一种新型的自适应模糊中值滤波算法

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。     

去除椒盐噪声的自适应开关加权均值滤波

提出一种去除椒盐噪声的自适应开关加权均值滤波算法。该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点,对检测出的噪声点采用加权均值滤波进行处理,而信号点保持其灰度值不变直接输出。实验结果表明,该算法能在有效去除椒盐噪声的同时保护图像细节,较传统中值滤波及其改算法有更好的滤波性能。     

基于噪声检测的激光光斑图像去噪方法

基于受污染图像的噪声检测,提出了一种有效的椒盐噪声图像混合滤波算法。首先利用可自适应变化的矢量窗口检测噪声,并对检测到的噪声进行分类,然后采用所提出的伪加权中值滤波和伪加权均值滤波两种算法对图像进行混合滤波,最后加入背景阈值和孤立噪点修正量对滤波后的图像进行灰度修正。提出的方法对不同椒盐噪声强度下的激光光斑图像均体现出优异的滤波性能,去噪和边缘保持性能得到了较大提高,优于传统的中值滤波、均值滤波及其一些改进算法。     

粗糙集在图像边缘增强滤波中的应用

为使图像噪声滤除的同时,边缘细节信息能得到有效地保护,基于粗糙集理论介绍了一种新的边缘增强滤波方法。首先,基于粗糙集不可分辨等价关系划分,分离出噪声点和非噪声点,对噪声点通过中值滤波进行滤除,然后,通过粗近似精度和方向模板检测边缘的连续性和方向,以具有最大粗近似精度的模板的灰度均值取代中心像素点灰度。在所有进行边缘检测的滤波算法中,该算法是唯一边缘测度在多次迭代运行后不会减小的滤波方法,同时通过对不同噪声程度的椒盐噪声和高斯噪声的滤波实验,说明该方法在有效滤除噪声同时能使边缘细节得到保护和增强,且比其它传统的空域和频域滤波方法具有更好的噪声适应性。     

椒盐图像的方向加权均值滤波算法

椒盐噪声是造成图像污染的主要因素之一,椒盐去噪是图像去噪领域的研究热点。方向加权中值滤波算法计算噪声点滤波输出时存在一定的问题,比如,未排除近邻噪声点的干扰,对方向的估计不准确,对局部灰度特性刻画不完整等。为此,提出一种方向加权均值滤波算法。此算法先根据方向灰度差异和灰度极值判断检测噪声点,然后根据对局部窗口噪声强度的估计自适应地选择递归或非递归滤波窗口的加权灰度均值作为滤波输出。仿真实验结果表明,提出的算法与现有的两种方向加权中值滤波算法相比,PSNR普遍提高了2~3dB和5~6dB,噪声密度高时提高的幅度更加明显;速度提高了接近10倍和30倍。     

一种新的图像椒盐噪声的非线性滤波算法

为了在滤除椒盐噪声的同时能很好地保持图像的边缘细节,提出了一种新颖的图像椒盐噪声非线性滤波算法.利用局部统计信息,先将图像像素点分为信号点和可能的噪声点两类.然后将可能的噪声点进一步细分为边缘点、噪声点和信号点:利用方向信息、均方差来判断是否为边缘点,利用自适应阈值的方法来判断是否为噪声点,并且对边缘点和噪声点采取不同的方法进行滤波.经过仿真实验并与其它滤波算法进行比较表明,文中的算法具有更好的效果.     

基于决策分析的高椒盐噪声轮胎痕迹图像滤波方法

决策分析能准确判断出噪声像素与信号像素,均值滤波能较好平滑噪声,而自适应中值滤波能较好地保持原始图像的细节及边缘。为了恢复被高密度椒盐噪声污染的轮胎痕迹图像,提出三者相结合的新算法。该算法结合三者的优点,与传统中值滤波器、自适应中值滤波器等非线性滤波器相比,能得到更好的图像质量。实验表明,算法能有效消除灰度轮胎痕迹图像中的高密度椒盐噪声和彩色轮胎痕迹图像中的中低密度椒盐噪声,较好地保护了图像的细节及边缘信息。     

