A new method to remove random-valued impulse noise in images

This paper presents a new method for detecting random-valued impulse noise (RVIN) in images. The proposed method is based on similar valued neighbor criterion and the detection of the noisy pixels are realized in maximum four phases. After the corrupted pixels detected in each phase, the median filtering is performed for only these pixels. As such, corrupted pixels are suppressed gradually at the end of the each phase. The performance of the proposed method is evaluated on different test images and compared with ten different comparison filters from the literature. It is shown from simulation results that proposed method provides a significant improvement over comparison filters.     

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%.     

A two-stage shearlet-based approach for the removal of random-valued impulse noise in images

In this paper, we introduce a novel two-stage denoising method for the removal of random-valued impulse noise (RVIN) in images. The first stage of our algorithm applies an impulse-noise detection routine that is a refinement of the HEIND algorithm and is very accurate in identifying the location of the noisy pixels. The second stage is an image inpainting routine that is designed to restore the missing information at those pixels that have been identified during the first stage. One of the novelties of our approach is that our inpainting routine takes advantage of the shearlet representation to efficiently recover the geometry of the original image. This method is particularly effective to eliminate jagged edges and other visual artifacts that frequently affect many RVIN denoising algorithms, especially at higher noise levels. We present extensive numerical demonstrations to show that our approach is very effective to remove random-valued impulse noise without any significant loss of fine-scale detail. Our algorithm compares very favourably against state-of-the-art methods in terms of both visual quality and quantitative measurements.     

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.     

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.     

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。     

Detail preserving impulsive noise removal

Most image processing applications require noise elimination. For example, in applications where derivative operators are applied, any noise in the image can result in serious errors. Impulsive noise appears as a sprinkle of dark and bright spots. Transmission errors, corrupted pixel elements in the camera sensors, or faulty memory locations can cause impulsive noise. Linear filters fail to suppress impulsive noise. Thus, non-linear filters have been proposed. Windyga's peak-and-valley filter, introduced to remove impulsive noise, identifies noisy pixels and then replaces their values with the minimum or maximum value of their neighbors depending on the noise (dark or bright). Its main disadvantage is that it removes fine image details. In this work, a variation of the peak-and-valley filter is proposed to overcome this problem. It is based on a recursive minimum–maximum method, which replaces the noisy pixel with a value based on neighborhood information. This method preserves constant and edge areas even under high impulsive noise probability. Finally, a comparison study of the peak-and-valley filter, the median filter, and the proposed filter is carried-out using different types of images. The proposed filter outperforms other filters in the noise reduction and the image details preservation. However, it operates slightly slower than the peak-and-valley filter.     

基于斜率差值的自适应图像椒盐噪声滤波算法

提出了一种基于斜率差值的自适应中值滤波算法,以有效去除图像脉冲噪声。该算法在经典自适应中值算法的基础上,采用斜率差值进行噪声判定。针对自适应中值滤波算法和基于斜率的自适应中值滤波算法在噪声强度较高情况下的不足进行了改进,同时解决了噪声块难以去除的问题。实验结果表明,该算法能有效去除图像脉冲噪声,并较好的保护图像细节。     

An expeditious cum efficient algorithm for salt-and-pepper noise removal and edge-detail preservation using cardinal spline interpolation

This work proposes a faster and an efficient way to remove salt-and-pepper impulse noise and edge-preserving regularization of the henceforth obtained image. In this paper, we propose a two phase mechanism where the noisy pixels are identified and removed in the first phase. The detected noisy pixels in the first phase are involved in cardinal spline edge regularization process in the second phase. Promising results were found even for Noise levels as high as 95% with the proposed algorithm. The results were found to be much better than the previously proposed nonlinear filters or regularization methods both in terms of noise removal as well as edge regularization.     

