighted Euclidean distance in measuring neighborhood similarity and the gradient information of a filtered image is used and applied to the Gaussian weighting coefficient to achieve the purpose of adaptive filtering.
Proceedings ArticleDOI

An Improved Non-local Means Denoising Algorithm and Its Application in Denoising Analysis of Metal Fracture

TL;DR: The experimental results show that the denoising performance of the proposed algorithm has higher signal-to-noise ratio than the traditional algorithms, and it is more complete in the preservation of metal fracture boundary information.
Proceedings ArticleDOI

A multi-scale texture segmentation method

TL;DR: A new texture segmentation method fused with Local Binary Pattern, Gray-level Co-occurrence Matrix and permutation entropy shows visible improvements both in segmentation accuracy, and in increasing boundary precision and region harmony.
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Shannon-Cosine Wavelet Precise Integration Method for Locust Slice Image Mixed Denoising

TL;DR: A novel denoising method for removing mixed noise from locust slice images is proposed by means of Shannon-cosine wavelet and the nonlinear variational model for the image processing and shows that both the values of SSIM and PSNR of the denoised locustslice images are better than the classical methods besides the algorithm efficiency.
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