一种改进的非局部平均去噪方法

 对非局部平均去噪算法提出了以下改进：首先,利用图像中具有对称结构的性质,在相似性邻域的比较中引入邻域的对称变换,更好地利用了图像的自相似性质;其次,提出一种基于图像灰度分布统计特性的滤波参数选取方法,能够根据不同像素的特点自适应地选取滤波参数;此外,利用非局部平均算法能有效地保护图像结构信息的性质,提出一种两级非局部平均去噪方法.对测试图像去噪的实验结果表明,与原始算法相比,提出的改进方法能够在保护图像结构信息的前提下更有效地去除噪声,峰值信噪比最多可以提高5.9dB, 去噪效果优于BM-3D方法.     

非局部群稀疏表示的图像去噪模型

针对图像的非局部稀疏表示忽略图像中结构相似信息的缺点,将群稀疏表示引入到图像的最优滤波中,提出了一种有效去除图像高斯噪声的非局部群稀疏表示模型。该模型首先选择图像非局部相似块构建相似矩阵,在群稀疏限制下对相似矩阵进行正交分解得到正交矩阵;在已知噪声服从高斯分布的情况下,再通过求得的正交矩阵结合贝叶斯最小均方误差准则实现对特征矩阵的最优估计;最后通过正交矩阵与特征矩阵重构去噪后的图像。实验对比证明,所提的非局部群稀疏表示的图像去噪模型在去除噪声的同时更好地保留了图像的结构信息,获得了更好的主客观评价指标,去噪的峰值信噪比提高1 dB以上。     

基于BM3D去噪算法在天文图像中的应用

针对平方公里阵列射电望远镜(SKA)观测到的射电天文图像在各种因素的影响下存在不同程度的噪声问题,本文提出将3维块匹配滤波算法应用在SKA图像去噪上,结合空域思想和转换变换的方法来提高图像质量。实验结果表明,经过BM3D算法去噪图像的峰值信噪比平均达41.180 9 dB,去噪效果明显;去噪后的天文图像的结构相似度相比非局部均值算法提升了15.25%,比中值滤波提高了近一倍;图像特征相似度均在0.9以上,其细节特征保留更好,视觉效果更加明显。     

激光主动成像制导图像去噪算法研究

快速有效地对所获图像进行去噪是提高激光主动成像制导精度的关键一步。针对成像中的散斑噪声,提出了一种改进的小波阈值与基于积分图像的非局部均值滤波相结合的去噪算法。首先对激光主动成像图像进行噪声分析;然后通过对数变换将乘性噪声转换为加性噪声;而后将含噪图像进行两层小波分解,在第一层高频部分运用改进的小波阈值法,在第二层高频部分运用基于积分图像的非局部均值滤波算法进行去噪;最后进行相应的逆变换得到去噪图像。理论分析和实验结果证明,该算法能有效去除噪声,较好地保证了图像细节,并且满足激光主动成像制导对图像去噪实时性的要求。     

一种新的结合非下采样Contourlet与自适应全变差的图像去噪方法

该文提出了一种新的结合非下采样Contourlet变换(NSCT)和自适应全变差模型的图像去噪方法。首先通过NSCT对含噪图像进行分解,根据高斯比例混合(GSM)模型建立图像模型;然后利用贝叶斯估计进行图像去噪,重构后得到初次去噪图像;最后,结合自适应全变差模型对初次去噪图像进行重构滤波,得到最终的去噪图像。实验结果表明,该方法可以有效地消除图像中的Gibbs伪影及噪声,在去噪图像峰值信噪比(PSNR)和边缘保持性能上都优于已有的算法。     

基于聚类的图像稀疏去噪方法

在图像去噪方法的研究中,非局部均值算法与稀疏去噪算法是近几年受到广为关注的方法.非局部均值算法将具有邻域相似性的像素点作加权平均;而稀疏去噪算法是将图像的非噪声部分用过完备字典进行稀疏表示.基于上述两种方法的思想,本文提出了基于聚类的稀疏去噪方法,该方法结合了非局部均值算法与稀疏去噪算法的优点,对相似的图像块进行聚类,并通过施加l1/l2范数的正则化约束,对同一类中的图像块在过完备字典上进行相同结构的稀疏表示,从而达到去噪目的.在字典的选择上,本文使用DCT字典和双正交小波字典,能够同时保留原图像中的平滑分量与细节分量.实验结果表明,本文方法比传统的稀疏去噪方法有更好的去噪效果.     

