Fast averaging peer group filter for the impulsive noise removal in color images

In the paper, a new approach to the impulsive noise removal in color images is presented. The new filtering design is based on the peer group concept, which determines the membership of a central pixel of the filtering window to its local neighborhood, in terms of the number of close pixels. Two pixels are declared as close if their distance in a given color space does not exceed a predefined threshold value. A pixel is treated as not corrupted by the impulsive noise process, if its peer group consists of at least two close pixels, otherwise this pixel is replaced by a weighted average of uncorrupted samples from the local neighborhood. The peer group size assigned to each pixel is used for the averaging operation, so that pixels which have many peers are taken with higher weight. The new filtering design proved to restore efficiently color images corrupted by even strong impulsive noise, while preserving tiny image details. The beneficial property of the proposed filter is its very low computational complexity, which allows its application in real-time image processing tasks.     

彩色图像迭代自适应滤波算法

对于被脉冲噪声污染的彩色图像,基于噪声检测,提出了一种迭代自适应滤波算法。对于彩色图像的每个通道,分别运用脉冲噪声检测器,估计出噪声像素点,应用后续迭代自适应滤波算法,只对每个通道中检测出来的噪声像素点进行滤波,而对非噪声像素点保持其值不变。实验结果证明,该滤波算法滤除脉冲噪声的能力优于传统的矢量滤波算法,并且更能有效地保留图像的边缘和细节。     

一种改进型的自适应基本矢量方向滤波器

基本矢量方向滤波器BVDF(Basicvectordirectionalfilter)是保护彩色图像色调的一种经典的矢量滤波器,主要用于消除彩色图像中的脉冲噪声和色调异常的噪声(vectorswithatypicaldirections),但它没有考虑在滤波器窗口内象素之间的空间距离对滤波效果的影响。根据人类视觉感知,在一个滤波器窗口中,即使二个象素具有相同的颜色值,其中离中心象素较近的象素应当对中心象素影响较大,而距离较远的象素对中心象素影响较小。基于此,论文试图将空间距离对滤波效果的影响量化,提出了一个基于心理距离的加权公式,并结合传统的基本矢量方向滤波器BVDF,构造出了相应的新的基于空间距离加权的自适应基本矢量方向滤波器。实验表明,新的自适应基本矢量方向滤波器,在抑制脉冲噪声、色调保持、细节或边缘保护方面,胜过传统的基本矢量方向滤波器BVDF。     

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。     

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.     

Some improvements for image filtering using peer group techniques

An image pixel peer group is defined as the set of its neighbor pixels which are similar to it according to an appropriate distance or similarity measure. This concept has been successfully used to devise algorithms for detection and suppression of impulsive noise in gray-scale and color images. In this paper, we present a novel peer group-based approach intended to improve the trade-off between computational efficiency and filtering quality of previous peer group-based methods. We improve the computational efficiency by using a modification of a recent approach that can only be applied when the distance or similarity measure used fulfills the so-called triangular inequality property. The improvement of the filtering quality is achieved by the inclusion of a refinement stage in the noise detection. The proposed method performs according to the following steps: First, we partition the image into disjoint blocks and we perform a fast classification of the pixels into three types: non-corrupted, non-diagnosed and corrupted; second, we refine the initial findings by analyzing the non-diagnosed pixels and finally every pixel is classified either as corrupted or non-corrupted. Then, only corrupted pixels are replaced so that uncorrupted image data is preserved. Experimental results suggest that the proposed method is able to outperform state-of-the-art methods both in filtering quality and computational efficiency.     

一种改进的各向异性扩散深度图像增强算法

深度图像受其测距原理所限,存在边缘不匹配、无效像素、噪声等问题,提出一种基于改进的各向异性扩散算法的深度图像增强方法。首先,校正深度图像和彩色图像的位置关系,并根据时间连续性选择多帧图像,进行多帧均值滤波预处理;其次,通过在彩色图像中引入权重的思想,构建具有4-邻域形式的深度图像模型,利用彩色图像引导的深度图像进行各向异性扩散,填补孔洞;最后,使用改进的自适应中值滤波平滑图像噪声。实验结果表明,该方法能够有效修复原始深度图像中存在的由无效像素组成的黑色孔洞,在抑制噪声的同时,仍能保持深度图像中物体边缘的细节信息。     

Smoothing of ultrasound images using a new selective average filter

Ultrasound images are strongly affected by speckle noise making visual and computational analysis of the structures more difficult. Usually, the interference caused by this kind of noise reduces the efficiency of extraction and interpretation of the structural features of interest. In order to overcome this problem, a new method of selective smoothing based on average filtering and the radiation intensity of the image pixels is proposed. The main idea of this new method is to identify the pixels belonging to the borders of the structures of interest in the image, and then apply a reduced smoothing to these pixels, whilst applying more intense smoothing to the remaining pixels. Experimental tests were conducted using synthetic ultrasound images with speckle noisy added and real ultrasound images from the female pelvic cavity. The new smoothing method is able to perform selective smoothing in the input images, enhancing the transitions between the different structures presented. The results achieved are promising, as the evaluation analysis performed shows that the developed method is more efficient in removing speckle noise from the ultrasound images compared to other current methods. This improvement is because it is able to adapt the filtering process according to the image contents, thus avoiding the loss of any relevant structural features in the input images.     

