结合NSST和快速非局部均值滤波的刀具图像去噪

为消除基于图像处理的刀具磨损检测中的图像噪声,提出了结合非下采样Shearlet变换(Non-subsampled Shearlet Transform, NSST)和快速非局部均值(Fast Non-local Means, FNLM)滤波的图像去噪方法。首先,利用基于决策的非对称剪切中值(Decision Based Un-symmetric Trimmed Median, DBUTM)方法滤除图像中的椒盐噪声;然后,对图像进行NSST多尺度分解,得到一个低频子带和一系列高频子带;最后,分别使用FNLM滤波和各向异性扩散模型调整低频和高频子带系数,并由调整后的各子带系数重构出噪声滤除后的图像。实验结果表明,与基于小波的阈值收缩方法、基于Contourlet的全变差模型结合各向异性扩散方法、基于NSST和标准非局部均值滤波方法相比,本文方法在主观视觉去噪效果、峰值信噪比、结构相似度以及处理速度等4个方面性能更优。      

Smoothing low-SNR molecular images via anisotropic median-diffusion

We propose a new anisotropic diffusion filter for denoising low-signal-to-noise molecular images. This filter, which incorporates a median filter into the diffusion steps, is called an anisotropic median-diffusion filter. This hybrid filter achieved much better noise suppression with minimum edge blurring compared with the original anisotropic diffusion filter when it was tested on an image created based on a molecular image model. The universal quality index, proposed in this paper to measure the effectiveness of denoising, suggests that the anisotropic median-diffusion filter can retain adherence to the original image intensities and contrasts better than other filters. In addition, the performance of the filter is less sensitive to the selection of the image gradient threshold during diffusion, thus making automatic image denoising easier than with the original anisotropic diffusion filter. The anisotropic median-diffusion filter also achieved good denoising results on a piecewise-smooth natural image and real Raman molecular images.     

Forward-and-backward diffusion processes for adaptive image enhancement and denoising

Signal and image enhancement is considered in the context of a new type of diffusion process that simultaneously enhances, sharpens, and denoises images. The nonlinear diffusion coefficient is locally adjusted according to image features such as edges, textures, and moments. As such, it can switch the diffusion process from a forward to a backward (inverse) mode according to a given set of criteria. This results in a forward-and-backward (FAB) adaptive diffusion process that enhances features while locally denoising smoother segments of the signal or image. The proposed method, using the FAB process, is applied in a super-resolution scheme. The FAB method is further generalized for color processing via the Beltrami flow, by adaptively modifying the structure tensor that controls the nonlinear diffusion process. The proposed structure tensor is neither positive definite nor negative, and switches between these states according to image features. This results in a forward-and-backward diffusion flow where different regions of the image are either forward or backward diffused according to the local geometry within a neighborhood.     

基于改进的扩散平滑和RBM的高光谱图像分类

为了改善传统分类方法在高光谱遥感图像去噪和特征提取方面的不足,提出了一种基于改进的扩散平滑算法和RBM的方法.该方法使用自适应扩散系数,对相应的区域进行不同程度的扩散平滑,实现了对高光谱遥感图像的快速去噪;然后利用多层限制玻尔兹曼机构建DBN网络,实现对高光谱遥感图像的分类.实验表明,与传统的分类方法和DBN相比,该方法在高光谱图像地物分类精度上有所改善.     

一类非线性信号去噪的奇异值分解有效迭代方法

对于一类非线性信号的去噪问题,该文提出一种基于奇异值分解(Singular Value Decomposition, SVD)的有效迭代方法。对现有奇异值差分谱方法在两类不同非线性信号上的去噪效果进行了对比,指出在信号不具有明显特征频率、非周期性变化时这一方法并不适用,并分析了现象产生的原因;然后针对该类信号的特点重新定义了Hankel矩阵结构,给出有效奇异值的确定方式,并通过SVD多次迭代过程实现对该类信号的有效去噪。对实际飞行数据去噪的实验结果表明,该方法对提出的一类信号对象不仅去噪效果良好,而且可提高运算效率。     

Hybrid Fourier-wavelet image denoising

Transform-domain image denoising methods assume that the original signal can be sparsely represented in the transform domain, but none of the orthogonal transforms can achieve sparse representation for all images. Proposed is a hybrid Fourier-wavelet denoising method to overcome this shortcoming. Experimental results show that the proposed algorithm improves denoising performance efficiently.     

