一种改进的图像中脉冲噪声滤波方法

提出了一种估计脉冲噪声强度的方法,并针对传统中值滤波(MF)法在去除脉冲噪声时存在的不足,给出了一种由噪声检测和噪声滤波2个阶段组成的图像滤波方法.实验结果表明,采用本文估计方法获得的噪声强度偏差较小,噪声滤波方法能在有效去除噪声的同时保持图像的边缘.     

红外图像噪声先验中值滤波方法的设计与应用

红外图像中的脉冲噪声影响温度测量及分析,同时影响红外图像的可观性。传统中值滤波容易造成过度滤波,影响温度分布,丢失图像温度信息。针对传统方法的缺陷,借鉴噪声检测的中值滤波方法,将其应用于红外图像的脉冲噪声滤波。该方法在中值滤波之前进行一次脉冲噪声检测,确定受到噪声污染的像素点,并进行记录标识。然后进行对噪声污染像素的针对性滤波,实验证明,达到了既能有效滤除噪声,又完整保留温度信息的目的。     

一种基于两阶段的脉冲噪声滤除算法

本文提出了一种基于两阶段的脉冲噪声滤除方法.在算法的第一阶段,提出利用列队排序检测器(ROD)来检测图像中所有可能的脉冲噪声点.在第二阶段,对所有的噪声候选点进行自适应中值滤波,滤波窗口的尺寸大小是根据噪声密度自适应调整的.该算法能对图像的边界以及非噪声点进行保护.实验表明,本文算法在滤除脉冲噪声的同时可以有效地保护图像细节,尤其是在噪声密度非常大的情况下.     

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。     

基于脉冲耦合神经网络的椒盐噪声滤波

基于脉冲耦合神经网络,提出了一种有效的椒盐噪声图像滤波算法.首先利用PCNN相似群神经元同步发放脉冲的特性检测噪声,并给出了神经元参数的估计方法.然后考虑到噪声点应和最近的非噪声点最相似,提出了一种扩展窗口中值滤波算法对噪声点进行滤波.仿真表明,本文提出的方法对不同强度的噪声图像均体现了优异的滤波性能,和相关的中值滤波算法相比也体现了相当明显的优势.     

一种针对图像脉冲噪声的改进中值滤波算法

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.     

一种新的图像去噪混合滤波方法

为了去除图像中混入的脉冲噪声和高斯噪声,提出了一种基于自适应中值滤波和模糊加权均值滤波的混合滤波方法.该方法首先进行噪声检测把受高斯型噪声污染的像素和受脉冲型噪声污染的像素区别开来,然后对受高斯噪声污染的像素采用模糊加权均值滤波算法,而对受脉冲噪声污染的像素则采用改进的中值滤波算法进行去噪.仿真结果证明,该方法更具有实用性和有效性.     

图像脉冲噪声的概率神经网络识别滤波方法

提出了一种用概率神经网络(PNN)检测图像随机脉冲噪声点方法.首先提取已知图像脉冲噪声像素点的特征作为PNN的输入,然后建立了PNN脉冲噪声点识别模型,再对其它噪声图像的每一个像点进行识别,最后只对噪声点进行中值滤波.Matlab仿真实验表明,同BPNN检测方法相比,该网络能明显提高识别正确率,因此有更好的脉冲噪声滤除效果,且该方法滤除脉冲噪声简单快速,是一种较好的神经网络图像脉冲噪声识别滤除方法.     

一种基于PCNN的医学图像混合噪声滤波方法

根据脉冲耦合神经网络同步脉冲发放特性来定位脉冲噪声和高斯噪声点的位置,提出一种在该网络控制下,只对与噪声相关的像素进行均值计算以替代噪声像素的亚均值滤波算法,实现了图像的较强自适应滤波。计算机仿真实验结果表明,这种方法适应性强,在去除医学图像噪声的同时能很好地保留医学图像的细节和边缘信息,有利于改善医学图像质量、提高信息利用率和诊断的正确率。该方法的效果优于均值滤波、中值滤波、维纳滤波等去噪方法,是去除医学CT图像混合噪声的一种比较理想的方法。     

一种基于PCNN的医学图像混合噪声滤波方法

根据脉冲耦合神经网络同步脉冲发放特性来定位脉冲噪声和高斯噪声点的位置,提出一种在该网络控制下,只对与噪声相关的像素进行均值计算以替代噪声像素的亚均值滤波算法,实现了图像的较强自适应滤波。计算机仿真实验结果表明,这种方法适应性强,在去除医学图像噪声的同时能很好地保留医学图像的细节和边缘信息,有利于改善医学图像质量、提高信息利用率和诊断的正确率。该方法的效果优于均值滤波、中值滤波、维纳滤波等去噪方法,是去除医学CT图像混合噪声的一种比较理想的方法。     

A switching median filter with boundary discriminative noise detection for extremely corrupted images.