基于HSV彩色空间的矢量形态学算子

在HSV彩色空间中,现有的矢量形态学算子对彩色像素的排序依据V、S、H顺序分层进行,从而违背了彩色图像中三个分量的平等原则,导致矢量形态学滤波算子难以去除彩色图像中由色调和饱和度分量引起的噪声,因此滤波算子性能较差.文中提出了一种基于HSV三分量混合运算的矢量形态学排序规则,并根据该规则定义了新的矢量形态学腐蚀、膨胀算子以及常用的矢量形态学滤波算子.实验结果表明,新的矢量形态学滤波算子较现有的矢量形态学滤波算子具有更强的噪声抑制性能,在保证图像不增加新的彩色像素的同时,去除了噪声并保留了图像细节,滤波后的图像具有较高的峰值信噪比和较小的均方根误差.     

自适应滤波窗实现距离加权图像椒盐噪声滤除

目的 在比较几种椒盐去噪方法的滤波窗口尺寸选择策略的基础上,提出一种基于自适应滤波窗的距离加权图像椒盐噪声滤除方法。方法 首先将图像中灰度值为0或255的像素点判定为噪声点,接着对每个噪声点,在以该噪声点为中心、不断增大面积的滤波窗口序列中,寻找包含非噪声点的最小尺寸窗口。若此窗口尺寸小于预设的阈值,则使用该窗口中的非噪声点进行距离加权滤波。否则认为该噪声点位置位于图像自身灰度值为0或255的像素点区域内部,使用少数服从多数策略计算灰度恢复值。结果 将本文方法与其他7种椒盐去噪方法相比较。当图像自身包含较多灰度值为0或255的像素点时,本文方法去噪效果优于其他7种方法。当图像自身不含或较少包含灰度值为0或255的像素点时,本文方法与其他方法中的最优去噪结果效果相当。结论 本文方法不仅能够有效滤除椒盐噪声,而且适用于自身包含灰度值为0或255的像素点多的椒盐噪声图像。     

彩色图象去噪方法探讨

应用二值图象的腐蚀和膨胀运算原理,提出了一种基于象素点识别并利用领域结构对其进行着色的彩色图象“去噪”方法,数值实验结果令人满意,在实验部分,将该方法与单纯的彩色腐蚀滤波进行了对比和分析,证明这种方法更为有效,可行。     

基于四元数旋转的开关型矢量中值滤波器

矢量中值滤波器是一种经典和高效的矢量滤波器,主要用于消除彩色图像中的冲击噪声。然而VMF没有区分细线条和噪声的能力,它往往把细线条当成噪声而过滤掉。本文利用四元数旋转理论,模仿Laplacian算子,提出一种用于检测彩色图像中的冲击噪声的算法,并结合传统的VMF构造出一个新颖的开关型矢量中值滤波器。实验结果表明,新的滤波器不仅能有效地保护细线条和边界等细节信息,而且其滤波性能也明显胜过传统的VMF和一些经典的及最近开发的矢量滤波器。     

Image denoising feedback framework using split Bregman approach

In this paper, an image denoising feedback framework is proposed for both color and range images. The proposed method works on an error minimization principle using split Bregman method. At first image is denoised by computing means in the local neighborhood. The pixels that have big differences from the center of the local neighborhood compared to the noise variance are then extracted from the denoised image. There is a low correlation between the extracted pixels and their local neighborhood. This information is fed to the feedback function and denoising is performed again, iteratively, to minimize the error. In most cases, the proposed framework yields best results both qualitatively and quantitatively. It shows better denoising results than the bilateral filtering when the edge information in the input images is affected by intense noise. Moreover, during the denoising process feedback function ensures that the edges are not over smoothed. The proposed framework is applied to denoise both color and range images, which shows it works effectively on a wide variety of images unlike the evaluated state-of-the-art denoising methods.     

