Monochromatic-Based Method for Impulse Noise Detection and Suppression in Color Images

A new method to detect and reduce the impulse noise in color images is presented in this paper. The method consists of two stages: detection and filtering. Since each of the individual channels (components) of the color image can be considered as a monochrome image, both stages are applied to each channel separately, and then the individual results are combined into one output image. The corrupted pixels are detected in the first stage based on a proposed innovative switching technique. The noise-free pixels are copied to their corresponding locations in the output image. In the second stage, average filtering is applied only to those pixels which are determined to be noisy in the first stage, and only noise-free pixel values are involved in calculating this average. The size of the sliding window depends on the estimated noise density and is very small even for high noise densities. The proposed method is effective in noise reduction while preserving edge details and color chromaticity. Simulation results show that the proposed method outperforms all the tested existing state-of-the-art methods used in digital color image restoration in both standard objective measurements and perceived image quality.     

基于噪声估计和双加权的彩色图像矢量中值滤波

针对彩色图像椒盐噪声滤除中保护色彩和细节这个关键问题,提出了一种基于噪声估计和双加权的滤波新算法.噪声估计采用二级估计策略,第1级基于灰度最大、最小值进行粗估计,第2级采用加权方向算子进行精估计.对非噪声点保持像素灰度不变,对噪声点设计了空间和灰度双加权的矢量中值滤波算法(VMF).实验结果表明,新算法对低、中密度的彩...     

Robust local similarity filter for the reduction of mixed Gaussian and impulsive noise in color digital images

In this paper, a novel technique designed for the suppression of mixed Gaussian and impulsive noise in color images is proposed. The new denoising scheme is based on a weighted averaging of pixels contained in a filtering block. The main novelty of the proposed solution lies in the new definition of the similarity between the samples of the processing block and a small window centered at the block’s central pixel. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the neighborhood of the central pixel is utilized. This measure is defined as the sum of distances in a given color space, between a pixel of the block and a certain number of most similar samples from the filtering window. The main advantage of the proposed scheme is that the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing mixed noise of high intensity and is competitive with respect to the state-of-the-art noise filtering methods.     

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.     

Fast switching based median–mean filter for high density salt and pepper noise removal

This paper proposes a fast switching based median–mean filter for high density salt and pepper noise in images. The extreme minimum value and extreme maximum value of the noisy image are used to identify the noise pixels. In the filtering stage, the corrupted pixel is replaced either by median value or mean value based on the number of noise free pixels in the filtering window. The qualitative and quantitative results show that the proposed filter outperforms the other switching based filters namely ACWMF, PSMF, AMF, DBA and MDBUTMF in terms of noise removal and edge preservation for noise densities varying from 10% to 90%.     

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.     

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.     

Efficient decision based algorithm for the removal of high density salt and pepper noise in images

In this paper, an efficient decision based scheme is proposed for the restoration of grayscale and colour images that are heavily corrupted by salt and pepper noise. The processed pixel is examined for 0 or 255; if found true, then it is considered as noisy pixel else not noisy. If found noisy the four neighbours of the noisy pixels are checked for 0 or 255. If all the four neighbours of the corrupted pixel are noisy, the mean of the four neighbours replaces the corrupted pixel. If any of the four neighbours is a non-noisy pixel, calculate the number of corrupted pixels in the current processing window. If the count is less than three then the noisy pixel is replaced by an unsymmetrical trimmed median. If the current window has more than three noisy pixels, then unsymmetrical trimmed mean replaces the corrupted pixels. If all the pixels of the current processing window are noisy then instead of unsymmetrical trimmed mean, global mean of the image is replaced as output. The uncorrupted pixel is left unchanged. The proposed algorithm is tested on various grayscale and colour images and found that it gives excellent PSNR, high IEF and lowest MSE. Also it consumes average time with excellent edge preservation even at higher noise densities. The quality of the results of proposed algorithm is superior when compared to the various state of the art methods.     

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.     