A novel switching median filter incorporating with a powerful impulse noise detection method, called the boundary discriminative noise detection (BDND), is proposed in this paper for effectively denoising extremely corrupted images. To determine whether the current pixel is corrupted, the proposed BDND algorithm first classifies the pixels of a localized window, centering on the current pixel, into three groups--lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. The center pixel will then be considered as "uncorrupted," provided that it belongs to the "uncorrupted" pixel group, or "corrupted." For that, two boundaries that discriminate these three groups require to be accurately determined for yielding a very high noise detection accuracy--in our case, achieving zero miss-detection rate while maintaining a fairly low false-alarm rate, even up to 70% noise corruption. Four noise models are considered for performance evaluation. Extensive simulation results conducted on both monochrome and color images under a wide range (from 10% to 90%) of noise corruption clearly show that our proposed switching median filter substantially outperforms all existing median-based filters, in terms of suppressing impulse noise while preserving image details, and yet, the proposed BDND is algorithmically simple, suitable for real-time implementation and application.     

A fuzzy impulse noise detection and reduction method.

Removing or reducing impulse noise is a very active research area in image processing. In this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise: fuzzy impulse noise detection and reduction method (FIDRM). It can also be applied to images having a mixture of impulse noise and other types of noise. The result is an image quasi without (or with very little) impulse noise so that other filters can be used afterwards. This nonlinear filtering technique contains two separated steps: an impulse noise detection step and a reduction step that preserves edge sharpness. Based on the concept of fuzzy gradient values, our detection method constructs a fuzzy set impulse noise. This fuzzy set is represented by a membership function that will be used by the filtering method, which is a fuzzy averaging of neighboring pixels. Experimental results show that FIDRM provides a significant improvement on other existing filters. FIDRM is not only very fast, but also very effective for reducing little as well as very high impulse noise.     

基于差分法的噪声检测和变分法的图像滤波算法

针对图像的椒盐噪声滤除算法中,在噪声检测阶段对噪声点的检测通常不够准确,在噪声恢复阶段,又缺乏对边缘信息的保护,文中提出了一种两步复原法,以用于复原被脉冲噪声破坏的模糊图像。算法将滤噪过程分为噪声检测和噪声恢复阶段。噪声检测过程中,在滑动窗口扩大当前的像素值和其他像素值之间的有序差异,来确定当前像素是否为噪声像素。而在噪声恢复过程中利用变分法,确保图像的边缘和细节。实验结果表明,文中所提检测、降噪方法在噪声密度较高的情况下,优于其他算法。     

A Fast Adaptive Salt and Pepper Noise Reduction Method in Images

In this paper, an effective filtering method is proposed to remove impulse noise from images. In this two-stage method, detected noise-free pixels remain unchanged. Afterwards, a Gaussian filter with adaptive variances according to the image noise level is applied on the noisy pixels. Experimental results show that the proposed method outperforms recent impulse denoising methods in terms of PSNR, MAE, IEF, and SSIM. Moreover, the speed of the method is comparable with them, and it can be used effectively in real-time applications.     

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.     

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).     

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.     

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.     

Adaptive 3-D median filtering for restoration of an image sequence corrupted by impulse noise

An adaptive 3-D median filtering, which achieves optimal image quality as well as fast computing time, is proposed to remove the impulse noise from a highly corrupted image sequence. The proposed algorithm is compared with the widely used impulse noise removal algorithms with respect to the peak signal-to-noise ratio and the number of computations. The proposed algorithm preserves the image details which are not expected to be corrupted by impulse noise so that the number of computations can be minimized. It has good restoration performance whether the number of pixels corrupted by impulse noise is large or small. In the proposed algorithm, the impulse noise ratio, which is the ratio of the number of pixels corrupted by impulse noise to the total number of pixels, is estimated, and the restoration filtering is adaptively applied based on the estimated impulse noise ratio.     

A Universal Impulse Noise Filter with an Impulse Detector and Nonlocal Means

In this paper, we propose an efficient filter for universal impulse noise removal. Operation is carried out in two stages: impulse detection followed by filtering. For detection, a robust local image statistic, called the extremum compression rank-order absolute difference (ECROAD), is designed to detect impulse noise in an image. For filtering, a universal impulse noise filter is proposed by combining the ECROAD statistic with the nonlocal means (NLM). The inherited switching behavior will preserve image details by selecting possible “noise pixels” for processing. Meanwhile, the joint impulsive weight is able to avoid the effect of impulsive components in restoring candidates. Simulation results show that the proposed filter produces excellent results and outperforms most existing filters for different impulse noise models.