维纳滤波和非线性扩散相结合的图像去噪

提出一种基于小波和非线性扩散的新的图像去噪算法。小波域局部维纳滤波是一种简单有效的去噪方法,利用该方法先对原始图像进行初步去噪,以此引导非线性扩散模型中的边缘检测函数,再用非线性扩散进行去噪。实验表明:该算法不仅很好地保存了图像的边缘信息,而且有效地去除了图像中的大部分噪声,无论是视觉效果还是客观标准上都优于单纯的小波域维纳滤波或非线性扩散去噪。     

基于非局部均值滤波的小波红外图像去噪

为避免小波去噪时阈值的缺陷和非局部均值滤波去噪时计算的复杂性和更有效地去除红外图像中的噪声,提出了一种采用非局部均值滤波的小波图像去噪方法.对含噪图像进行多层小波分解,采用新的贝叶斯估计阈值对高频系数进行阈值化处理,以消除高频噪声;在部分低层子带上进行非局部均值处理以进一步消除噪声.实验结果表明,与通常的小波阈值去噪和非局部均值去噪相比,该方法能很好地去除红外图像中的噪声,获得更高的信噪比(Signal To Noise Ratio,SNR)和更小的均方误差(MeanSquared Error,MSE),而且该方法计算相对简单,能达到很好的视觉效果.     

基于多尺度卷积神经网络的图像去噪方法研究

应用传统方法对图像去噪处理后,图像的峰值信噪比仍旧比较低,文章提出了基于多尺度卷积神经网络的图像去噪方法。以多尺度卷积神经网络为架构,由去噪模块与边缘模块组建成多尺度卷积神经网络去噪模型,利用残差学习法对模型进行训练,并利用寻优迭代算法对代价函数进行求解,利用训练好的去噪模型对图像进行多尺度卷积计算,根据噪声真值对图像平滑处理,实现图像去噪。通过实验证明,本次设计方法去噪后图像噪声有了明显降低,峰值信噪比高于传统方法。     

基于低秩分解和改进的非局部平均的SAR图像相干斑抑制

为抑制合成孔径雷达（SAR）图像成像过程中形成的相干斑噪声,提出了一种基于低秩分解和改进的非局部平均的SAR图像相干斑去噪方法。首先将SAR图像进行对数处理,将乘性噪声转换为加性噪声;然后利用低秩稀疏分解将对数图像分解成低秩图像部分和稀疏图像部分;接着对含噪严重的稀疏图像部分分析其结构张量,生成非局部平均滤波所需的衰减因子,进行改进的非局部平均滤波去噪;最后再做图像合成,经指数变换得到去噪后的SAR图像。实验结果表明,该方法经视觉评价、边缘保持指数（EPI）和等效视数（ENL）等方面评测,具有较好的抑制噪声和保持边缘及纹理细节的能力。      

基于非局部双边随机投影低秩逼近图像去噪算法

该文提出一种基于非局部双边随机投影的低秩逼近图像去噪新方法。首先,对每个图像块通过非局部搜索寻找相似匹配块簇,然后对相似匹配块簇进行双边随机投影,用投影后的低秩结构恢复原图像。实验结果表明,所提方法比奇异值分解方法有较低的计算复杂度,比单边随机投影方法有较小的重构误差。特别是和3维块匹配方法相比,所提方法能保持相近的信噪比和较好的视觉质量。     

Image denoising based on non-local means filter and its method noise thresholding

Non-local means filter uses all the possible self-predictions and self-similarities the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity. As the pixels are highly correlated and the noise is typically independently and identically distributed, averaging of these pixels results in noise suppression thereby yielding a pixel that is similar to its original value. The non-local means filter removes the noise and cleans the edges without losing too many fine structure and details. But as the noise increases, the performance of non-local means filter deteriorates and the denoised image suffers from blurring and loss of image details. This is because the similar local patches used to find the pixel weights contains noisy pixels. In this paper, the blend of non-local means filter and its method noise thresholding using wavelets is proposed for better image denoising. The performance of the proposed method is compared with wavelet thresholding, bilateral filter, non-local means filter and multi-resolution bilateral filter. It is found that performance of proposed method is superior to wavelet thresholding, bilateral filter and non-local means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index.     

基于非局部均值的彩色图像去噪

传统的彩色图像去噪算法通常是分层处理的,而忽略了彩色图像RGB通道之间的相关性,因此基于RGB通道联合相似度估计提出了一种新的彩色图像非局部均值去噪方法。在用非局部均值滤波对彩色图像进行去噪时,首先以目标像素为中心确定其支撑区域,然后根据多通道联合相似度估计确定权重,最后采用逐块滤波的方法对每一层进行滤波。并且针对彩色图像中含有的高斯噪声提出了一种新的噪声参数估计方法。由实验结果可以看出该算法比传统的去噪算法在PSNR和FSIM方面都有提高。因此可以看出在图像去噪过程中考虑三通道之间的相关性是必要的,同时也证明了算法的有效性。     

Denoising by Averaging Reconstructed Images: Application to Magnetic Resonance Images

A novel denoising approach is proposed that is based on averaging reconstructed images. The approach first divides the spectrum of the image to be denoised into different parts. From every such partial spectrum is then reconstructed an image using a 2-D singularity function analysis model. By expressing each of the reconstructed images as the sum of the same noise-free image and a different smaller noise, the denoising is achieved through averaging the reconstructed images. The theoretical formulation and experimental results on both simulated and real images consistently demonstrated that the proposed approach can efficiently denoise while maintaining high image quality, and presents significant advantages over conventional denoising methods.     

