去除图像中高斯-脉冲噪声的有效方法

数字图像在获取和传输过程中常常会受到噪声的污染,有时会同时受到高斯噪声和脉冲噪声的污染。然而现有的去噪算法大多针对单一的高斯噪声或脉冲噪声,在处理混合噪声时无法取得令人满意的去噪效果。给出了一种去除数字图像中高斯-脉冲混合噪声的有效方法,去噪过程分为两个步骤：首先采用一种称为边界判定噪声检测的脉冲噪声检测方法检测出混合噪声中的脉冲噪声,对噪声图像作中值滤波后得到一幅受高斯噪声污染的过渡图像。然后用贝叶斯最小平方-高斯尺度混合模型对过渡图像进行滤波得到降噪后的图像。实验表明,同现有的其他去噪方法相比,该方法能够更有效地去除混合噪声。     

一种彩色图像混合滤波的方法

提出了一种彩色图像混合滤波算法,对混有高斯和脉冲噪声的图像进行去噪处理。算法首先滤除受脉冲噪声污染的像素点,采用基于个数判断脉冲噪声的中值滤波算法;其次滤除受高斯噪声污染的像素点,采用对称近邻均值滤波算法。实验结果表明,与传统的中值滤波和均值滤波算法相比,该算法能够有效地去除高斯和脉冲噪声,同时能够保留更多的图像细节信息。     

一种彩色图像混合滤波的方法

提出了一种彩色图像混合滤波算法,对混有高斯和脉冲噪声的图像进行去噪处理。算法首先滤除受脉冲噪声污染的像素点,采用基于个数判断脉冲噪声的中值滤波算法;其次滤除受高斯噪声污染的像素点,采用对称近邻均值滤波算法。实验结果表明,与传统的中值滤波和均值滤波算法相比,该算法能够有效地去除高斯和脉冲噪声,同时能够保留更多的图像细节信息。     

图像混合噪声的一种组合滤波消除方法

提出了一种基于高斯拉普拉斯边缘检测的含高斯噪声和脉冲噪声的图像组合滤波去噪方法,即首先对含有混合噪声的图像进行中值滤波,再用高斯拉普拉斯边缘检测方法检测出图像的边缘,得到边缘图像;然后利用自适应Wiener滤波对中值滤波后得到的图像进一步滤波去噪,最后将边缘图像嵌入经Wiener滤波得到的平滑图像中。此种方法不但能够有效去除含高斯噪声和脉冲噪声的图像中的噪声,而且可以保持图像的边缘信息,提高了图像的去噪效果和清晰度。     

基于循环检测的气动退化图像混合域去噪算法

气动退化图像中存在先验信息未知以及噪声成分复杂不易进行滤波处理的问题,为此,提出一种气动退化图像混合域去噪算法。设计针对高斯噪声、泊松噪声、固定值脉冲噪声和随机值脉冲噪声的混合检测方法,使用改进的滤波方法在空域去除固定值脉冲噪声和随机值脉冲噪声,进行非下采样轮廓波变换多层分解,在变换域使用阈值方法去除高斯噪声和泊松噪声。进行噪声循环检测,设定迭代停止条件控制算法循环从而实现算法自适应。仿真实验结果表明,该算法的噪声检测性能和滤波性能较好,图像细节信息得到正确恢复。同时,算法复杂度较低,实时性较好,可满足气动退化图像去噪处理的需要。     

基于脉冲耦合神经网络的声纳图像噪声抑制

为解决声纳图像中存在的高斯与脉冲噪声的同时去除问题,在简化的脉冲耦合神经网络(Pulse Coupled Neural Net-work,PCNN)模型的基础上,提出了一种结合了数学形态学与中值滤波的噪声抑制算法.首先利用PCNN输出二值图像确定噪声在图像中的位置,用数学形态学方法保持目标的完整性,然后利用中值滤波方法去除图像的脉冲噪声并对PCNN逐次迭代的输出结果进行调整去除高斯噪声.实验结果证明,方法在保持图像边缘信息的前提下,与其他方法相比获得了更好的去噪效果.     