基于极值的椒盐噪声滤波改进算法

为了在有效滤除椒盐噪声的同时更好地保护图像细节,提出一种基于极值的椒盐噪声滤波改进算法.算法首先进行噪声检测,将灰度值为0和255附近的像素点,且不构成5像素或以上直线的点作为噪声点,其余点作为信号点;然后进行噪声滤波,为了保护图像中的边缘、细节或细线,信号点不做任何处理,而对噪声点使用梯度法进行处理.Matlab仿真实验结果表明,新算法不仅能有效滤除椒盐噪声,在保护细节方面也取得了优于传统中值滤波算法的效果.     

一种新的图像椒盐噪声的非线性滤波算法

为了在滤除椒盐噪声的同时能很好地保持图像的边缘细节,提出了一种新颖的图像椒盐噪声非线性滤波算法。利用局部统计信息,先将图像像素点分为信号点和可能的噪声点两类。然后将可能的噪声点进一步细分为边缘点、噪声点和信号点：利用方向信息、均方差来判断是否为边缘点,利用自适应阈值的方法来判断是否为噪声点,并且对边缘点和噪声点采取不同的方法进行滤波。经过仿真实验并与其它滤波算法进行比较表明,文中的算法具有更好的效果。     

基于模糊推理的方向中值椒盐滤波算法

针对灰度图像中的椒盐噪声,提出了一种基于模糊逻辑推理的方向中值滤波算法.该算法先利用椒盐噪声的特点,将图像像素点分为信号像素点和噪声像素点,然后利用模糊推理在4个方向上推理出最接近理想值的非噪声点代替当前噪声点.同时算法中采用一种简便的方法检测出噪声点所处的滑动窗口中的边和线.仿真结果表明,该算法能在有效抑制噪声的同时较好地保存图像的细节信息.     

Image enhancement using deep-learning fully connected neural network mean filter

Improving the quality of a noisy image is important for image applications. Many novel schemes pay great efforts in the removal of impulse noise. Most of them restore noisy pixels only by using the neighboring noise-free pixels, but the relationship between a noisy image and its noise-free one, which denotes the clean image not corrupted by noise, is ignored. So the reconstruction quality cannot be further improved. In this study, we employ a deep-learning fully connected neural network (FCNN) to select top N candidates of neighboring un-corrupted pixels for the restoration of a center noisy pixel in an analysis window. Hence, the mean value of the gray levels of these top N pixels is computed and employed to replace the noisy pixel, yielding the noisy pixel being restored. The experimental results reveal that the proposed deep-learning FCNN mean filter can remove impulse noise effectively in corrupted images with different noise densities.

     

椒盐噪声图像的非局部平均滤波算法

针对非局部平均(NLM)方法对椒盐噪声图像滤波效果较差的问题,通过引入噪声检测结果扩展NLM方法去除图像中椒盐噪声。在噪声检测阶段,利用图像的两个极值Lmin和Lmax把图像像素点分为非噪声点和噪声点。在滤波阶段,非噪声点的灰度值保持不变。对于噪声点,如果以该噪声点为中心的自适应滤波窗口内均为噪声点,则认为该噪声点位于图像自身灰度值为Lmin或Lmax的区域内,使用两个极值的统计结果进行恢复。否则,采用改进的NLM方法滤除噪声。构造联合噪声检测模板避免噪声点对相似权计算的干扰,噪声点的恢复值由非噪声点的灰度值加权平均得到。此外,采用迭代滤波策略对高密度噪声图像噪声点进行恢复。相关去噪实验结果证实了算法去噪的有效性,不足之处是算法的时间复杂度较高。     

Context model based edge preservation filter for impulse noise removal

In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods.     

