异型纤维图像去噪分析

为了去除异型纤维图像中的噪声,首先分析了异型纤维图像中的噪声模型,然后针对噪声模型提出了一种能同时去除异型纤维图像中高斯和脉冲混合噪声的去噪算法.该算法在全变差(Total Variation,TV)算法的基础上进行了算法改进,综合了中值滤波的优点,在达到去噪目的的同时,较好地处理了去除噪声、保留边缘细节信息这对在图像去噪中存在的矛盾.同时,对参数的选取也做了分析,较好地平衡了去噪效果和处理效率问题.数值对比实验中的视觉效果和客观标准均表明了该去噪算法的有效性。     

Image Deblurring in the Presence of Impulsive Noise

Consider the problem of image deblurring in the presence of impulsive noise. Standard image deconvolution methods rely on the Gaussian noise model and do not perform well with impulsive noise. The main challenge is to deblur the image, recover its discontinuities and at the same time remove the impulse noise. Median-based approaches are inadequate, because at high noise levels they induce nonlinear distortion that hampers the deblurring process. Distinguishing outliers from edge elements is difficult in current gradient-based edge-preserving restoration methods. The suggested approach integrates and extends the robust statistics, line process (half quadratic) and anisotropic diffusion points of view. We present a unified variational approach to image deblurring and impulse noise removal. The objective functional consists of a fidelity term and a regularizer. Data fidelity is quantified using the robust modified L ¹ norm, and elements from the Mumford-Shah functional are used for regularization. We show that the Mumford-Shah regularizer can be viewed as an extended line process. It reflects spatial organization properties of the image edges, that do not appear in the common line process or anisotropic diffusion. This allows to distinguish outliers from edges and leads to superior experimental results.     

Seismic noise attenuation by means of an anisotropic non-linear diffusion filter

Noise attenuation is a major seismic data processing concern. In seismic data, noise can appear as random, coherent and/or impulsive. Recently, many different techniques, ranging from relatively simple processes to extremely complex ones, have been used for noise attenuation. Image filtering techniques are relatively new methods in seismic exploration. We introduced the anisotropic non-linear diffusion filter which is an effective way to de-noise images. Since a seismic section can be considered as an image of a two-variable function, we implemented the anisotropic non-linear diffusion filter to reduce both random and Gaussian noises. This filter is shown to be effective in removing noise while preserving edges and hence reducing resolution loss in seismic data. The anisotropic non-linear diffusion filter, with Tukey's function to guide the diffusivity, was applied to synthetic and real seismic data. The results show a signal-to-noise ratio increase with reflector continuity in addition to better recovery of reflector amplitudes even when dealing with complex subsurface geological structures.     

Analyzing a biorthogonal information channel by the Walsh-Hadamard transform

This paper reports the development of optimal processors to be used on a biorthogonal information channel for both Gaussian noise and impulsive noise. The biorthogonal system makes use of the Walsh-Hadamard transform (WHT). An efficient method to filter noise from a system which has unused capacity is developed. For Gaussian noise, three filters (linear, hard-limiting and sign detection) were used before processing. Hard-limiting is best for a large signal-to-noise ratio because it gives the lowest probability of error. When impulsive noise is added to the Gaussian noise, two optimal filters are designed, one from the standpoint of a whitening filter and the other from hypothesis testing to see if the noise is Gaussian or impulsive.     

Decision-based fuzzy image restoration for noise reduction based on evidence theory

A novel decision-based fuzzy averaging (DFA) filter consisting of a D–S (Dempster–Shafer) noise detector and a two-pass noise filtering mechanism is presented in this paper. The proposed filter can effectively deal with impulsive noise, and a mix of Gaussian and impulsive noise. Bodies of evidence are extracted, and the basic belief assignment is developed using the simple support function, which avoids the counter-intuitive problem of Dempster’s combination rule. The combination belief value is the decision rule for the D–S noise detector. A fuzzy averaging method, where the weights are constructed using a predefined fuzzy set, is developed to achieve noise cancellation. A simple second-pass filter is employed to improve the final filtering performance. Experimental results confirm the effectiveness of the new DFA filter both in suppressing impulsive noise as well as a mix Gaussian and impulsive noise and in improving perceived image quality.     

