Edge detection in noisy images by neuro-fuzzy processing

A novel neuro-fuzzy (NF) operator for edge detection in digital images corrupted by impulse noise is presented. The proposed operator is constructed by combining a desired number of NF subdetectors with a postprocessor. Each NF subdetector in the structure evaluates a different pixel neighborhood relation. Hence, the number of NF subdetectors in the structure may be varied to obtain the desired edge detection performance. Internal parameters of the NF subdetectors are adaptively optimized by training by using simple artificial training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed NF operator outperforms competing edge detectors and offers superior performance in edge detection in digital images corrupted by impulse noise.     

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.     

基于噪声估计的自适应开关型中值滤波器

提出了一种基于噪声估计的自适应开关型中值滤波器(IASMNE,improved adaptive switching median filter based on noise estimation)。IASMNE以图像经小波变换后在不同尺度和不同方向提取的子带滤波系数值的统计信息构成刻画图像受噪声干扰程度的特征矢量,在大量噪声图像上获得的特征矢量为学习数据集,并利用支持向量回归(SVR)分析实现对图像中噪声比例的准确估计。基于此,IASMNE对高、中、低不同噪声比例图像启动不同的滤波策略,并灵活设置滤波参数。大量实验表明,与其它开关型滤波器相比,IASMNE能够合理地根据图像噪声干扰程度进行最佳滤波,尤其是对于大于70%的椒盐噪声(SPN)能够大幅度提高图像质量。     

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.     

A simple neuro-fuzzy method for improving the performances of impulse noise filters for digital images

A novel method for improving the performances of impulse noise filters is presented. The method enhances the performance of an impulse noise filter in two ways: increases its noise-suppression ability and decreases its distortion effects. The method is based on a simple 2-input 1-output neuro-fuzzy system. The internal parameters of the system are tuned by training. Training of the system is easily accomplished by using a simple computer-generated artificial image. The proposed method can easily be used with any impulse noise removal operator. The application of the method is completely independent of the noise removal operator and it has no influence on the filtering behavior of the operator. Experimental results show that the proposed method may efficiently be used with any type of impulse noise removal operator to significantly improve its filtering performance.     

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。     

直方图加权均值滤波器

本文提出了一种适合于消除图像盐椒噪声的滤波器——直方图加权均值(HWM)滤波器.该算法以加权均值滤波器为基础,利用被污染图像的直方图函数作为权值进行加权运算.实验表明,对于噪声率在5％到90％的的噪声图像,HWM滤波器具有良好而稳健的去噪效果,当噪声率超过70％时,其优越性更加突出.     

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.     

FLANN-based adaptive threshold selection for detection of impulsive noise in images

In this paper, a novel scheme has been suggested for removing random-valued impulsive noise from images. The proposed scheme utilizes a second-order differential impulse detection followed by a recursive median filter on the corrupted pixel locations. Adaptive threshold selection from noisy image characteristics has been emphasized in this paper. A functional link artificial neural network is used for this purpose. Comparative analysis on standard images at different noise conditions shows that the proposed scheme, in general, outperforms the existing schemes.     

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 new impulse detector based on order statistics

The acquisition or transmission of digital images through sensors or communication channels is often interfered by impulse noise. It is very important to eliminate noise in the images before subsequent processing, such as image segmentation, object recognition, and edge detection. In this letter, a novel impulse noise-detection algorithm is presented, which can remove impulse noise from corrupted images successfully, and at the same time, without eliminating image details. The algorithm is based on the order statistics within a local window. Although our method is low in complexity when compared with some other complicated algorithms, experimental results show that it produces better restored images than many other existing techniques.     

A universal noise removal algorithm with an impulse detector.

We introduce a local image statistic for identifying noise pixels in images corrupted with impulse noise of random values. The statistical values quantify how different in intensity the particular pixels are from their most similar neighbors. We continue to demonstrate how this statistic may be incorporated into a filter designed to remove additive Gaussian noise. The result is a new filter capable of reducing both Gaussian and impulse noises from noisy images effectively, which performs remarkably well, both in terms of quantitative measures of signal restoration and qualitative judgements of image quality. Our approach is extended to automatically remove any mix of Gaussian and impulse noise.     

