沿边局部灰度差分椒盐噪声滤波

为了在减小高密度椒盐噪声漏检的同时最大限度提升滤波效果,提出一种新算法.该算法首先利用椒盐灰度极值,抽取准噪声点;然后对准噪声点执行局部窗口沿边检测,判断疑似噪声集合;最后,只对疑似噪声集合中像素点进行就近双边加权灰度替换滤波.实验数据显示,不同噪声密度环境下,本文算法在现有滤波算法的基础上,将峰值信噪比(PSNR)参数分别提高0.2～1.6 dB.实验结果表明,该算法的综合性能优于其他相关的算法.     

基于Lp伪范数和高阶OGS全变分的椒盐噪声去除

全变分(TV)模型广泛应用于椒盐噪声的去除。然而,TV 模型中存在着严重的阶梯效应。近年 来,由于低阶交叠组稀疏(LOGS)全变分能够很好地抑制阶梯效应,受到了越来越多的关注,但仍有改进空间。 实际上,其只考虑一阶图像梯度的先验信息,而忽略了高阶图像梯度的先验信息。为了进一步提高恢复图像的 质量,提出了一种结合 Lp 伪范数的高阶 OGS 全变分,在利用高阶梯度的 OGS 约束更好地描述图像梯度稀疏 先验的同时,还利用 Lp 伪范数的强稀疏诱导能力更好地描述椒盐噪声的稀疏性。该模型采用交替方向乘子法 求解,并将模型分解为若干个子问题求解。最后,通过实验验证了该模型的正确性,并结合峰值信噪比、结构 相似性度和梯度幅值相似性偏差对模型的恢复性能进行了评价。实验结果表明,该方法相比一些先进的去噪模 型具有很强的竞争力。     

基于方向中值的图像椒盐噪声检测算法

为了在有效去除椒盐噪声的同时最大限度地保持图像的细节,针对现有应用于椒盐噪声检测算法的优缺点,提出一种基于方向中值的椒盐噪声两级检测算法。算法通过初级全局噪声检测将图像分为可疑噪声点与信号点,二级检测中算法以可疑噪声点为中心在5×5的检测窗口中设置9个方向检测区,通过可疑噪声点灰度值与检测区像素点灰度中值的比较最终确定噪声点的位置。算法中的可行性漏检在保证图像质量的同时减少了后续处理的像素数,同时,算法具有较低的噪声误检率,保持了图像的细节。仿真实验结果验证了算法的有效性。     

图像椒盐噪声的自适应滤波算法研究

为有效去除严重的椒盐噪声、更好地保护图像细节,提出了一种基于改进脉冲耦合神经网络（PCNN）的自适应去噪方法。根据PCNN神经网络的点火时刻矩阵,对受噪声污染的像素进行定位,仅对噪声像素进行类中值滤波,实现了图像细节的有效保留;根据噪声强度的估计信息,自动进行滤波次数和滤波窗口尺寸的优选,实现了图像的强自适应滤波。实验表明,与传统去噪方法相比,该方法噪声去除效果好,图像细节保持完整,而且系统具有一定的泛化能力。     

基于自适应开关插值算法的图像椒盐噪声滤波*

针对传统中值滤波算法在滤除椒盐噪声时的缺点,提出了一种自适应开关插值算法。该方法根据椒盐噪声的特点,通过极大值、极小值和块均匀度检测来标志噪声,然后根据噪声分布情况,利用拉格朗日插值和自适应中值滤波来滤除噪声。实验结果表明,该方法对椒盐噪声密度为10%80%的测试图像,能更加有效地抑制椒盐噪声并很好地保持了图像的细节信息,滤波性能比传统中值滤波方法更理想。该方法为图像去噪提供了一种新的途径。     

