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。     

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.     

Adaptive progressive filter to remove impulse noise in highly corrupted color images

In this paper, an adaptive progressive filtering (APF) technique with low computational complexity is proposed for removing impulse noise in highly corrupted color images. Color images that are corrupted with impulse noise are generally filtered by applying a vector-based approach. Vector-based methods tend to cluster the noise and receive a lower noise reduction performance when the noise ratio is high. To improve the performance, in the proposed technique, a new reliable estimation of impulse noise intensity and noise type is made initially, and then a progressive restoration mechanism is devised, using multi-pass non-linear operations with selected processing windows adapted to the estimation. The effect of impulse detection based on geometric characteristics and features of the corrupt pixel/pixel regions and the exact estimation of impulse noise intensity and type are used in the APF to efficiently support the progressive filtering mechanism. Through experiments conducted using a range of color images, the proposed filtering technique has demonstrated superior performance to that of well-known benchmark techniques, in terms of standard objective measurements, visual image quality, and the computational complexity.     

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.     

Removal of impulse noise from color images based on the localized image characteristics and noise level

The proposed method is a switching vector median filter that utilizes suitable noise detection and restoration algorithms for different impulse noise densities. It works in a multistage manner with an enhancing accuracy of noise detection in each successive stage. The processing window is initially categorized into a non-edge-window and an edge-window, depending on the scale of abrupt transitions. The non-edge-window is further verified to be a smooth-window or a disorder-window, whereas the edge-window is again confirmed to be noise-free or a noisy edge-window. The proposed method is simulated on a variety of medical images and other standard test images to prove its efficiency in detection of noise and restoration.     

Quaternion switching filter for impulse noise reduction in color image

A novel approach to impulse noise reduction in color image is introduced in this paper. By applying the quaternion unit transform theory, the difference between two color pixels can be represented in the quaternion form. Based on the difference mentioned above, an efficient filter that can switch between the vector median filter (VMF) and the identity filter (no filtering operation) is proposed. Extensive simulation result indicates that the proposed filter achieves a trade-off between noise suppression and detail preservation in both correlated and uncorrelated impulse noise scenarios when compared with other widely used filters. Furthermore, the computational complexity analysis shows that the proposed filter is quite efficient.     

High performance detection filter for impulse noise removal in images

A high performance detection (HPD) filter is proposed for impulse noise removal in images. In this approach, the noisy pixels are detected iteratively through several phases, based on a set of unique similarity criteria. Simulation results show that the HPD filter outperforms others at medium to high noise rates and suppresses impulse noise effectively while preserving image details, even thin lines.     

基于颜色分量单独噪声检测的混合中值滤波器

为了进一步提高开关型矢量中值滤波器滤除脉冲噪声尤其是细微噪声的能力,提出了一种基于颜色分量单独噪声检测的开关型标量和矢量混合中值滤波器(SVHMF)。与其它经典矢量滤波器相比,SVHMF最大的不同在于它是根据对像素点各个颜色单独进行噪声检测的结果,采用与之相适应的标量和矢量混和滤波方法。新滤波器可以最大限度地保护彩色图像图像像素中未被噪声干扰的颜色分量数据,从而可以进一步提高滤波效果。实验数据表明:SVHMF的滤波效果明显优于其它各类在彩色图像降噪中得到广泛应用的矢量中值滤波器。     

Universal impulse noise filter based on genetic programming.

In this paper, we present a novel method for impulse noise filter construction, based on the switching scheme with two cascaded detectors and two corresponding estimators. Genetic programming as a supervised learning algorithm is employed for building two detectors with complementary characteristics. The first detector identifies the majority of noisy pixels. The second detector searches for the remaining noise missed by the first detector, usually hidden in image details or with amplitudes close to its local neighborhood. Both detectors are based on the robust estimators of location and scale-median and MAD. The filter made by the proposed method is capable of effectively suppressing all kinds of impulse noise, in contrast to many existing filters which are specialized only for a particular noise model. In addition, we propose the usage of a new impulse noise model-the mixed impulse noise, which is more realistic and harder to treat than existing impulse noise models. The proposed model is the combination of commonly used noise models: salt-and-pepper and uniform impulse noise models. Simulation results show that the proposed two-stage GP filter produces excellent results and outperforms existing state-of-the-art filters.     

A new filter for the removal of random-valued impulse noise from highly corrupted images

In this paper, we present a new two-stage filter for the removal of random-valued impulse noise. The new filter identifies noise candidates by analyzing the amount of similar pixels in intensity value, and then reconstructs them by the total variation inpainting method. The experimental results are reported which show the efficiency of our method in removing random-valued impulse noise. Further, our filter can be used for image restoration from images damaged by the superimposed artifacts.     

Impulsive noise filtering based on noise detection in corrupted digital color images

This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations.This work was supported by the Korea Science & Engineering Foundation (KOSEF) under grant no. 981-0912-057-2.     