An efficient algorithm for the removal of impulse noise from corrupted images

In this letter, we propose an efficient algorithm, which can successfully remove impulse noise from corrupted images while preserving image details. It is efficient, and requires no previous training. The algorithm consists of two steps: impulse noise detection and impulse noise cancellation. Extensive experimental results show that the proposed approach significantly outperforms many other well-known techniques for image noise removal.     

Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector

A predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector (PASMF) is presented. The PASMF has a noise detector stage and a noise filtering stage. The noise detector implemented using feed forward neural network detects impulse noises in the corrupted image. The filter is a modified median filter, which removes detected impulse noise from the image. In contrast to the standard median filter, the PASMF computes the median value after predicting the appropriate values for neighboring corrupted pixels of the current central pixel in the filtering window. The results show that the PASMF gives better performance visually as well as in terms of different performance measures.     

An Analog–Digital Hardware for L-Estimate Space-Varying Image Filtering

An analog–digital hardware solution for implementation of the L-estimate space-varying filtering has been proposed. The considered filter form is based on the robust space/spatial-frequency representation and provides efficient denoising of two-dimensional signals/images corrupted by heavy-tailed noise. Moreover, for images with fast-varying details and textures, the L-estimate filtering outperforms the commonly used filters. However, it requires significant processing time, since the space/spatial-frequency representation is calculated for each pixel, on a window by window basis. Therefore, in order to make it feasible for practical applications, a fast implementation of L-estimate space-varying filtering is proposed using a combined analog–digital approach. It provides efficient real-time processing of images corrupted by strong mixed Gaussian and impulse noise.     

A new efficient approach for the removal of impulse noise fromhighly corrupted images

A new framework for removing impulse noise from images is presented in which the nature of the filtering operation is conditioned on a state variable defined as the output of a classifier that operates on the differences between the input pixel and the remaining rank-ordered pixels in a sliding window. As part of this framework, several algorithms are examined, each of which is applicable to fixed and random-valued impulse noise models. First, a simple two-state approach is described in which the algorithm switches between the output of an identity filter and a rank-ordered mean (ROM) filter. The technique achieves an excellent tradeoff between noise suppression and detail preservation with little increase in computational complexity over the simple median filter. For a small additional cost in memory, this simple strategy is easily generalized into a multistate approach using weighted combinations of the identity and ROM filter in which the weighting coefficients can be optimized using image training data. Extensive simulations indicate that these methods perform significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques with as much as 40% impulse noise corruption. Moreover, the method can effectively restore images corrupted with Gaussian noise and mixed Gaussian and impulse noise. Finally, the method is shown to be extremely robust with respect to the training data and the percentage of impulse noise.     

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.     

Switching degenerate diffusion PDE filter based on impulselike probability for universal impulse noise removal

In this paper, a switching degenerate diffusion partial differential equation filter (SDDPDE) is developed by introducing the switching operators for reducing all kinds of impulse noise, and especially for images having a mixture of salt-and-pepper impulse noise and random-valued impulse noise which is a shortage for most of the existing filtering models. Our SDDPDE consists of the coarse and fine filtering stages. In the coarse filtering stages, the switching operator depends on a simple noise detector. In the fine filtering stages, we introduce the notion of impulselike probability, and the switching operator depends on both a simple noise detector and impulselike probability. Our SDDPDE will denoise noise pixels detected by the coarse detector while further modify the so-called noise-free pixels according to impulselike probability. The main advantages of our SDDPDE over published approaches are its simplicity and universality. In addition, we demonstrate the performance of our SDDPDE via application to three standard test images, corrupted by salt-and-pepper impulse noise, random-valued impulse noise and mixed impulse noise with high-noise levels, and the comparison with the other well-known filters. Experimental results show that our SDDPDE achieves high peak signal-to-noise ratio and better visual effect.     

基于相关度量的高椒盐噪声软阈值直方图滤波算法

利用图像邻域相关和直方图对椒盐噪声的鲁棒性,提出了一种针对高椒盐噪声图像的软阈值直方图加权滤波算法.对邻域灰度相关进行了量化分析,定义了灰度相关函数作为信号邻域相关性的度量,并将该系数作为直方图加权滤波算法的软阈值,根据像素被判定为噪声或有效信号的概率,自行调整滤波强度,减少图像滤波处理中的细节损失.仿真结果表明,对于高椒盐噪声图像,本算法在椒盐噪声滤除方面有良好的表现.     

Fuzzy two-step filter for impulse noise reduction from color images.

A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image.