基于改进BM3D算法的椒盐噪声去噪

在处理由椒盐噪声污染的高对比度图像时,使用传统的三维块匹配算法（Block-Matching and 3D filtering,BM3D）去噪不能有效保留图像的边缘和纹理细节,在图像的边缘会出现边缘振铃效应。为了改善传统BM3D算法在处理椒盐噪声时的不足,提出了用边缘方向代替水平方向搜索相似块的BM3D改进去噪算法。实验结果表明,改进BM3D算法获得的相似块数量是传统BM3D算法的3倍,峰值信噪比（PSNR）也得到进一步提高,在去除椒盐噪声的同时也使图像边缘得到有效保留。     

一种针对椒盐噪声的高速自适应中值滤波算法

大数据量高清视频流在拍摄、传输等过程中可能受到干扰而产生椒盐噪声.由于其具有数据传输速度快的特点,为了确保它的实时性,进一步提高滤波算法的时间效率和计算效率,对现有的自适应中值滤波进行了改进,提出了一种高速自适应中值滤波算法.滤波过程主要分为噪声点检测和噪声去除两个阶段.其中,在噪声点检测阶段,根据椒盐噪声的极值特性,将图像的像素点分为噪声点和信号点;在噪声去除阶段,信号点保持原值,噪声点根据自适应中值进行赋值.实验结果表明,该算法相较于多种中值滤波方法具有很好的滤波作用,以及很大的速度提升.     

基于加权超图和IOWA算子的图像椒盐噪声滤除算法

针对图像椒盐噪声,提出基于加权超图和诱导有序加权平均(IOWA)算子的椒盐噪声滤除算法.首先,用加权超图对图像进行表示,根据椒盐噪声为极值的特点,定义超图边的权值,该权值能够反映边内中心节点对应像素为噪声点的可能性,进而利用超图边的权值进行噪声检测;其次,构建IOWA算子对噪声点进行复原,并采用噪声检测与复原交替进行的方式完成图像的椒盐噪声滤除.仿真实验结果表明,所提出的算法不但可有效复原椒盐噪声,而且能保持原图像的轮廓等细节信息.     

Salt and pepper noise removal in surveillance video based on low-rank matrix recovery

Computational Visual Media - This paper proposes a new algorithm based on low-rank matrix recovery to remove salt & pepper noise from surveillance video. Unlike single image denoising...     

基于改进型中值滤波的高密度椒盐噪声图像去噪算法研究

针对目前已有滤波算法对高密度椒盐噪声降噪能力较低的问题,提出了一种基于改进型中值滤波的算法。该算法在自适应中值滤波与斜率差值的基础上,采用图像局部均值与方差的方式对噪声点进行预判定,并对图像边缘进行二次邻域均值滤波。实验结果表明,该算法能够有效去除高密度椒盐噪声,并且能较好地保留细节信息。     

神经网络噪声检测的自适应加权均值滤波算法

针对传统滤波方法对纹理比较细腻的图像以及高噪声密度图像的处理能力欠佳的缺陷,提出了一种基于BP神经网络噪声检测的自适应加权均值滤波方法.用训练好的BP神经网络检测出图像中被椒盐噪声污染的像素并对其进行标记,对检测出的噪声点进行自适应加权均值滤波,信号点则保持不变,从而实现了对图像细节的有效保护.仿真表明了该算法滤波性能和细节保护能力均优于各种传统滤波算法.     

Piecewise monotone filtering in discrete-time with smallobservation noise

A discrete-time model for filtering with small observation noise is considered. A piecewise linear observation function is considered with two intervals of monotonicity. A sequential quadratic variation test is found to detect intervals of linearity of the observation function. Diffusion approximations to certain discrete processes are made to estimate the mean times for reaching a decision and the error probabilities     

图像脉冲噪声滤波算法

针对低噪声污染图像提出了一种改进的中值滤波算法.该算法通过计算滑动窗口内的像素均值和方差,根据数理统计特性,自适应选定阈值,对符合噪声条件的像素进行初次滤除,然后采用开关中值滤波算法对不符合条件的像素再次滤波.实验结果表明,该算法既能有效地去除噪声,又能清晰地保持图像边缘,降低了传统改进型中值滤波算法对阚值的依赖性和对图像边缘细节的损害程度,且滤波性能优于一些典型改进型中值滤波算法.     

