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.     

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.     

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.     

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.     

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.     

An experimental study of noise and asynchrony in elementary cellular automata with sampling compensation

This article focuses on the set of 32 legal elementary cellular automata (ECA). We perform an exhaustive study of the systems’ response under: (i) α-asynchronous dynamics, from full asynchronism to perfect synchrony, (ii) κ-scaling, which extends α-asynchrony to compensate for less cell activity, and (iii) ?-noise scheme, a perturbation that affects the local transition function and causes a cell to probabilistically miscalculate the new state when it is updated. We propose a new classification in three classes under asynchronous conditions: α-invariant, α-robust, and α-dependent. We classify the 32 legal ECA according to the degree of behavioural modification, and we show that our classifying scheme provides results coherent with the density-based classification. We also show that κ-scaling provides results comparable to synchronous systems, both quantitatively and qualitatively. Subsequently, we analyse the effects of including different levels of noise in synchronous systems. We identify different responses to noise, including systems that are robust to asynchrony and susceptible to noise. To conclude, we investigate the behavioural changes caused by simultaneous asynchrony and noise in models tolerant to both perturbations. We describe a number of effects caused by the interplay of noise and asynchrony, thus further reinforcing that both aspects are pertinent for future studies.     

基于二维细胞自动机和中值运算的图像滤波方法

为滤除手写汉字识别图像预处理中二值图像上的噪声点,结合中值滤波和细胞自动机滤波方法,提出一种二值图像CA-M（Cellular Automata—Median）滤波方法,并给出具体的算法流程．利用该方法进行滤波去噪实验并计算算法效率,对二值图像去噪非常有效．     

有效去除图像混合噪声的方法

脉冲耦合神经网络PCNN具有良好的脉冲传播特性,可以用于图像的边缘检测;多级中值滤波是对中值滤波的改进,具有较好的保护图像细节的作用;均值滤波是一种成熟的能较好滤除图像高斯噪声的方法.该算法充分结合上述3种算法的优点,先运用PCNN算法对被混合噪声干扰的图像进行边缘检测,然后利用多级中值滤波和均值滤波方法进行滤波处理.对不同的混合噪声进行滤除的实验结果表明本文算法较中值滤波和均值滤波方法有较大提升.     

基于PCNN的高斯噪声滤波

论文针对高方差的高斯噪声的特点,提出了一种先定位和去除大噪声像素,后平滑小噪声像素的滤波方法。文中采用类均值滤波方法去除大噪声像素,利用改进的PCNN平滑小噪声像素。与已有的滤波方法相比,该算法在较好地滤除噪声的同时,具有自适应和图像边缘保护能力。实验结果证实了该方法的可行性和有效性。     

Quantum and impulse noise filtering from breast mammogram images

Recent advances in the field of image processing have shown that level of noise highly affect the quality and accuracy of classification when working with mammographic images. In this paper, we have proposed a method that consists of two major modules: noise detection and noise filtering. For detection purpose, neural network is used which effectively detect the noise from highly corrupted images. Pixel values of the window and some other features are used as feature for the training of neural network. For noise removal, three filters are used. The weighted average value of these three filters is filled on noisy pixels. The proposed technique has been tested on salt & pepper and quantum noise present in mammogram images. Peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) are used for comparison of proposed technique with different existing techniques. Experiments shows that proposed technique produce better results as compare to existing methods.     

White noise theory of robust nonlinear filtering with correlated state and observation noises

In the existing ‘direct’ white noise theory of nonlinear filtering, the state process is still modelled as a Markov process satisfying an Itô stochastic differential equation, while a ‘finitely additive’ white noise is used to model the observation noise. We remove this asymmetry by modelling the state process as the solution of a (stochastic) differential equation with a ‘finitely additive’ white noise as the input. This enables us to introduce correlation between the state and observation noises, and to obtain robust nonlinear filtering equations in the correlated noise case.     

基于模糊推理的方向中值椒盐滤波算法

针对灰度图像中的椒盐噪声,提出了一种基于模糊逻辑推理的方向中值滤波算法.该算法先利用椒盐噪声的特点,将图像像素点分为信号像素点和噪声像素点,然后利用模糊推理在4个方向上推理出最接近理想值的非噪声点代替当前噪声点.同时算法中采用一种简便的方法检测出噪声点所处的滑动窗口中的边和线.仿真结果表明,该算法能在有效抑制噪声的同时较好地保存图像的细节信息.     

Unscented Kalman filtering in the additive noise case

The unscented Kalman filter(UKF) has four implementations in the additive noise case,according to whether the state is augmented with noise vectors and whether a new set of sigma points is redrawn from the predicted state(which is so-called resampling) for the observation prediction.This paper concerns the differences of performances for those implementations,such as accuracy,adaptability,computational complexity,etc.The conditionally equivalent relationships between the augmented and non-augmented unscente...     

多模噪声中粒子滤波算法研究

粒子滤波是一种解决非高斯滤波问题的有效方法,受到许多领域的研究人员的重视。在扩展卡尔曼滤波(EKF)的基础上,提出一种基于多层感知器(MLP)的扩展卡尔曼滤波算法。利用扩展卡尔曼粒子滤波器和MLP对当前时刻状态重要性采样,引入MLP对样本进行重采样。该算法能有效利用测量值的最新信息,对状态估计的误差更小。在实验中,对于多模噪声非线性系统,该算法与另外算法进行比较。结果证明,所提算法性能优异于其他算法。