A hybrid neuro-fuzzy filter for edge preserving restoration of images corrupted by impulse noise.

A new operator for restoring digital images corrupted by impulse noise is presented. The proposed operator is a hybrid filter obtained by appropriately combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of the neuro-fuzzy network are adaptively optimized by training. The training is easily accomplished by using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail, and texture preservation performance while, at the same time, effectively removing noise from the input image. Extensive simulation experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.     

Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images

This paper proposes a new anisotropic diffusion approach to remove the impulse noise and retain the fine details. The proposed approach contains two stages, the first stage detects the impulse noise, and the second stage removes the noisy pixel and retains the fine details of the original image. The Laplacian operator is used to fine-tune the image quality of the restored image in the anisotropic diffusion filter. The proposed approach is tested with PSNR, IEF, correlation factor, and NSER for different test images and the results are compared against existing algorithms. The simulation results show that the proposed approach gives better results than the existing denoising algorithms.     

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.     

Adaptive two-pass rank order filter to remove impulse noise in highly corrupted images

In this paper, we present an adaptive two-pass rank order filter to remove impulse noise in highly corrupted images. When the noise ratio is high, rank order filters, such as the median filter for example, can produce unsatisfactory results. Better results can be obtained by applying the filter twice, which we call two-pass filtering. To further improve the performance, we develop an adaptive two-pass rank order filter. Between the passes of filtering, an adaptive process is used to detect irregularities in the spatial distribution of the estimated impulse noise. The adaptive process then selectively replaces some pixels changed by the first pass of filtering with their original observed pixel values. These pixels are then kept unchanged during the second filtering. In combination, the adaptive process and the second filter eliminate more impulse noise and restore some pixels that are mistakenly altered by the first filtering. As a final result, the reconstructed image maintains a higher degree of fidelity and has a smaller amount of noise. The idea of adaptive two-pass processing can be applied to many rank order filters, such as a center-weighted median filter (CWMF), adaptive CWMF, lower-upper-middle filter, and soft-decision rank-order-mean filter. Results from computer simulations are used to demonstrate the performance of this type of adaptation using a number of basic rank order filters.     

一种用于抑制椒盐噪声的多窗口中值滤波器

该文提出了一种用于抑制椒盐噪声的多窗口中值滤波算法。算法在招待过程中根据具体情况采用不同大小的滤波窗口。仿真结果表明，与标准中值滤波算法相比，该方法不仅可以有效去除图像中的椒盐噪声，特别是在噪声密度非常大的情况下，表现了很好的性。     

The ANN based detector to remove random-valued impulse noise in images

This paper presents an artificial neural network (ANN) based method to detect random-valued impulse noise (RVIN) in images. The proposed method employs the ANN to decide whether a pixel is corrupted or not with RVIN. The inputs of the ANN are the rank ordered absolute differences (ROAD) and the rank-ordered logarithmic difference (ROLD) values. After the detection process is completed, the corrupted pixels are restored by the edge-preserving regularization (EPR) method which allows edges and noise-free pixels to be preserved. The performance of the proposed method is evaluated on different test images and compared with ten different comparison filters from the literature. Simulation results indicate that the proposed method provides significant improvement over comparison filters especially for high noise densities.     

Novel peer group filtering method based on the CIELab color space for impulse noise reduction

This paper presents a novel peer group filtering method for impulsive noise reduction. The main contributions of the proposed method are twofold. First, noise detection is performed in the CIELab, instead of the RGB, color space to enhance the noise detection effect. Secondly, two different-sized windows are used to determine the peer group for deducing more accurate status of each pixel, alleviating the problem of deducing non-corrupted pixels as corrupted in the neighborhood of edges in the textural regions. Based on five typical test color images, experimental results demonstrate that the proposed method achieves better performance in noise detection and hence noise reduction when compared to five existing competitive methods.     

Similar neighbor criterion for impulse noise removal in images

In this paper, a new method is proposed for removing and restoring random-valued impulse noise in images. This approach is based on a similar neighbor criterion, in which any pixel to be considered as an original pixel it should have sufficient numbers of similar neighboring pixels in a set of filtering windows. Compared with other well known methods in the literature, this technique achieves superior performance in restoring heavily corrupted noisy images. Furthermore, it has low computational complexity, and equally effective in restoring corrupted color and gray-level images.     

一个基于像素编组和逐级质量控制的线段检测算子

针对现有线段提取算法存在的不足,提出了一个点元梯度特征引导下的线段检测算子,在编组的前、中和后三大环节进行逐级质量控制.基于梯度最优并结合邻近性、方向性等规则控制"欠提取"错误,采用假设检验方法控制"过提取"错误.多源数据实验表明,提出的算法在编组中不易受到弱梯度像素或噪声的干扰,与经典的线段提取算子相比,在线段提取的效率和稳健性都有一定的优势,有利于实现从场景到结构的重要视觉符号描述.