基于图形小波变换的遥感图像表示与去噪

离散小波变换(Discrete Wavelet Transform,DWT)通常用于图像的表示。然而,对于具有不规则形状边缘的图像,尤其是对于纹理和细节信息较多的遥感图像,DWT却很难有效表示,进而影响后续去噪效果。针对该问题,提出了一种基于图形小波变换(Graphic Wavelet Transform,GWT)的图像去噪方法。首先,将图像表示为图形信号,并通过该图形信号的谱表示构造相应的变换矩阵;然后,设计了一种改进自适应阈值的图像去噪方法,在GWT变换域内对图像去噪。实验结果表明,与常用的图像去噪方法相比,所提算法能够提供更好的图像主观质量。采用均方根误差(Root Mean Square Error,RMSE)和峰值信噪比(Peak Signal-to-Noise Ratio,PSNR)作为客观指标,结果表明,采用所提方法得到的重建图像客观质量更优。     

Spectral-spatial adaptive and well-balanced flow-based anisotropic diffusion for multispectral image denoising

Anisotropic diffusion can provide better compromise between noise reduction and edge preservation. In multispectral images, there exist different spatial local structures in the same band. Therefore, the levels of smoothing of anisotropic diffusion process should conform to both of image spectral and spatial features. In this paper, we present an effective denoising algorithm by integrating the spectral-spatial adaptive mechanism into a well-balanced flow (WBF) based anisotropic diffusion model, in which an adjustable weighted function is introduced to perform the appropriate levels of smoothing and enhancing according to different feature scales. Moreover, we make the fidelity term in the model to be adaptive by replacing the original noisy signal with the last evolution of the smoothed image. Consequently, the proposed algorithm can better control the diffusion behavior than traditional multispectral diffusion-based algorithms. The experimental results verify that our algorithm can improve visual quality of the image and obtain better quality indices.     

基于AOS非线性扩散的SAR图像去噪研究

针对合成孔径雷达(SAR)图像的斑点噪声,介绍了一种基于非线性各向异性扩散的去噪方法,该方法经过加性算子分离(AOS)方案离散可以保证其扩散迭代过程中滤波结果的绝对稳定,并且利用其在消除噪声的同时锐化边界的特点,将之引入SAR图像的斑点噪声抑制问题当中.通过对一幅SAR图像的滤波处理,以及若干衡量滤波算法效果的评价指标,将其与传统的自适应局域统计滤波方法进行分析比较,并得出相关结论,从而证实了该研究提出的AOS非线性扩散滤波法(AOS ND法)抑制SAR图像相干斑的可行性和有效性.     

基于小波前向后向扩散的红外图像降噪与边缘增强算法

针对红外图像特点,该文提出了一种基于小波前向后向扩散的红外图像降噪与边缘增强算法。小波前向后向扩散是建立在小波扩散理论的基础上,其继承了小波扩散迭代降噪与边缘保持特性,在此基础上实现了图像的边缘增强。为了克服传统小波扩散基于小波模值的边缘映射的不足,该文利用小波模值与局部奇异性测度的联合概率分布对边缘映射进行初步估计,结合几何约束进行修正,获得准确的边缘映射,并重新设计了小波前向后向扩散系数方程。实验证明算法能有效实现红外图像降噪的同时增强图像边缘。     

一种改进的各向异性扩散模型

各项异性扩散方程是一种经典的图像去噪方法,但该方法在去除噪声的过程中会造成一定程度的模糊边缘。对此文中提出了一种基于改进的各向异性扩散方程的图像去噪方法,通过在其能量泛函的目标函数中添加残差项,使能量泛函的极小解更加接近原始的函数,可取得比其更好的去噪效果。文中方法可看作是各项异性扩散方程和全变差模型的结合。实验表明,新提出的方程相对经典的方程有较好的边界处理效果和更高的信噪比。     

基于向量相似度的LPG-PCA 图像去噪算法

基于主成分分析的去噪算法在进行局部像素分组时,由于噪声具有不确定性和随机性,以欧氏距离 直接作为图像块相似性这一判断标准容易使得结果产生偏差。针对此问题,文中提出了一种基于向量相似度的 LPG-PCA 图像去噪算法,将向量相似度和欧氏距离相结合作为相似图像块的判断标准,优化了相似图像块的选取。 此外,在相似图像块样本数的选取方面采用自适应的数量选取方法,使得样本数的选取更加合理,进一步提高了图 像的去噪质量。实验结果表明所提算法在峰值信噪比和结构相似性方面均优于传统的LPG-PCA 图像去噪算法,且 对亚毫米波成像也具有一定的去噪效果。     

激光主动成像图像噪声抑制方法

针对激光主动成像图像特点及实际应用需要,提出了一种基于同态滤波与双数复值小波变换级联的图像降噪算法。首先通过同态滤波将乘性散斑噪声变换为加性噪声;然后用基于改进Q-shift滤波器的双树复值小波对含噪图像进行分解,通过Bayes自适应阈值法修正小波系数;最后再进行相应的逆变换得到去噪图像。该算法具有近似平移不变性、多方向选择性及精确重构性,采用信噪比（SNR）、峰值信噪比（PSNR）和运行时间作为算法去噪性能的评价标准进行实验。实验结果表明该算法能够有效抑制图像中的散斑噪声,计算效率高,且很好地保护了图像细节。     