基于图像受噪程度的改进模糊加权均值滤波

根据高斯噪声密度大、噪声强度的波动范围宽,其污染图像不仅每一个像素灰度级都会受影响,而且即使是同一灰度级受污染的程度也会不同的特点和传统的图像模糊滤波算法在图像细节保护方面上的不足,提出基于图像受噪程度的改进模糊加权均值滤波算法,该算法根据图像各像素点的受噪程度,得到首次滤波图像和原图像估计直方图,根据该直方图确定模糊隶属度函数,然后对首次滤波图像中灰度小于25的像素点进行模糊加权均值滤波,该算法在不需要期望图像和高斯噪声方差的情况下能有效地去除噪声,同时能够很好地保护图像细节信息。     

结合修剪均值与高斯加权中值滤波的图像去噪算法

为快速准确地滤除图像中的脉冲噪声并较好地保持图像的纹理细节和边缘结构,提出一种基于修剪均值与高斯加权中值滤波的图像去噪算法。根据脉冲噪声的灰度特征与统计特征,以局部统计方式进行噪声检测,将灰度取最小值或最大值且与邻域像素相关性较小的像素识别为噪声像素。对于图像平滑区域和细节区域中的噪声像素,使用自适应修剪均值和高斯加权中值滤波算法进行去噪处理。实验结果表明,该算法在视觉效果、峰值信噪比、结构相似性及计算速度上均优于对比算法,并且能够在彻底滤除噪声的同时,较好地保持图像的纹理细节和边缘结构。     

Iterative deviation filter for fixed-valued impulse noise removal

An iterative deviation filter for fixed-valued impulse noise removal is proposed, with the aim to overcome the defects of existing filters, and further improve the denoising performance. In the proposed filter, a noise detection method based on the extreme intensity values and the deviation of neighbor pixels is proposed, i.e., the pixels with the extreme intensity and differ greatly from the mean of neighbor pixels, are identified as noises. A noise removal method based on the minimum deviation of neighbor pixels is proposed, i.e., the intensity of one neighbor noise free pixel, which is closest to the mean of neighbor noise free pixels, is used as estimated intensity of noisy pixel under consideration. Furthermore, the noise removal strategy performs iteratively and takes full advantage of the previous denoising results. Simulation results show that the proposed method has better denoising performance than the existing distinguished filters in terms of visual representation, peak signal to noise ratio and structural similarity index.

     

一种改进的非局部均值去噪算法*

针对非局部均值去噪算法在图像块相似度计算方面存在的不足,提出计入图像旋转对相似度贡献的、效果更好的图像块匹配算法.为了获得与给定像素点邻域相似的图像子块,首先对给定像素点周边的相关邻域子块按灰度值大小排序,计算其与同样按灰度值大小排序的给定像素点邻域子块之间的距离,据此筛选出灰度分布相似的图像子块作为候选集,更进一步在候选集中选出结构上更为相似的图像子块.同时为了克服噪声影响,在计算子块相似度之前对输入图像进行预滤波处理.实验表明,与原始的非局部均值去噪算法相比,文中算法在峰值信噪比、平均结构相似性及主观视觉效果等方面均具有一定优势,特别是在噪声较大时,文中算法的去噪效果更好.     

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.     

A new cluster based adaptive fuzzy switching median filter for impulse noise removal

A new clustering technique based on most allied directional neighbors is proposed to suppress low and high-density impulse noise from digital images. Most allied neighbors exhibit a vital role in estimation as well restoration of appropriate gray level value of corrupted pixels. In first phase, most allied directional neighbors, i.e., pixels directly attached to central pixel and the directional pixels (horizontal, vertical and two diagonal directions) next to attached pixels in the processing window are partitioned into two equal size clusters based on gradient values. Cluster with a minimum sum of gradient values (most similar neighbors) and the one with relatively large gradient values are passed to fuzzy inference system to infer the current pixel to be noisy-free, edge or a noisy. In second phase, a switching technique opts one of the three options depending upon fuzzy membership degrees and local information to restore the corrupted pixel value. A non-parametric approach based on local information for dynamic threshold setting using fuzzy logic makes the proposed filter computationally effective and adaptive to process a large number of images without user-defined parameters. The proposed algorithm is simple to implement and simulation results based on well know quantitative measures indicate the supremacy of the proposed filter for random-valued impulse noise as well as salt and peppers noise.