基于双边滤波与非局部均值的图像去噪研究

提出了一种基于双边滤波与非局部均值的图像去 噪算法,近年来提出的非局部均值算法是去噪效果非常 出色的算法之一,双边滤波去噪算法采用空间邻近度和灰度相似性构造新的权重系数,其取 得了良好的去噪效果,本文 据此改进非局部均值算法的权重部分,把空间邻近度因子与非局部均值的权重系数相结合, 构造新的权重系数。实验表 明,本文改进权重的非局部均值算法与已有的去噪方法相比,能得到更好的峰值信噪比,能 更好的保护图像细节以及结构信息。     

Image denoising via local and nonlocal circulant similarity

A patch based image denoising method is developed in this paper by introducing a new type of image self-similarity. This self-similarity is obtained by cyclic shift, which is called “circulant similarity”. Given a corrupted image patch, it can be estimated by incorporating circulant similarity into a weighted averaging filter. By choosing an appropriate kernel as weight function, the patch filter is implemented by circular convolution, and can be efficiently solved using fast Fourier transform. In addition, the circulant similarity can be enhanced by using nonlocal modeling. We stack the similar image patches into 3D groups, and propose a denoising scheme based on group estimation across the patches. Numerical experiments demonstrate that the proposed method with local circulant similarity outperforms much its local filtering based counterparts, and the proposed method with nonlocal circulant similarity shows very competitive performance with state-of-the-art denoising method, especially on images corrupted by strong noise.     

基于非局部的图像去噪方法的发展现状综述

通过对图像的相似块做处理从而达到去噪目的,是当前较为新颖的去噪方法,在对国内外利用相似块去噪文献进行理解和分析的基础上,回顾相似块去噪研究的发展,阐明相似块去噪的原理,并总结了相似块去噪的各种方法。最终基于对相似块去噪方法的分析,提出了对其研究方法的展望。     

A novel denoising method for infrared image based on bilateral filtering and non-local means

This paper presents an image denoising method based on bilateral filtering and non-local means. The non-local region texture or structure of the image has the characteristics of repetition, which can be used to effectively preserve the edge and detail of the image. And compared with classical methods, bilateral filtering method has a better performance in denosing for the reason that the weight includes the geometric closeness factor and the intensity similarity factor. We combine the geometric closeness factor with the weight of non-local means, and construct a new weight. Experimental results show that the modified algorithm can achieve better performance. And it can protect the image detail and structure information better. This work has been supported by the Student’s Platform for Innovation and Entrepreneurship Training Program (No.201510060022). E-mail:lflian@tjut.edu.cn      

基于同步噪声选择非局部平均除噪声的最优参数

提出基于同步噪声和信噪比（SNR）自动选择非局部平均（NCM）的核函数参数的算法。为了正确估计平滑图像中的剩余噪声,一幅纯噪声图像作为同步噪声与观测图像同步进行计算,同步噪声作为图像剩余噪声的估计值。为了保持同步噪声与剩余噪声的统计特性相似,提出了一种新的NCM算子,依据图像的特征对同步噪声进行平滑,能够保持两种噪声有...     

Gamma norm minimization based image denoising algorithm

Focusing on the issue of rather poor denoising performance of the traditional kernel norm minimization based method caused by the biased approximation of kernel norm to rank function,based on the low-rank theory,a gamma norm minimization based image denoising algorithm was developed.The noisy image was firstly divided into some overlapping patches via the proposed algorithm,and then several non-local image patches most similar to the current image patch were sought adaptively based on the structural similarity index to form the similar image patch matrix.Subsequently,the non-convex gamma norm could be exploited to obtain unbiased approximation of the matrix rank function such that the low-rank denoising model could be constructed.Finally,the obtained low-rank denoising optimization issue could be tackled on the basis of singular value decomposition,and therefore the denoised image patches could be re-constructed as a denoised image.Simulation results demonstrate that,compared to the existing state-of-the-art PID,NLM,BM3D,NNM,WNNM,DnCNN and FFDNet algorithms,the developed method can eliminate Gaussian noise more considerably and retrieve the original image details rather precisely.