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.     

中值滤波算法在人眼视觉系统中的增强改进

本文提出一种在中值滤波算法基础上利用人眼视觉特性改进的图像去噪增强算法。该方法首先利用人眼特性将灰度级进行调整以提高图像背景亮度,然后在滤波模版中利用JND曲线特性提高像素间的对比度,从而在去除噪声的同时提高了图像的清晰程度,使人眼获得更多的信息量。实验结果表明:该方法与传统滤波算法相比,处理过的图像更利于人眼观察。     

红外图像的自适应混合双边滤波算法

针对红外图像中的混合噪声,提出了一种自适应混合双边滤波算法。首先对双边滤波原理进行了分析,提出不能滤除强高斯噪声和脉冲噪声是由于双边滤波引入灰度域权值而带来的固有不足,因此根据双边滤波算法的特点设置了一种像素间的相似度,并以该相似度为基础将双边滤波不能滤除的强噪声点进行了标记,仅对红外图像中标记出的强噪声点进行中值滤波以减少图像模糊,对普通噪声点采用灰度方差自适应双边滤波以保留更多边缘特征。自适应混合双边滤波能够有效滤除红外图像中的高斯噪声、脉冲噪声以及由其组成的混合噪声,同时在滤波过程中并不降低双边滤波保留红外图像边缘特征的性能。仿真实验结果表明,与传统双边滤波、改进的双边滤波以及各项异性扩散-中值滤波算法相比,该算法无论是滤除红外图像的混合噪声还是保留边缘特征都较为优越。     

一种具有噪声鲁棒性的人脸表情识别算法研究

为降低噪声对人脸表情识别的影响,首先提出具有人眼视觉特性的各向异性扩散滤波方法,对图像进行滤波预处理;同时采用改进HOG算子提取人脸表情特征。实验结果表明,改进的各向异性扩散滤波算法在滤除噪声的同时能更好地保留表情图像的弱小细节信息,改进HOG算子相比传统特征提取算子可以更准确地描述人脸表情特征。因此,该算法是一种有效的、具有一定噪声鲁棒性的人脸表情识别算法。     

基于人眼视觉特性的自适应的图像增强算法的研究

本文提出了一种基于人眼视觉特性的自适应的图像增强算法,这种算法是对基于ＬＩＰ模型的Ｌｅｅ图像增强算法的改进。该算法能够有效地增强整个图像的对比度,增大图像的动态范围,并且依据人眼视觉特性,增强图像的边缘而不明显地增大噪声。这种算法容易实现,适用于实时系统的应用。几种算法实验结果的分析和分析,表明了这种算法的有效性。     

基于自适应滤波的头部姿态跟踪方法研究

针对视觉式头部定位系统存在着噪声统计特性掌握不充分的问题,设计了一种新的基于自适应滤波的头部姿态跟踪方法,其综合了视觉测量算法和自适应滤波算法。在获取n对2D/3D(图像点/空间点)匹配对的基础上,先利用正交迭代算法(OI)解算头部姿态,然后应用自适应滤波器估计精度更高的头部姿态,最后针对带有时变噪声且噪声统计特性掌握不充分的测量值,应用头部姿态跟踪方法进行了仿真测试,结果显示该方法的测量精度有很大提高,并且验证了所设计的头部姿态跟踪方法是合理且有效的。     

一种用于消除红外图像噪声的自适应加权滤波算法

针对带有高斯噪声和椒盐噪声两种混合噪声的红外图像,提出了一种自适应加权混合去噪算法。该算法首先通过邻域像素的灰度差值来判断像素噪声的类别,然后对高斯噪声采用自适应加权均值滤波法滤除,对椒盐噪声采用自适应加权中值滤波算法滤除。实验表明,该方法优于传统均值滤波算法和中值滤波算法,能同时消除混合噪声,并具有较好的保护图像细节的能力。     

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.     

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。     

图像去噪的新型自适应混合滤波算法

针对含有椒盐噪声和高斯噪声的灰度图像,研究一种新型的自适应混合滤波算法.首先利用改进的自适应中值滤波算法过滤图像中的椒盐噪声;其次利用改进的自适应均值滤波算法过滤图像中的高斯噪声.这种混合滤波算法具有自适应扩大滤波窗口以及自适应选择最佳阉值的特点.计算机仿真实验证实,该方法不仅在改善信噪比(SNR)和最小均方误差(MSE)上明显优于传统的中值滤波、均值滤波、小波硬阈值、软阈值等方法,而且能较好地保护图像细节.     

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.     

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.     

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。     

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.     

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 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.     

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.