一种针对图像脉冲噪声的改进中值滤波算法北大核心

针对中值滤波算法在图像脉冲噪声处理中存在的不足,提出一种新的改进中值滤波算法。该方法根据噪声图像的极值和像素点滤波窗口的局部信息对滤波窗口内像素点(含待处理像素点)是否为噪声点进行判断,剔除滤波窗口内的噪声点,然后根据新的滤波窗口及待滤波的中心像素点灰度值信息进行滤波操作。以迭代的方法更新噪声图像中的每个像素点,从而去除图像中的脉冲噪声。实验结果表明,与传统中值、加权中值、多级中值滤波方法相比,该方法能有效去除图像中的脉冲噪声,并保持图像细节特征完整。     

基于×字形窗口的自适应中值滤波算法

传统的中值滤波方法在去除脉冲噪声的同时会损失部分图像细节,且运行速度也不能很好地满足实时性要求。在此对Matlab工具箱中的中值滤波算法进行改进,提出一种基于×字形滤波窗口的自适应中值滤波算法。该方法具有根据3×3的×字形窗口中噪声点个数自适应调整滤波窗口大小及根据矩阵的对称性及基本的逻辑运算实现×字形窗口的特点。实验结果表明,与传统的方形窗口中值滤波算法相比,该方法在有效去除椒盐噪声和脉冲噪声的同时,较好地保持了图像细节,缩短了运行时间。     

EMCCD图像自适应模糊中值滤波算法研究

针对电子倍增CCD（EMCCD）图像噪声密度随着增益的变化而变化,提出了一种基于噪声点检测的自适应模糊中值滤波算法。该算法由模糊滤波模块和自适应模块两部分组成。首先,该算法对滤波窗口内的中心点进行噪声检测;然后对检测为噪声的像素点引入双阈值,并根据引入的阈值和滤波窗口内的中值建立噪声点的模糊隶属函数,根据模糊隶属函数对噪声点进行滤波处理后输出;最后采用自适应模块调整待处理图像的像素。仿真及实验结果表明,新算法不仅能够有效地将图像中的噪声去除,而且很好地保护了图像中的细节和边缘,PSNR比传统的自适应中值滤波算法平均提高了15 dB以上;该算法在低噪声密度情况下性能明显好于其他中值滤波器,在高噪声密度情况下性能也比较稳定。     

一种多子窗口中值加权的高斯噪声滤波算法

为了有效地去除图像中的高斯噪声,提出一种多子窗口中值加权的滤波算法。算法采用5×5滤波窗口,并将其划分为9个3×3子窗口区域,先找出每个子窗口的中值像素点,然后求出每个中值像素点与它们的中值点差的绝对值,利用这些绝对值的平均值采用归一化方法计算出权值。最后将各子窗口的中值进行加权运算作为滤波窗口中心像素点的滤波输出。实验结果表明,该算法对图像中的高斯噪声具有较好的滤除性能,并且较好地保持了图像的细节,效果优于传统的滤波算法。     

脉冲噪声图像的中-均值滤波算法

由于在图像信息的获取和传输过程中,图像常常受到不同程度的脉冲噪声污染。为了有效地去除高浓度脉冲噪声,提出了一种基于中－均值滤波器的噪声去除算法。该方法根据脉冲噪声特点,设定一个简单的噪声检测算子,根据噪声检测结果设定自适应滤波窗口,同时根据噪声密度选择中值和均值滤波器。为了更加有效地保留图像的原有信息,对非噪声点不做滤波处理。仿真结果表明,所提出的中－均值滤波方法不仅能有效地去除高浓度的脉冲噪声,而且能很好地保留图像的原有信息,并具有较短的滤波处理时间。     

一种基于中值-模糊技术的混合噪声滤波器

结合中值与模糊滤波技术,提出了一种新的图像混合噪声滤波算法。算法将受混合噪声污染的图像分为脉冲噪声点集与含有高斯噪声的像素点集两部分,首先进行灰度极值检测,进而借助邻域纹理信息准确检测出脉冲噪声,并以中值滤波滤除;对于含有高斯噪声的像素点则采用一种保护细节的模糊滤波器进行处理。实验结果说明算法不仅能有效地滤除脉冲与高斯混合噪声,而且可以较好地保护图像细节。     

