Doubly Adaptive Nonlocal Means Image Denoising Algorithm Based on Mathematical Morphology

2013 ◽  
Author(s):  
Zhao Jing-Juan ◽  
Zhou Zuo-Feng ◽  
Cao Jian-Zhong ◽  
Zhang Hui
Keyword(s):  
Image Denoising ◽  
Mathematical Morphology ◽  
Nonlocal Means

scholarly journals Image Denoising Based on Dilated Singularity Prior

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Shaoxiang Hu ◽  
Zhiwu Liao ◽  
Wufan Chen
Keyword(s):  
Em Algorithm ◽  
Image Denoising ◽  
Mathematical Morphology ◽  
Visual Quality ◽  
Spatial Domain ◽  
Experimental Results ◽  
Canny Operator ◽  
Nonlocal Means ◽  
Spatial Image ◽  
Noisy Images

In order to preserve singularities in denoising, we propose a new scheme by adding dilated singularity prior to noisy images. The singularities are detected by canny operator firstly and then dilated using mathematical morphology for finding pixels “near” singularities instead of “on” singularities. The denoising results for pixels near singularities are obtained by nonlocal means in spatial domain to preserve singularities while the denoising results for pixels in smooth regions are obtained by EM algorithm constrained by a mask formed by downsampled spatial image with dilated singularity prior to suiting the sizes of the subbands of wavelets. The final denoised results are got by combining the above two results. Experimental results show that the scheme can preserve singularity well with relatively high PSNR and good visual quality.

scholarly journals An Improved Image Denoising Model Based on Nonlocal Means Filter

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yan Jin ◽  
Wenyu Jiang ◽  
Jianlong Shao ◽  
Jin Lu
Keyword(s):  
Image Denoising ◽  
Natural Images ◽  
Nonlocal Means ◽  
New Model ◽  
Model Based ◽  
Better Than

The nonlocal means filter plays an important role in image denoising. We propose in this paper an image denoising model which is a suitable improvement of the nonlocal means filter. We compare this model with the nonlocal means filter, both theoretically and experimentally. Experiment results show that this new model provides good results for image denoising. Particularly, it is better than the nonlocal means filter when we consider the denoising for natural images with high textures.

scholarly journals Image Denoising in Spatial and Transform Domains

2021 ◽  
Author(s):  
Mina Sharifymoghaddam
Keyword(s):  
Image Denoising ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Weighting Scheme ◽  
Block Matching ◽  
Hard Thresholding ◽  
Weighted Version ◽  
Nonlocal Means ◽  
Processing Step ◽  
Index Measure

Image denoising is an inseparable pre-processing step of many image processing algorithms. Two mostly used image denoising algorithms are Nonlocal Means (NLM) and Block Matching and 3D Transform Domain Collaborative Filtering (BM3D). While BM3D outperforms NLM on variety of natural images, NLM is usually preferred when the algorithm complexity is an issue. In this thesis, we suggest modified version of these two methods that improve the performance of the original approaches. The conventional NLM uses weighted version of all patches in a search neighbourhood to denoise the center patch. However, it can include some dissimilar patches. Our first contribution, denoted by Similarity Validation Based Nonlocal Means (NLM-SVB), eliminates some of those unnecessary dissimilar patches in order to improve the performance of the algorithm. We propose a hard thresholding pre-processing step based on the exact distribution of distances of similar patches. Consequently, our method eliminates about 60% of dissimilar patches and improves NLM in terms of Peak Signal to Noise Ratio (PSNR) and Stracuteral Similarity Index Measure (SSIM). Our second contribution, denoted by Probabilistic Weighting BM3D (PW-BM3D), is the result of our thorough study of BM3D. BM3D consists of two main steps. One is finding a basic estimate of the noiseless image by hard thresholding coefficients. The second one is using this estimate to perform wiener filtering. In both steps the weighting scheme in the aggregation process plays an important role. The current weighting process depends on the variance of retrieved coefficients after denoising which results in a biased weighting. In PW-BM3D, we propose a novel probabilistic weighting scheme which is a function of the probability of similarity of noiseless patches in each 3D group. The results show improvement over BM3D in terms of PSNR for an average of about 0.2dB.

Improved nonlocal means method based on adaptive pre-classification for image denoising

2016 ◽  
Author(s):  
Shaorong He ◽  
Yaping Lin ◽  
Yonghe Liu ◽  
Junfeng Yang ◽  
Hongyan Jiang
Keyword(s):  
Image Denoising ◽  
Nonlocal Means

Image denoising method used in earthquake ruin scene based on multi-scale mathematical morphology

2012 ◽  
Vol 10 (s2) ◽  
pp. S21002-321004 ◽  
Author(s):  
Yanxing Song Yanxing Song ◽  
Shucong Liu Shucong Liu ◽  
Jingsong Yang Jingsong Yang
Keyword(s):  
Image Denoising ◽  
Mathematical Morphology ◽  
Denoising Method ◽  
Multi Scale

An enhanced nonlocal-means algorithm for image denoising

2007 ◽  
Author(s):  
Anoosheh Heidarzadeh ◽  
Alireza Nasiri Avanaki
Keyword(s):  
Image Denoising ◽  
Nonlocal Means

Gabor feature based nonlocal means filter for textured image denoising

2012 ◽  
Vol 23 (7) ◽  
pp. 1008-1018 ◽  
Author(s):  
Shanshan Wang ◽  
Yong Xia ◽  
Qiegen Liu ◽  
Jianhua Luo ◽  
Yuemin Zhu ◽  
...  
Keyword(s):  
Image Denoising ◽  
Nonlocal Means ◽  
Gabor Feature ◽  
Feature Based

Image Denoising Using Quadtree-Based Nonlocal Means With Locally Adaptive Principal Component Analysis

2016 ◽  
Vol 23 (4) ◽  
pp. 434-438 ◽  
Author(s):  
Chenglin Zuo ◽  
Ljubomir Jovanov ◽  
Bart Goossens ◽  
Hiep Quang Luong ◽  
Wilfried Philips ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Image Denoising ◽  
Principal Component ◽  
Component Analysis ◽  
Nonlocal Means ◽  
Locally Adaptive ◽  
Adaptive Principal Component Analysis
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