scholarly journals A Hybrid and Adaptive Non-Local Means Wavelet based MRI Denoising Method with Bilateral Filter Enhancement

2017 ◽  
Vol 166 (10) ◽  
pp. 1-7
Author(s):  
Kenneth Kagoiya ◽  
Elijah Mwangi
Keyword(s):  
Bilateral Filter ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

scholarly journals Monte-Carlo Acceleration of Bilateral Filter and Non-Local Means

2018 ◽  
Vol 27 (3) ◽  
pp. 1462-1474 ◽  
Author(s):  
Christina Karam ◽  
Keigo Hirakawa
Keyword(s):  
Monte Carlo ◽  
Bilateral Filter ◽  
Local Means ◽  
Non Local

scholarly journals Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means

2020 ◽  
Vol 206 ◽  
pp. 03019
Author(s):  
Kun Zhao ◽  
Jisheng Ding ◽  
YanFei Sun ◽  
ZhiYuan Hu
Keyword(s):  
Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Side Scan Sonar ◽  
Sonar Image ◽  
Local Means ◽  
Residual Component ◽  
Mode Decomposition ◽  
Non Local ◽  
Bidimensional Empirical Mode Decomposition

In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.

scholarly journals Optimal graph edge weights driven nlms with multi-layer residual compensation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Graph Laplacian ◽  
Denoising Method ◽  
Local Means ◽  
Effective Performance ◽  
Non Local ◽  
Local Gradient ◽  
Laplacian Regularization ◽  
Image Details ◽  
Optimal Graph

AbstractNon-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.

scholarly journals Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures

2018 ◽  
Vol 8 (10) ◽  
pp. 1985 ◽  
Author(s):  
Yoshihiro Maeda ◽  
Norishige Fukushima ◽  
Hiroshi Matsuo
Keyword(s):  
Computational Cost ◽  
Bilateral Filter ◽  
Processing Unit ◽  
Normal Numbers ◽  
Edge Preserving ◽  
Central Processing ◽  
Local Means ◽  
Kernel Weights ◽  
Computational Performance ◽  
Non Local

In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers; thus, the computational performance of edge-preserving filtering is severely diminished. We propose approaches to prevent the occurrence of the denormalized numbers for acceleration. Moreover, we verify an effective vectorization of the edge-preserving filtering based on changes in microarchitectures of central processing units by carefully treating kernel weights. The experimental results show that the proposed methods are up to five-times faster than the straightforward implementation of bilateral filtering and non-local means filtering, while the filters maintain the high accuracy. In addition, we showed effective vectorization for each central processing unit microarchitecture. The implementation of the bilateral filter is up to 14-times faster than that of OpenCV. The proposed methods and the vectorization are practical for real-time tasks such as image editing.

Performance Assessment of Edge Preserving Filters

2017 ◽  
Vol 8 (2) ◽  
pp. 1-29
Author(s):  
Kamireddy Rasool Reddy ◽  
Madhava Rao Ch ◽  
Nagi Reddy Kalikiri
Keyword(s):  
Bilateral Filter ◽  
Wavelet Thresholding ◽  
Bilateral Filtering ◽  
Noisy Image ◽  
Edge Preserving ◽  
Local Means ◽  
Non Local ◽  
Assess Quality ◽  
Filter Algorithms ◽  
Denoised Image

Denoising is one of the important aspects in image processing applications. Denoising is the process of eliminating the noise from the noisy image. In most cases, noise accumulates at the edges. So that prevention of noise at edges is one of the most prominent problem. There are numerous edge preserving approaches available to reduce the noise at edges in that Gaussian filter, bilateral filter and non-local means filtering are the popular approaches but in these approaches denoised image suffer from blurring. To overcome these problems, in this article a Gaussian/bilateral filtering (G/BF) with a wavelet thresholding approach is proposed for better image denoising. The performance of the proposed work is compared with some edge-preserving filter algorithms such as a bilateral filter and the Non-Local Means Filter, in terms that objectively assess quality. From the simulation results, it is found that the performance of proposed method is superior to the bilateral filter and the Non-Local Means Filter.

Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

2020 ◽  
Vol 13 (4) ◽  
pp. 14-31
Author(s):  
Nikita Joshi ◽  
Sarika Jain ◽  
Amit Agarwal
Keyword(s):  
Magnetic Resonance ◽  
Total Variation ◽  
Similarity Index ◽  
Noise Removal ◽  
Magnetic Resonance Images ◽  
Variation Method ◽  
Mr Images ◽  
Denoising Method ◽  
Local Means ◽  
Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

An Improvement of Non-Local Means Denoising Method in the Presence of Large Noise

2012 ◽  
Vol 263-266 ◽  
pp. 223-226
Author(s):  
Musab Elkheir Salih ◽  
Xu Ming Zhang ◽  
Ming Yue Ding
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Additive White Gaussian Noise ◽  
Principal Component ◽  
Singular Value ◽  
Kernel Principal Component Analysis ◽  
Denoising Method ◽  
Local Means ◽  
Non Local ◽  
Value Decomposition

The performance of singular value decomposition (SVD) based nonlocal mean (NLM) denoising method degrades when the noise is high. This paper describes an approach of an improvement of NLM denoising when the noise is large. Instead of SVD, we combine the kernel principal component analysis (KPCA) with NLM. It is demonstrated in terms of peak signal to noise ratio (PSNR) in decibels (dB) that the NLM denoising method is improved using various test images corrupted by large additive white Gaussian noise (AWGN)

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