一种改进的加权均值滤波算法

当图像中同时存在脉冲噪声和高斯噪声时,传统的中值滤波算法和均值滤波算法均不能达到较好的去噪效果。针对这一问题,提出了一种改进的加权均值滤波算法。算法采用局部阈值优化的方法计算各像素点的权值,将滤波窗口各像素点的灰度值与对应的权值进行加权运算,结果作为窗口中心点的滤波输出。仿真实验结果证明,该算法对脉冲噪声和高斯噪声具有较强的去噪能力,且较好地保持了图像的细节,效果均优于传统中值、均值滤波算法和改进的中值滤波算法(IMF)。     

一种改进的自适应中值滤波算法

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。     

采用一种新的滤波方法去除图像噪声

提出了一种新的滤波方法,使用适当窗口在图像上滑动,计算该窗口中心像素的块均匀度,并与整幅图像的块均匀度比较,自适应地确定窗口中心像素是否为噪声点;然后统计该窗口中噪声点的个数,自适应地调整滤波窗口大小,最后自适应地计算权值,并采用改进的加权中值滤波方法对噪声点进行逐点滤波.模拟实验和分析结果表明该方法是有效的,既能有效地去除图像噪声点,又能较好地保持图像细节部分,为去除图像中的噪声提供了一种新的方法.     

改进自适应中值滤波的图像去噪

针对传统自适应中值滤波算法的不足,提出了一种改进的自适应中值滤波算法,该算法引入了角度和相关性,第一,根据角度α的大小,判断当前像素点是否为可疑噪声点,再由其像素间的相关性判断此可疑噪声点是否为噪声点。第二,把排序得到的中值与窗口中所有像素点的均值加权得到新的灰度中值,再将噪声点用新的灰度中值替代,从而达到滤除噪声的效果。实验结果表明,该算法滤波,既能有效地平滑噪声,又能保存细节,效果远优于传统自适应中值滤波算法。     

一种快速的彩色图像矢量中值滤波算法

文章提出一种新的快速矢量中值滤波算法。首先，求取象素的方向区域距离测度并将滤波窗口划分为两个子窗口：然后，在子窗口内进行矢量中值滤波。实验结果表明，该算法速度快，具有较好的滤波效果。     

An improved recursive and adaptive median filter for high density impulse noise

An improved recursive and adaptive median filter (RAMF) for the restoration of images corrupted with high density impulse noise is proposed in the present paper. Adaptive operation of the filter is justified with the variation in size of working window which is centered at noisy pixels. Based on the presence of noise-free pixel(s), the size of working window changes. The noisy pixels are filtered through the replacement of their values using both noise-free pixels of the current working window and previously processed noisy pixels of that window. These processed noisy pixels are obtained recursively. The combined effort thus provides an improved platform for filtering high density impulse noise of images. Experimental results with several real-time noisy images show that the proposed RAMF outperforms other state-of-the-art filters quantitatively in terms of peak signal to noise ratio (PSNR) and image enhancement factor (IEF). The superiority of the filter is also justified qualitatively through visual interpretation.     

Erratum to: Multi-scale directional-filtering-based method for follicular lymphoma grading

A new decision-based algorithm has been proposed for the restoration of digital images which are highly contaminated by the saturated impulse noise (i.e., salt-and-pepper noise). The proposed denoising algorithm performs filtering operation only to the corrupted pixels in the image, keeping uncorrupted pixels intact. The present study has used a coupled window scheme for the removal of high density noise. It has used sliding window of increasing dimension, centered at any pixel and replaced the noisy pixels consecutively by the median value of the window. However, if the entire pixels in the window are noisy, then the dimension of sliding window is increased in order to obtain the noise-free pixels for median calculation. Consequently, this algorithm has been found to be able to remove the high density salt-and-pepper noise and also preserved the fine details of the four images, Lena, Elaine, Rhythm, and Sunny, used as test images in this study (The latter two real-life images have been acquired using Sony: Steady Shot DSC- S3000). Experimentally, it has been found that the proposed algorithm yields better peak signal-to-noise ratio, image enhancement factor, structural similarity index measure and image quality index, compared with the other state-of-art median-based filters viz. standard median filter, adaptive median filter, progressive switched median filter, modified decision-based algorithm and modified decision-based unsymmetric trimmed median filter.     