图像高斯噪声估计的极大似然算法

提出一种图像高斯噪声极大似然估计方法,目的是估计出噪声图像所含噪声大小。首先,根据高斯噪声模型的特点,用极大似然法估计噪声值,对图像所含噪声模型进行分析。其次,把噪声图像用直方图表示,从归一化直方图中选出不同的样本观测值,用极大似然算法对噪声的方差进行估计。最后,用MATLAB对该方法进行了模拟实验,实验结果表明此方法所得的图像噪声的方差与实际图像噪声的方差近似相等。所以,此方法无论是在准确性上还是在可行性上均具有优良的特性。     

脉冲噪声中基于广义恒模算法的多用户检测

在高斯白噪声信道中提出的各种多用户检测方法在脉冲噪声存在的情况下出现了明显的质量下降,而实际的物理信道中又经常伴随着脉冲噪声。将α-稳定分布噪声作为背景噪声模拟实际物理信道中存在的脉冲噪声,根据脉冲噪声的特殊性质,在恒模多用户检测算法的基础上将广义恒模算法应用到了多用户检测技术中去。仿真结果表明该算法在高斯白噪声以及α-稳定分布的脉冲噪声中均取得的良好的性能,具有很好的鲁棒性。     

基于中值的自适应均值滤波算法

中值滤波和均值滤波通常被分别用来处理脉冲噪声和高斯噪声.但是当图像同时存在高斯噪声和脉冲噪声时,单独用那种滤波方法都不会达到最好的去噪效果.针对该问题,提出了一种基于中值的改进自适应加权均值滤波算法.该算法采用了一种基于自适应阈值的方法对滤波系数加以优化,使其可以更有效地减少噪声,又可以较好的保持图像的边缘细节信息.实验结果表明,该算法能较好的滤除混合噪声,且滤波效果优于传统的滤波算法.     

A Filter To Remove Gaussian Noise by Clustering the Gray Scale

An algorithm to suppress Gaussian noise is presented, based on clustering (grouping) gray levels. The histogram of a window sliding across the image is divided into clusters, and the algorithm outputs the mean level of the group containing the central pixel of the window. This filter restores well the majority of noisy pixels, leaving only few of them very deviated, that can be finally restored with a common filter for impulsive noise, such as a median filter. In this paper the clustering filter CF is described, analysed and compared with other similar filters.     

脉冲噪声环境下基于卡尔曼滤波的语音增强

语音增强的目标在于从含噪信号中提取纯净语音,纯净语音在某些环境下会被脉冲噪声所污染,但脉冲噪声的时域分布特征却给语音增强带来困难,使传统方法在脉冲噪声环境下难以取得满意效果。为在平稳脉冲噪声环境下进行语音增强,提出了一种新方法。该方法通过计算确定脉冲噪声样本的能量与含噪信号样本的能量之比最大的频段,利用该频段能量分布情况逐帧判别语音信号是否被脉冲噪声所污染。进一步地,该方法只在被脉冲噪声污染的帧应用卡尔曼滤波算法去噪,并改进了传统算法执行时的自回归(AR)模型参数估计过程。实验中,采用白色脉冲噪声以及有色脉冲噪声污染语音信号,并对低输入信噪比的信号进行语音增强,结果表明所提出的算法能显著地改善信噪比和抑制脉冲噪声。     

Impulsive noise suppression in the case of frequency estimation by exploring signal sparsity