基于正交小波变换的图像去噪算法研究

提出了一种基于正交小波变换的图像去噪方法,首先利用离散小波对图像信号按Mallat算法进行分解,然后采用软闽值与小波重构的算法进行去噪。深入研究了小波变换中的图像分解与重构的Mallat算法,详细介绍了正交小波变换中阈值的选取,并进行了实验研究。实验结果表明,该方法可以有效去除噪声,并保留了图像细节部分的有用信息。     

Wavelet domain denoising method based on multistage median filtering

There are two main problems in the threshold denoising method based on wavelet transform. One is the difficulty of threshold selection, and the other is the inconsistence of the dip and curved events in the low signal-to-noise ratio (SNR) seismic data after denoising. In image denoising, multistage median filtering can preserve the details of the signal. So we proposed a denoising algorithm in wavelet transform domain based on multistage median filtering. Using this method the flat region and the edge region are differentiated by the difference between the maximum mid-value and the minimum mid-value, which preserves the details, thus improves the denoising effect. The simulation data and the real data processing results reveal that this method has stronger ability in separating signal from noise than that of the threshold denoising method.     

基于全变分理论的红外图像去噪

为了去除红外图像中的噪声,提出了一种基于全变分理论的去噪算法。该方法继承了经典全变分模型在去除噪声中保护边缘的优点,结合图像平滑扩散原理,得到了一个全新的扩散函数;同时引入了一个边缘检测算子,对正则项和忠诚项的相关参量进行了改进,使得修复后的图像大大避免了阶梯效应;最后对该算法的实现进行了推导。结果表明,该算法能够有效地去除噪声,并且避免了阶梯效应的产生。     

Image denoising with anisotropic bivariate shrinkage

This paper presents a novel image denoising algorithm based on the modeling of wavelet coefficients with an anisotropic bivariate Laplacian distribution function. The anisotropic bivariate Laplacian model not only captures the child-parent dependency between wavelet coefficients, but also fits the anisotropic property of the variances of wavelet coefficients in different scales of natural images. With this statistical model, we derive a closed-form anisotropic bivariate shrinkage function in the framework of Bayesian denoising and a new image denoising approach with local marginal variance estimation based on this newly derived shrinkage function is proposed in the discrete wavelet transform (DWT) domain. The proposed anisotropic bivariate shrinkage approach is also extended to the dual-tree complex wavelet transform (DT-CWT) domain to further improve the performance of image denoising. To take full advantage of DT-CWT, a more accurate noise variance estimator is proposed and the way the anisotropic bivariate shrinkage function applied to the magnitudes of DT-CWT coefficients is presented. Experiments were carried out in both the DWT and the DT-CWT domain to validate the effectiveness of the proposed method. Using a representative set of standard test images corrupted by additive white Gaussian noise, the simulation results show that the proposed method provides promising results and is competitive with the best wavelet-based denoising results reported in the literature both in terms of peak signal-to-noise ratio (PSNR) and in visual quality.     

基于平稳小波变换的图像去噪方法

针对传统正交小波变换在图像去噪时存在的边缘失真,提出了一种基于平稳小波变换的图像去噪方法。使用系数关联法将图像小波分解后的高频分量像素标记为噪声和边缘,如果小波系数被标记为边缘,则保持其系数不变,否则采用基于邻域的方法进行系数收缩。当噪声方差较大时,收缩后最小尺度的高频分量中会存在一些孤立的亮点或暗点,借助次大尺度高频分量将其去除,对处理后的小波系数进行平稳小波反变换得到去噪图像。实验结果表明,本文方法能够在去除噪声的同时较好地保持图像的边缘,是一种有效的图像去噪方法。     

基于平稳多小波变换的红外图像噪声抑制方法

提出了一种平稳多小波变换方法,该方法结合多小波和平稳小波变换在信号去噪方面的优点,给出了二维图像平稳多小波变换的mallat分解重构算法,并对红外图像的平稳多小波变换系数进行阚值处理实现图像去噪,仿真结果表明,相对于平稳标量小波变换和多小波的噪声抑制方法,此方法对噪声有更好的抑制作用,并尽可能多的保持目标的特征和细节．     

基于图像局部方向特性的自适应全变分去噪模型

针对全变分模型(total variation,TV)以图像的梯度信息作为去噪的尺度参数,未考虑图像局部纹理的方向性的缺点,提出了一种基于图像局部方向特性的自适应全变分去噪模型(Adaptive directional total variation,ADTV),并推导出该模型的迭代数值求解过程。在该模型中,首先,计算出图像局部方向的角度矩阵。然后,构造与图像纹理方向一致的椭圆区域代替TV模型的圆形区域。最后,通过优化最小化算法迭代求解以获得去噪后图像。通过对比实验证明,本文提出的模型取得了更高的峰值信噪比,去噪过程中更好地增强了图像的细节信息。