椒盐图像的方向加权均值滤波算法

椒盐噪声是造成图像污染的主要因素之一,椒盐去噪是图像去噪领域的研究热点。方向加权中值滤波算法计算噪声点滤波输出时存在一定的问题,比如,未排除近邻噪声点的干扰,对方向的估计不准确,对局部灰度特性刻画不完整等。为此,提出一种方向加权均值滤波算法。此算法先根据方向灰度差异和灰度极值判断检测噪声点,然后根据对局部窗口噪声强度的估计自适应地选择递归或非递归滤波窗口的加权灰度均值作为滤波输出。仿真实验结果表明,提出的算法与现有的两种方向加权中值滤波算法相比,PSNR普遍提高了2~3dB和5~6dB,噪声密度高时提高的幅度更加明显;速度提高了接近10倍和30倍。     

Kalman filtering in extended noise environments

This note introduces an extended environment for Kalman filtering that considers also the presence of additive noise on input observations in order to solve the problem of optimal (minimal variance) estimation of noise-corrupted input and output sequences. This environment includes as subcases both errors-in-variables filtering (optimal estimate of inputs and outputs from noisy observations) and traditional Kalman filtering (optimal estimate of state and output in presence of state and output noise). A Monte Carlo simulation shows that the performance of this extended filtering technique leads to the expected minimal variance estimates.     

Measurement noise filtering for PID controllers

Measurement noise can generate undesired control activity resulting in wear of actuators and reduced performance. The effects of measurement noise can be alleviated by filtering the measurement signal. The design of the filter is then a trade-off; heavy filtering reduces the undesired control activity but performance is degraded. In this paper we discuss the trade-offs for PID control. Based on the insight gained we introduce two quantities that characterize the effect of measurement noise the SDU, which is a measure of noise activity analog to the IAE commonly used to characterize load disturbance response, and the noise gain k_n, which tells how fluctuations in the filtered measurement signal are reflected in variations of the control signal. Simple rules for choosing the filter time constant for PI and PID controllers are also given. The results are illustrated by simulations and lab experiments.     

Adaptive-hierarchical filtering approach for noise removal

In this paper, our contribution is to propose an adaptive-hierarchical filter that can remove the impulse noise while preserving the details in an image. The global image structure, which is estimated from a set of pyramid images, can be used as prior information in order to apply different filters adaptively. The proposed filter outperforms other methods in that it can make use of both local and global information efficiently. Experimental results show that the proposed method produces better performance than many other well-known methods do.     

Optimal filtering for polynomial system states with polynomial multiplicative noise

In this paper, the optimal filtering problem for polynomial system states with polynomial multiplicative noise over linear observations is treated proceeding from the general expression for the stochastic Ito differential of the optimal estimate and the error variance. As a result, the Ito differentials for the optimal estimate and error variance corresponding to the stated filtering problem are first derived. The procedure for obtaining a closed system of the filtering equations for any polynomial state with polynomial multiplicative noise over linear observations is then established, which yields the explicit closed form of the filtering equations in the particular cases of a linear state equation with linear multiplicative noise and a bilinear state equation with bilinear multiplicative noise. In the example, performance of the designed optimal filter is verified for a quadratic state with a quadratic multiplicative noise over linear observations against the optimal filter for a quadratic state with a state‐independent noise and a conventional extended Kalman–Bucy filter. Copyright © 2006 John Wiley & Sons, Ltd.     

Intelligent noise detection and filtering using neuro-fuzzy system

In this paper, we propose a neuro-fuzzy based blind image restoration to remove impulse noise from low as well as highly corrupted images. Main components of the proposed technique include noise detection, histogram estimation and noise filtering process. Proposed technique constructs the fuzzy sets using fuzzy number construction algorithm. These fuzzy sets are used in noise filtering process to remove impulse noise from the noisy pixels using neuro-fuzzy inference system and fuzzy decider. Experimental results are based on global and local error measures, which prove that the proposed technique gives superior results than the present well known impulse noise filtering methods.