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。     

基于小波变换和中值滤波的Canny边缘检测算法

传统的Canny边缘检测算法采用的是高斯平滑,用来去除图像中的计算噪声,这种去噪方法虽然对抑制高斯噪声效果较好,但对脉冲噪声等的去除并不理想。针对这一问题,提出了用小波变换与中值滤波相结合的方法取代了传统的高斯滤波法,并对平滑后的图像作图像增强。实验表明,该方法有效地提高了边缘检测的准确性,得到了比较理想的边缘检测效果。     

去除红外图像中脉冲噪声的双边加权中值滤波

针对现有方法在去除红外图像的脉冲噪声时,未能有效保持图像的边缘细节和纹理结构,提出了基于众数检测的双边加权中值滤波算法。算法根据脉冲噪声的取值和分布特征,用最小和最大像素值以及众数原则进行噪声检测;对检测出来的噪声像素,以迭代的方式、自适应地用双边加权系数对邻域中的无噪像素和已经去噪处理的像素进行频次加权,然后取它们的中值作为当前噪声像素的估计值。其中双边加权系数自适应于距离邻近度与灰度相似度。实验结果表明,相对于部分现有方法,本文方法去噪所得的EPI和SSIM值更高,去噪图像的视觉效果更佳。     

基于加权检测的脉冲噪声新滤波器

在分析噪声检测与噪声滤波原理的基础上,提出了用于恢复被脉冲噪声污染的图像的去噪算法。该算法基于方向差异性将检测窗口分解为四个子窗口,并取子窗口的中间像素与相邻像素的灰度值之差的加权平均值与预先定义的阈值进行比较,较准确地区分噪声点和信号点;然后根据方向相关依赖性,采用一种边缘保持滤波方法来重构被噪声污染像素的灰度值。实验结果证明,该算法在提高图像信噪比的同时,可以更好地保持图像的细节信息。     

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.     

一种改进的自适应中值滤波方法

提出了一种改进的自适应中值滤波算法,以有效地去除图像中的脉冲噪声,并保留图像细节。在进行噪声点检测时,引入了最小集合距离测度,有效地避免了将高频细节信号误判为噪声。采用最小无污染点集合的中值恢复噪声点,消除了其邻域噪声点的影响。通过与RAMF、NASMF等方法的比较实验表明,新算法噪声检测的正确率高、降噪与保留细节效果好, 尤其对含噪声密度高的图像的处理效果优势更为明显。     

一种脉冲噪声图像复原算法

针对被脉冲噪声污染的观测图像提出了一种基于自相似灰度校正的自适应图像复原算法．该算法首先用有选择的中值滤波器对脉冲噪声进行抑制，然后利用图像的自相似性对像素值进行校正以克服中值滤波器造成的图像局部区域像素相关性增加对图像复原处理产生的不利影响．用规整化方法进行图像复原处理，并且使用在灰度校正过程中得到的竞争因子对图像局部区域的统计量进行加权来产生规整化参数，并使其在迭代过程中自适应的更新．实验结果表明，该算法获得的复原图像具有良好的客观评价指标和主观视觉效果．     

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.     

Restoration of images corrupted by mixed Gaussian-impulse noise via l1-l0 minimization

In this paper, we study the restoration of images corrupted by Gaussian plus impulse noise, and propose a l₁-l₀ minimization approach where the l₁ term is used for impulse denoising and the l₀ term is used for a sparse representation over certain unknown dictionary of images patches. The main algorithm contains three phases. The first phase is to identify the outlier candidates which are likely to be corrupted by impulse noise. The second phase is to recover the image via dictionary learning on the free-outlier pixels. Finally, an alternating minimization algorithm is employed to solve the proposed minimization energy function, leading to an enhanced restoration based on the recovered image in the second phase. Experimental results are reported to compare the existing methods and demonstrate that the proposed method is better than the other methods.     

一种基于多尺度噪声检测的图像中值滤波器

介绍了标准中值滤波与有效中值滤波的概念,提出了一种基于自适应多尺度噪声检测的中值滤波器,可用于恢复被椒盐脉冲噪声污染了的图像。滤波器将输入图像像素分为有效信号类、脉冲噪声类和恒定区域类,对各类像素采用不同的方法进行滤波处理。实验结果证明,本文算法的性能比现存的其它许多算法有了显著的提高,而且便于实现。     

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。     

Intelligent noise detection and filtering using neuro-fuzzy system

In this paper, we propose a neuro-fuzzy based blind image restoration to remove impulse noise from low as well as highly corrupted images. Main components of the proposed technique include noise detection, histogram estimation and noise filtering process. Proposed technique constructs the fuzzy sets using fuzzy number construction algorithm. These fuzzy sets are used in noise filtering process to remove impulse noise from the noisy pixels using neuro-fuzzy inference system and fuzzy decider. Experimental results are based on global and local error measures, which prove that the proposed technique gives superior results than the present well known impulse noise filtering methods.