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双语推荐：中值滤波

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中值滤波方法是图像去除噪声的重要方法之一,广泛应用于数字图像处理中。本文为了更好地检验滤波方法的效果,在图像中添加随机和椒盐两种常见噪声,对多级中值滤波、开关中值滤波和极值中值滤波算法进行仿真,以峰值信噪比和归一化均方差(NMSE)作为客观评价标准,对几种典型中值滤波算法进行了对比分析。结果表明,中值滤波既可以去除图像中的噪声又能保护图像的边缘和轮廓。
Median filtering method is one of the important digital image de-noising methods, which is widely used in digital image processing. In order to better test the effectiveness of filtering method, the paper adds the ran-dom noise and the Salt and Pepper noise in the image to simulate multi-level median filtering, switching median filtering and extremum median filtering, and compares and analyzes their processing results by using PSNR and NMSE as the evaluation criteria. The test results show that median filtering algorithms can not only remove the im-age noise but also protect its edge and contour.

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针对传统自适应中值滤波算法的不足,文中提出了一种改进的自适应中值滤波方法,以有效的去除图像中的高密度脉冲噪声。第一,对于噪声点的检测,首先利用极大值和极小值的数量差找出可疑的噪声点,再利用邻域像素的相似性判断可疑点是否为噪声点。第二,对于滤波中值的计算,先把滤波窗口内具有相同灰度值的极值点压缩到一个,然后再计算中值。实验结果表明,该算法的滤波效果优于传统自适应中值滤波,且具有较好的稳定性。
To overcome the drawbacks of the traditional adaptive median filter, an improved adaptive medi-an filter is proposed to effectively remove the high density impulse noise of the image in this paper. Firstly, detect noise points. Different point values between the maximum and minimum points are used to identify suspicious noises. Then, the similarity of neighborhood pixels is used to judge these suspicious points whether they are noise points or not. Secondly, calculate the filtering median. Extreme points that have the same gray value in the filter window are compressed to only one. Finally, calculate the median values. The experimental result shows that this algorithm is better than traditional adaptive median filter to remove the noise and has a good stability.

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主要针对图像的高斯噪声和椒盐噪声的去噪算法进行研究，分别使用到中值滤波、均值滤波和维纳滤波三种滤波算法。实验结果表明中值滤波对于椒盐噪声有更好的去噪效果；维纳滤波对高斯噪声有明显的作用，相比中值滤波和均值滤波，维纳滤波对高斯噪声有很好的抑制效果，与此同时，维纳滤波却容易丢失边缘信息且对椒盐噪声几乎没有去噪作用。
Analyzes the denoising algorithm based on salt, pepper noise and gaussian noise. These algorithms have median filtering, average filter and Wiener filter. The experimental results show that the median filtering has better effect for salt and pepper noise. Compared with medi-an filtering and average filter, the Wiener filter has the better effect for the Gaussian noising. However, the Wiener filter algorithm is easy to loss of edge information and almost invalidly to salt and pepper noise.

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针对红外图像中常出现的颗粒噪声，在对自适应中值滤波（AMF）改进的基础上，提出了一种基于噪声检测的加权融合中值滤波算法。该算法对于噪声的滤除分为检测和滤波2个环节。首先根据对大量图像信息分布特征进行分析，设计出了较为通用的4类噪声检测模版；其次分别采用各模版对噪声进行极值检测并加以标记，以区分出极值点与像素点；然后根据模版内极值点与非极值点的数目比例确定噪声点，并将非极值点取均值作为噪声滤波结果输出；对于区域的极值点，弓l入距离判别准则加以确认，被确认为噪声点的像素值通过模版内非极值点分别取均值和中值并加权融合得到；最后对4个模版处理后的滤波值进行等权融合，得到最终的滤波图像。分别与经典中值滤波（MF）、自适应中值滤波（AMF）、开关中值滤波（SMF）性能比较，实验表明，本文滤波算法对于高强度的斑点噪声具有较好的滤波性能，对于红外图像处理具有一定的借鉴价值。
A new improved median filtering algorithm is presented so as to remove the salt&pepper noise in infrared image effectively on the basis of the adaptive median filter(AMF). The filtering algorithm is divided into noise detection and noise filter. Firstly, according to the analysis of the characteristics of the distribution of the digital image information, four types of noise detection template are designed. Then, the new templates are used respectively to detect the extreme values and label them in the image, so as to distinguish between the extreme value pixels and non-extreme value pixels. Thirdly, the noise pixels are determined according to the ratio between extreme value pixels and non-extreme value pixels. The average value of the non-extreme value pixels is calculated and regarded as the filtering results. The distance criterion is introduced to deal with the extreme value point in large area in the template, and the value of the noise pixels is replaced by the weighted fusion v