一种基于排序阈值的开关中值滤波方法

提出了一种基于排序阈值的开关中值滤波方法以克服图像滤波中去噪与细节保护的矛盾。该方法利用滤波窗口内像素点的排序信息，在极值中值滤波方法的基础上，将受脉冲噪声污染图像中的像素点进一步分为噪声点、边缘细节区和平坦区3种类型。通过对多种图像测试的统计结果，获得合适的分类器参数，然后利用类型判决，进行开关中值滤波，即对噪声点和平坦区进行中值滤波以得到良好的噪声滤除效果，而对边缘细节区不做处理以获得良好的细节保护效果。比较了标准中值滤波、极值中值滤波和本方法的结果。实验结果表明，本方法具有更好的效果。     

一种基于模糊决策的开关矢量中值滤波方法

针对现有的彩色图像脉冲噪声去除方法没有区分滑动窗口中的像素是否为噪声像素而导致滤波效果差的问题, 提出一种基于模糊决策的开关矢量中值滤波方法。该方法首先利用开关条件判断像素是否被污染, 针对被污染的像素, 通过模糊数学理论构造适合脉冲噪声去除的隶属函数; 然后计算滑动窗口内所有像素的模糊隶属度, 并根据置信区间去除疑似噪声像素以优化滑动窗口的取值空间; 最后对优化后的滑动窗口应用矢量中值滤波(VMF)以去除噪声像素。与现有方法相比, 新的方法去除了滑动窗口中心像素的邻域疑似噪声, 从而有效提升了滤波效果。实验验证了该方法的高鲁棒性和实用性。     

基于暗原色先验原理的水下图像增强

针对水下光衰减和散射导致的图像严重降质问题和用传统方法进行水下图像增强 产生色偏现象,提出一种新的水下图像增强方法。基于暗原色先验原理进行水下图像增强,用 软抠图的方法对图像暗通道进行细化;在图像前0.1%最亮的像素点中,用中值滤波算法计算出 这些像素点的中值,再计算这些像素点和与之对应的中值的差值,差值最小的像素点作为背景 光的预估值,并用该像素点所在区域颜色饱和度方差来判断预估背景光的准确性;利用Retinex 算法和图像各颜色通道的衰减系数比对增强后的图像进行颜色校正。实验表明,该方法能有效 地去除水下图像中的雾色、校正图像色偏问题,进而提高图像对比度。     

A whitening transformation for two-color blood cell images

An image enhancement technique is described for the preprocessing of stained white blood cell images which have been digitized through two different color filters from either end of the visible spectrum. Typically, corresponding picture elements (or pixels) from blood cell images digitized in this manner exhibit slight changes in grey-level due to the color filtering, but remain strongly correlated in optical density with each other. Also, color and density information are interrelated in the pixels of both of the filtered images. The technique described is a whitening transformation on the bivariate distribution of image pixels, this results in two uncorrelated axes, one relating to density and the other relating to color. The spatial effect on the two original images is to produce two separate, transformed, “color” and “density” images.     

基于排序差值脉冲噪声检测的彩色图像滤波方法

提出一种针对彩色图像脉冲噪声进行检测,并根据检测结果利用改进的自适应矢量中值滤波法滤除彩色图像脉冲噪声的方法。试验结果表明,该方法能够明显地减少脉冲噪声检测过程中的噪声漏判数量,有效地去除彩色图像中的脉冲噪声,滤波后不会产生新的颜色,并能较好地保持图像的边缘与细节信息。     

彩色图像矢量滤波技术综述

彩色图像滤波是彩色图像处理的最基本的研究领域之一。彩色图像滤波技术可以分成标量滤波法和矢量滤波法两大类。其中,标量滤波法只是早期的滤波方法。大量的研究表明,矢量滤波法比标量滤波法更加有效,因为它更能保护彩色图像的光谱特性。为使人们对彩色图像矢量滤波技术及其应用有个系统的了解,该文首先全面地总结了彩色图像矢量滤波的基本理论和方法,并跟踪该领域的最新进展,同时分析介绍了彩色图像矢量滤波技术的一些典型应用;然后对彩色图像矢量滤波技术进行了分类,并对每种类型的滤波算法中经典和目前最常用的算法做了详细的介绍和阐述;接着结合笔者对该领域的研究,提出了一些新的研究方法;最后,对于一些有代表性、经常使用的矢量滤波算法,以冲击噪声为例,给出了其视觉上的滤波效果和客观的评估数据。     

基于PGF检测的改进彩色图像滤波方法

针对彩色图像中的噪声污染问题,提出一种改进的开关自适应矢量滤波方法。通过对噪声图像进行同组滤波器检测得到滤波窗口内满足检测条件的噪声像素个数,当满足条件的像素个数较少时,直接对检测出的噪声进行矢量中值滤波,当满足条件的像素个数较多时,采用改进的自适应矢量中值滤波器进行2次检测后再滤波。实验结果表明,该方法能提高噪声检测的准确性,并能更好保护滤波的细节。