A Fuzzy Switching Median Filter of Impulses in Digital Imagery (FSMF)

This paper proposed a fuzzy-based switching technique that aims at detection and filtering of impulse noises from digital images. Two types of noise models are used to obtain the noisy images. In this two-step process, the noise-free pixels are remained unchanged. The proposed detection algorithm uses 5 \(\times \) 5 window, based on all neighboring pixels on the center of the window of a noisy pixel. Two weighted median filters are devised, and a particular one is applied selectively to the noisy pixel based on the characteristics of the neighboring pixels within the window. Instead of a single threshold, two threshold values are used in the proposed fuzzy membership function to partition the noise level, and accordingly, a filtering method is applied to restore the corrupted pixel. Experimental results show that the proposed technique outperforms the existing impulse denoising methods in terms of peak signal-to-noise ratio and visual effects, with a comparable time complexity with the existing methods.     

Local pixel statistics based impulse detection and hybrid color filtering for restoration of digital color images

This paper presents a two stage filtering system to remove random valued impulse noise from color images based on local statistics of the filtering window under consideration. In the first stage, to detect the noisy pixel, the locally adaptive threshold is derived from the pixels of the filtering window. In the second stage, the restoration of the noisy pixel is done on the basis of brightness and chromaticity information obtained from the neighbouring pixels in the filtering window. Simulation results show that the proposed scheme yields much superior performance in comparison with other color image filtering methods.     

A new fuzzy-based decision algorithm for high-density impulse noise removal

This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).     

An effective 2-stage method for removing impulse noise in images

In this paper, a robust 2-stage impulse noise removal system is proposed to remove impulse noise from extremely corrupted images. The contributions are in two-fold. First, a neuro-fuzzy based impulse noise detector (NFIDET) is introduced to identify the noisy pixels. NFIDET is a powerful noise detector that can handle image corruption even up to 90% with zero miss and false detection rate with a simple neuro-fuzzy structure. This is the best result among the other impulse noise detectors in the literature. Second, this paper presents a new approach for weight calculation of adaptive weighted mean filter by using robust statistical model. An adaptive robust weighted mean (ARWM) filter removes a detected noisy pixel by adaptively determining filtering window size and replacing a noisy pixel with the weighted mean of the noise-free pixels in its window. A Geman–McClure robust estimation function is used to estimate the weights of the pixels. Simulation results also show that the proposed robust filter substantially outperforms many other existing algorithms in terms of image restoration.     

基于灰色关联度的图像混合噪声的自适应滤波算法

利用中值滤波和灰色关联度的特点,提出基于中值滤波和灰色关联度相结合的混合噪声图像滤波算法.算法选取加窗混合噪声图像的中值,采用灰色关联度自适应地计算各像素的加权系数,通过加权得到结果.实验结果表明,该算法有较好的自适应性,不但能够有效去除含有高斯噪声和脉冲噪声的图像噪声,而且能较好地保护图像的细节信息,提高图像的去噪效果和清晰度.     

基于人眼视觉特性的混合滤波算法

为了有效地滤除混合噪声,本文提出了一种基于人眼视觉特性的混合滤波算法。该方法首先采用基于人眼视觉特性的噪声敏感系数作为阈值来确定脉冲噪声点,对检测出脉冲噪声点采用自适应窗口大小的迭代中值滤波进行滤波,而对于含有高斯噪声的像素点则采用一种保护细节的改进的自适应模糊滤波器进行处理。该算法与标准滤波方法及其它改进混合滤波算法相比,具有更好的滤波性能。