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运用Kalman滤波对目标位置进行跟踪时,测量数据中的野值是影响滤波效果的重要因素之一。分析了动态测量数据中野值产生的原因和野值对Kalman滤波性能的影响机理,利用新息的统计特性,提出了野值判别准则和一种抗野值Kalman滤波算法。仿真结果表明,该方法使野值处理与状态估计同步进行,可有效抑制野值对Kalman滤波的影响,提高滤波精确度。
When Kalman filter is applied to target tracking, the outliers in dynamic measurement data are one of the important factors which affect filtering performance. The reasons and characteristics of outliers emerging in the dynamic measurement data are analyzed, as well as the mechanism that outliers affect Kalman filter. Based on the statistical characteristics of innovation, the method of dynamically identifying outliers and anti-outlier Kalman filter algorithm are presented. The simulation results demonstrate that the method can eliminate outliers and estimate states simultaneously, and it can effectively eliminate the negative effect of the outlier on the filter, and improve the filter accuracy.

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针对传统中值滤波算法不能很好地保护图像细节以及受严重噪声污染时性能急剧下降的情况，提出了一种新型的自适应模糊中值滤波算法。通过比较滤波窗口内像素点的灰度值与像素点灰度值的均值定义了模糊滤波系数，利用此模糊滤波系数对滤波方法进行加权，得到一种加权中值滤波器。通过对小窗口内的灰度值不等于最大灰度值和最小灰度值的像素点的检测自适应调整窗口大小，对超过设定的最大窗口的情况，噪声点的灰度值用四个相邻的已处理的像素点灰度值的均值进行替换。仿真结果表明，新算法具有较好的细节保护能力和较强的去除噪声能力。
A new adaptive fuzzy median filtering algorithm is proposed for the problem that traditional median filtering algorithm can’t protect the image detail very well, and that its performance will be in the sharp decline in dealing with high-density noise image. By comparing the gray value of the pixels and the mean gray value of the pixels in the filtering window, coefficient of the fuzzy filter is defined. By using this coefficient as the weight function, a new weighted median filter approach is proposed. The size of the window is adjusted automatically by detecting the presence of pixels that have gray value between the maximum gray value and the minimum gray value of the filtering window. The gray value of the pixels in the center of window which exceeds the maximum window is replaced with the mean of gray value of neighbor-ing four pixels. Simulation results demonstrate the new algorithm has better image detail preservation and strong denois-ing ability.

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针对自适应中值滤波算法的缺陷---对高密度椒盐噪声图像滤波后留下黑色斑块，提出了一种分阶段中值滤波算法。该算法对图像执行两次小窗口的滤波操作，相较于采用较大窗口的滤波，其在有效去除噪声的同时降低了结果图像的模糊程度。先对所有噪声点进行一次中值滤波消除了盐粒噪声，再用窗口内非噪声点的灰度中值代替胡椒噪声点的灰度值以去除黑色斑块。最后的仿真实验结果表明，本文算法既有像自适应中值算法一样滤除低密度椒盐噪声的良好性能，又有对高密度椒盐噪声图像的降噪能力。
A grading median filtering algorithm was proposed to offset the defect of adaptive median filter ( AMF) that it left some black plaque after filtering images corrupted by high density salt and pepper noises. Through twice filter to the noise image with small size win-dow,compared with bigger ones, it reduced the blur degree of result image. For the first time,it eliminated the salt noise by using median filter ( MF ) to noise pixels, and then wiped off the black plaque by replacing pepper noise pixels with the median of the noise free pixels in its 8-neighborhood. Lastly, the simulation result shows, our algorithm either has the good capability to filter the low density noises as well as AMF or has the ability to filter higher density salt and pepper noises in image.