The frequency estimation problem is addressed in this work in the presence of impulsive noise. Two typical scenarios are considered; that is, the received data are assumed to be uniformly sampled, i.e., without data missing for the first case and data are randomly missed for the second case. The main objective of this work is to explore the signal sparsity in the frequency domain to perform frequency estimation under the impulsive noise. Therefore, to that end, a DFT-like matrix is created in which the frequency sparsity is provided. The missing measurements are modeled by a sparse representation as well, where missing samples are set to be zeros. Based on this model, the missing pattern represented by a vector is indeed sparse since it only contains zeros and ones. The impulsive noise is remodeled as a superposition of a unknown sparse vector and a Gaussian vector because of the impulsive nature of noise. By utilizing the sparse property of the vector, the impulsive noise can be treated as a unknown parameter and hence it can be canceled efficiently. By exploring the sparsity obtained, therefore, a joint estimation method is devised under optimization framework. It renders one to simultaneously estimate the frequency, noise, and the missing pattern. Numerical studies and an application to speech denoising indicate that the joint estimation method always offers precise and consistent performance when compared to the non-joint estimation approach.     

Robust edge detection

Edge detection is an important issue in computer vision and image understanding systems. Most conventional techniques have assumed Gaussian noise, and their performance could decrease with the departure of noise distribution from normality. In this paper, we present an edge detection approach using robust statistics. The edge structure is first detected by a robust one-way design model, and then localized by a robust contrast test. Finally, hysteresis thresholding is applied to yield the output edge map. To evaluate its performance, experiments were carried out on synthetic and real images corrupted with both Gaussian noise and a mixture of Gaussian and impulsive noise. The results show that the performance of the proposed edge detector is stable and reliable under severe impulsive noise conditions.     

基于统计假设测试的噪声方差估计方法

在数字图像处理中,噪声方差估计是一个重要的研究课题。提出一种针对加性高斯噪声的噪声方差估计方法。利用一种基于统计假设测试的方法来度量图像结构特征度,基于图像结构特征度找出平滑子块和非平滑子块（含有边缘或纹理子块）;以平滑子块中的最小方差为参考方差,选择出方差与参考方差相差在一定范围内且不含边缘的所有子块;从选出的子块中求以图像结构特征度为权重的方差平均值作为噪声方差估计值。相比于现有的噪声估计方法,该方法具有非常高的估计精度,适合感染高斯噪声的各种图像。     

Binormalized data-reusing adaptive filtering algorithm for active control of impulsive sources

The main objective of active noise control (ANC) is to provide attenuation for the environmental acoustic noise. The adaptive algorithms for ANC systems work well to attenuate the Gaussian noise; however, their performance may degrade for non-Gaussian impulsive noise sources. Recently, we have proposed variants of the most famous ANC algorithm, the filtered-x least mean square (FxLMS) algorithm, where an improved performance has been realized by thresholding the input data or by efficiently normalizing the step-size. In this paper, we propose a modified binormalized data-reusing (BNDR)-based adaptive algorithm for impulsive ANC. The proposed algorithm is derived by minimizing a modified cost function, and is based on reusing the past and present samples of data. The main contribution of the paper is to develop a practical DR-type adaptive algorithm, which incorporates an efficiently normalized step-size, and is well suited for ANC of impulsive noise sources. The computer simulations are carried out to demonstrate the effectiveness of the proposed algorithm. It is shown that an improved performance has been realized with a reasonable increase in the computational complexity.     

Improved bilateral filter for suppressing mixed noise in color images

It is a challenging problem to suppress mixed noise in color images. The traditional bilateral filter can excellently reduce additive noise without destroying image edges and details, but it fails to remove impulsive noise. This paper presents an improved bilateral filtering method, which can simultaneously suppress both impulsive and additive noise. The proposed solution first introduces a new weighting function to the bilateral filtering mechanism, which is experimentally more effective than the traditional Gaussian kernel. Then, either the current pixel or the vector median, instead of always the current pixel itself, is chosen as the base to take part in the bilateral filtering action, which is determined by whether the current pixel is a possible impulse or not. The experimental results show that the proposed solution can simultaneously remove impulsive and additive noise while preserving edge structures, and outperforms other vector filtering methods in terms of both objective evaluation and subjectively visual quality.     