Color image noise removal algorithm utilizing hybrid vector filtering

This paper proposes a robust approach to color image noise removal that efficiently eliminates noisy pixels by exploiting several vector-class characteristics of multichannel pixels. This algorithm treats multichannel images as a vector class and takes both magnitude and phase angles of the pixel vectors into consideration. It consists of two steps: an efficient noise detector based on pixel vector angle statistics and impulse noise filtering with a hybrid of vector magnitude and vector angle function. Extensive experimental results demonstrate that the proposed approach significantly outperforms several other well-known techniques for color image noise removal.     

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。     

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.     

基于一种新的同态滤波算法的散斑噪声压缩

提出一种新的用于压缩散斑噪声的同态滤波算法。它在同态变换后寻求滤波窗中的最均匀区域,以其灰度均值替代滤波窗中心像素值,重复迭代至其值基本不变,最后作同态变换的逆变换。仿真结果表明它较好的保持了图像的边缘和较有效地抑制了散斑噪声     

Color impulsive noise removal based on quaternion representation and directional vector order-statistics

A new method for detecting and suppressing impulsive noise in color images is presented in this paper. The proposed method is a type of switching vector filters, where the impulse detection is based on the order-statistic information about the color samples in the horizontal, vertical, and diagonal directions. The new solution first uses quaternion-based representation of color differences and median deviation-based techniques to search for the edge direction with the maximum number of similar pixels, and then utilizes the samples aligning with this edge direction to judge whether the current pixel is noisy or not and control the switching between identity (no filtering) and vector median filtering actions. Extensive experimental comparisons exhibit the validity of the proposed approach by showing significant performance improvements over other well-known color image filtering techniques.     

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.     

基于人眼视觉特性的自适应中值滤波算法

为了在滤除图像椒盐噪声的同时能很好地保持图像的细节,提出了一种基于人眼视觉特性(HVS)的自适应中值滤波算法.该方法首先采用基于HVS的噪声敏感系数作为阈值来确定噪声点,然后自适应调整滤波窗口大小,采用迭代中值滤波对噪声点进行滤波.该算法与标准中值(SM)滤波及其它改进中值滤波算法相比,具有更好的滤波性能.     

Adaptive filtering for color filter array demosaicking.

Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and (2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.     

结合邻域权值和色彩信息的彩色立体匹配算法

为了提高彩色图像的立体匹配精度,结合格式塔 心理学和像素的色彩信息,在RGB彩色空间下提出了 一种改进的基于误差绝对值累积(SAD)的立体匹配算法。算法使用色彩相似度和像素距离信 息获取邻域窗内的 像素权值,并使用线性查找表技术加速权值的计算速度;根据像素点RGB色彩分量的分布 特点,生成对应 的分量权值,进一步增强了匹配代价函数的稳定性。实验结果表明,改进后的匹配算法较灰 度SAD及标准彩色SAD算法在精度上有明显的提高。     

Using Sorted Switching Median Filter to remove high-density impulse noises

We propose a novel Sorted Switching Median Filter (i.e. SSMF) for effectively denoising extremely corrupted images while preserving the image details. The center pixel is considered as “uncorrupted” or “corrupted” noise in the detecting stage. The corrupted pixels that possess more noise-free surroundings will have higher processing priority in the SSMF sorting and filtering stages to rescue the heavily noisy neighbors. Five noise models are considered to assess the performance of the proposed SSMF algorithm. Several extensive simulation results conducted on both grayscale and color images with a wide range (from 10% to 90%) of noise corruption clearly show that the proposed SSMF substantially outperforms all other existing median-based filters.     

Switching impulse noise filter based on Laplacian convolution and pixels grouping for color images

This paper presents a new switching filter consisting of three steps to restore color images corrupted by impulse noise. Firstly, Laplacian convolution is performed on pixels in four directions to mark the pixels which are radically different in value from neighboring pixels as noise candidates. Secondly, those missed neighboring pixels involved in the step of pixels grouping decrease the occurrence of false detection. Pixels in the observation window are separated into noisy pixels and normal pixels with a dividing threshold, whose value is assigned according to a noise density estimator. Finally, a modified arithmetic mean filter is applied to restore the polluted image. Extensive experiments show that the proposed method achieves better performance than comparative methods in terms of peak-signal-to-noise ratio and structural similarity. The proposed method can effectively remove impulse noise in which noise density is varying from 10 to 80%.