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文中研究的目的是通过滤噪来改善图像质量,便于医生分析CT图像和实现计算机辅助诊断对医学图像的预处理。采用均值滤波、中值滤波、自适应滤波算法对医学图像进行噪声处理。不仅提供了算法的程序代码,还通过Matlab平台进行了模拟实验。实验结果表明,均值滤波算法的滤噪能力和保护图像细节能力不及中值滤波算法,自适应中值滤波算法处理效果优于其他两种滤波算法。结论是文中所研究的几种滤波算法在医学图像上滤噪是可行且有效的。
The purpose of this study is to improve image quality to filter the noise and convenient for doctors to analyze CT images and realize the pretreatment of image about computer aided diagnosis. Use average filter, median filter, adaptive median filter algorithm to process the noise of medical image. Not only provide the algorithm’ s program code but also make the simulated experiment by Matlab. Experimental results show that the average filtering algorithm’ s ability of filtering noise and protecting the image detail is less than the median filtering algorithm,adaptive median filtering algorithm processing is better than the other two filtering algorithms. The conclusion is that these noise filtering algorithms are feasible and effective in medical image.

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为了有效地滤除红外图像中的噪声，提出了一种小波域多方向自适应加权伪中值滤波算法。该算法首先对红外噪声图像的各高频分解子图像分别进行噪声点检测和标记；然后根据各子图像中像素点分布特征分别设计出4类具有多方向性的滤波模板进行自适应加权滤波；最后将低频分解子图像与滤波后的各小波高频分解子图像进行重构。分别将中值滤波（MF）、伪中值滤波（PMF）、极值中值滤波（EMF）、加权中值滤波（WMF）、以及本文算法应用于标准测试图像以及红外图像去噪，并引入峰值信噪比（PSNR）、平均绝对误差（MAE）进行去噪效果评定。标准测试图像和红外图像仿真结果表明，该算法性能明显优于PMF，且相对于与其余几类同类型算法而言，也具有一定的优势。
In order to filter the noise in infrared image, a multi-direction adaptive weighted pseudo median filtering algorithm is proposed based on wavelet transform. Firstly, the salt & pepper noise image is conducted by wavelet transform, and the high-frequency sub-images and low-frequency sub-image are obtained. The noise distribution areas of the high-frequency sub-images are detected and labeled effectively. Then, according to the characteristics of the ground objects and the features of the directionality of the high frequency wavelet decomposition sub-images, four kinds of directional filtering templates are respectively designed so as to deal with the noise through adaptive weighted filtering. Finally, low-frequency sub-image and high-frequency sub-images are reconstructed. The median filtering(MF), pseudo median filtering(PMF), extreme median filtering(EMF), weighted median filtering(WMF) and the algorithm in this paper are used to filter the salt & pepper noise in standard t

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图像去噪是图像处理邻域的重要课题，该文在3维直方图的基础上定义图像的3维轴距，分析无噪声及椒盐噪声影响下图像3维轴距的分布特点，提出基于3维轴距的去噪算法。算法首先对图像进行边沿扩展，再利用3维轴距来检测噪声，最后用非噪声中值滤波来消除噪声。仿真比较了3维轴距去噪算法与中值滤波、自适应开关中值、快速自适应均值滤波、修正方向加权中值滤波和自适应模糊开关中值滤波算法的去噪性能，结果证明该文算法的有效性。
Image denoising is an important issue in the field of image processing. This paper defines the three-dimensional axis distance on the basis of three-dimensional histogram, analyzes the distribution features of image’s three-dimensional axis distance under the influence of non-noise and salt-pepper noise situation, thus the denoising algorithm upon the three-dimensional axis distance is proposed. The proposed algorithm extends image edge at first and then detects noise by employing the three-dimensional axis distance. Finally, the noise is eliminated by excluding noise pixels’ median filter. The results of comparisonsamong the proposed algorithm, median filter, adaptive switching median filter, efficient average filter, modified directional-weighted-median filter, and noise adaptive fuzzy switching median filter verify the effectiveness of the proposed algorithm.

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