基于二阶导数算子与小波变换的图像去噪

二阶导数算子噪声定位的图像去噪法对椒盐噪声有很强的去噪能力,但对高斯噪声去噪效果较差,基于小波变换的图像去噪法能有效去除高斯噪声,但几乎不能去除椒盐噪声。针对上述问题,采用二阶导数算子降噪与小波变换去噪相结合的方法对图像去噪,利用2种方法进行优势互补,能较好地去除椒盐、高斯噪声和椒盐-高斯混合噪声,降低选择阈值的难度,有利于提高图像去噪精度。实验结果表明,该算法是有效可行的。     

The Nonlinear Prefiltering and Difference of Estimates Approaches to Edge Detection: Applications of Stack Filters

The theory of stack filtering, which is a generalization of median filtering, is used in two different approaches to the detection of intensity edges in noisy images. The first approach is a generalization of median prefiltering: a stack filter or another median-type filter is used to smooth an image before a standard gradient estimator is applied. These prefiltering schemes retain the robustness of the median prefilter, but allow resolution of finer detail. The second approach, called the Difference of Estimates (DoE) approach, is a new formulation of a morphological scheme [Lee et al., IEEE Trans. Robotics Automat. RA-3, Apr. 1987, 142-156, Maragos and Ziff, IEEE Trans. Pattern Anal. Mach. Intell. 12(5), May 1990.] which has proven to be very sensitive to impulsive noise. In this approach, stack filters are applied to a noisy image to obtain local estimates of the dilated and eroded versions of the noise-free image. Thresholding the difference between these two estimates yields the edge map. We find, for example, that this approach yields results comparable to those obtained with the Canny operator for images with additive Gaussian noise, but works much better when the noise is impulsive. In both approaches, the stack filters employed are trained to be optimal on images and noise that are "typical" examples of the target image. The robustness of stack filters leads to good performance for the target image, even when the statistics of the noise and/or image vary from those used in training. This is verified with extensive simulations.     

Variational Bayesian inference based robust multiple measurement sparse signal recovery

This work concentrates on not only probing into a novel Bayesian probabilistic model to formulate a general type of robust multiple measurement vectors sparse signal recovery problem with impulsive noise, but also developing an improved variational Bayesian method to recover the original joint row sparse signals. In the design of the model, two three-level hierarchical Bayesian estimation procedures are designed to characterize impulsive noise and joint row sparse source signals by means of Gaussian scale mixtures and multivariate generalized t distribution. Those hidden variables, included in signal and measurement models are estimated based on a variational Bayesian framework, in which multiple kinds of probability distributions are adopted to express their features. In the design of the algorithm, the proposed algorithm is a full Bayesian inference approach related to variational Bayesian estimation. It is robust to impulsive noise, since the posterior distribution estimation can be effectively approached through estimating unknown parameters. Extensive simulation results show that the proposed algorithm significantly outperforms the compared robust sparse signal recovery approaches under different kinds of impulsive noises.     

一种新的线性混合滤波器

提出一种新的滤波器,称为线性混合滤波器（简称为LMF）,它适用于恢复被一类混合噪音（即一致脉冲噪音与高斯噪音）污染的数字图像。当混合噪音强度在一定范围内变化时,它具有自动调节机制。与若干已知的同类滤波器相比,LMF的速度更快,具有简单而统一的控制参数计算公式,而不是关于参数的选取范围。而且从实验结果可见,用LMF时峰值信噪比得到提高,均方误差得到降低。     

基于可变步长PCNN赋时矩阵高斯噪声滤波

提出了一种用脉冲耦合神经网络(pulse-coupled neural network,PCNN)赋时矩阵定位噪声、分类滤波并能自适应调整灰度补偿步长的高斯噪声滤波方法。使用PCNN求得含噪图像的赋时矩阵,再在滤波窗口中根据目标像素与周围像素的点火时刻关系确定噪声点,对噪声点分4类滤波:增加、减少可变灰度步长,维纳滤波和中值滤波,最后对图像维纳滤波平滑小噪声点。实验结果表明,该算法能有效去除噪声且较好保持图像的边缘细节,与同类高斯滤波方法进行峰值信噪比、信噪比改善因子比较,验证了算法的有效性。