改进自适应中值滤波的图像去噪

传统自适应中值滤波的最大最小窗口尺寸固定,并且其最大最小窗口相差较大时,运算时间较长,去噪效果并小一定最佳。本文针对传统自适应中值滤波算法的不足,提出了改进自适应中值滤波算法,首先根据椒盐噪声的分布特点,从单幅含椒盐噪声图像中估算出椒盐噪声的浓度,并分析噪声浓度与自适应中值滤波窗口尺寸之间的关系,建立它们之间的函数关系一其次根据噪声浓度确定自适应中值滤波的最大最小窗口尺寸,最后对图像进行自适应中值滤波：实验结果表明本文算法运算时间随着噪声浓度的变化而变化,而且从PSNR角度来看本文去噪效果比传统自适应中值滤波效果较好。     

基于小波阈值的激光探测声音信号去噪研究

激光声音探测技术是声音探测领域中重要的研究方向,但该探测技术极易受到背景光、大气湍流等引起的噪声干扰,对探测信号的噪声进行抑制是激光声音探测技术的关键。因此提出一种改进的阈值函数,通过调整参数可以改变小波系数估计值与原小波系数之间的偏差,同时尽量保存信号的特征信息。在实验室环境下通过实验验证了基于所提改进阈值函数的小波阈值去噪法的有效性,探测信号经去噪处理后噪声得到有效去除。     

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。     

一种抑制脉冲噪声的边缘检测算法

提出了一种针对脉冲噪声图像的边缘检测算法,算法借鉴了中值滤波的思想,并采用十字型卷积模板计算图像梯度。首先,对参与图像中梯度计算的像素点进行阈值判断,如果是噪声点,该点像素值用3x3窗口中值滤波结果值替代,然后参与梯度计算,如果不是噪声点则直接参与梯度计算;其次对梯度图像进行细化和二值化以提取边缘图像。实验证明,本文算法对脉冲噪声污染图像边缘检测效果良好,较好地抑制了脉冲噪声的影响,而且提取的图像边缘较细,轮廓清晰。和传统的边缘检测算法及基于小波模变换的边缘检测算法相比,算法在抑噪能力上和边缘提取效果上均比较优秀。     

改进的中值滤波去噪算法分析

通过对传统中值滤波的分析,针对传统中值滤波在图像去噪过程中的不足,提出了一种改进算法,根据图像细节特征进行阈值设定,给出噪声与复杂图像细节的判断方法。通过实验仿真该算法对椒盐噪声的抑制和复杂图像细节保护具有很好的鲁棒性和适应性。     

全方位钟形加权中值滤波器

在图象恢复处理中,细节信号的方向性是随机的。采用标准中值滤波器恢复图象时,是将中心象素以用毗邻象素与其它象素在(2N+1)(2N+1)方窗内的图象块中的作用是不一样的。针对上述两个问題,本文提出了全方位钟形加权中值滤波器。在理论上,证明了该滤波器的收敛特性,在实验中将这种滤波器与其它中值滤波器进行了性能比较。结果表明,这种滤波器恢复图象的效果较好。     

一种新的小波图像去噪方法

小波图像去噪已经成为目前图像去噪的主要方法之一,目前的研究主要集中于如何选取阈值使去噪达到较好的效果。边缘信息是图像最为有用的高频信息,在图像去噪的同时,应尽量保留图像的边缘信息,基于这一思想,提出一种新的小波图像去噪方法。用数学形态学算子对图像小波变换后的小波系数进行处理,以去除具有较小支持域的噪声,保留具有连续支持域的边缘。实验结果表明,与普通的小波阈值去噪方法相比,该方法不但可以保留图像的边缘信息,而且能提高去噪后图像的峰值信噪比2～5dB,提高信噪比6～10dB。     

基于方向信息测度的自适应多级中值滤波器

针对图像滤噪中滤除噪声和保护细节(边缘)的矛盾,本文提出一种新的基于方向信息测度的自适应多级中值滤波器。方向性是边缘和噪声的本质区别之一,通过基于方向信息测度所构造的自适应结构,决定中值滤波器的形式和滤波窗口尺寸的大小,而不需要图像和噪声的先验知识。文章比较了标准中值滤波器、单向多级中值滤波器、双向多级中值滤波器和本文方法的结果。实验表明本文方法具有更好的效果。