Adaptive filtering of electroencephalogram signals using the empirical-modes method

A new method for the removal of physiological artifacts in the experimental signals of human electroencephalograms (EEGs) has been developed. The method is based on decomposition of the signal in terms of empirical modes. The algorithm involves EEG signal decomposition in terms of empirical modes, searching for modes with artifacts, removing these modes, and restoration of the EEG signal. The method was tested on experimental data and showed high efficiency in the removal of various physiological artifacts in EEGs.     

Adaptive video filtering framework

We present a new 3D adaptive filtering approach capable of detecting and removing impulsive noise in image/video sequences. The proposed method takes advantage of switching median schemes and robust lower‐upper‐middle (LUM) smoothing characteristics. Simulation studies reported in this article indicate that the proposed filtering scheme achieves an excellent trade‐off between noise attenuation and detail preserving characteristics, and clearly outperforms previously introduced approaches in terms of subjective and objective image quality measures. Besides the filter analysis and the testing of its performance, an important part of this article discusses the filter implementation in Altera field programmable logic devices (FPLD). Simulation studies indicate that the proposed method can be efficiently implemented in hardware and is suitable for real‐time image/video processing applications. © 2005 Wiley Periodicals, Inc. J Imaging Syst Technol 14, 223–237, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20027     

Fringe filtering technique based on local signal reconstruction using noise subspace inflation

A noise filtering technique is proposed to filter the fringe pattern recorded in the optical measurement set-up. A single fringe pattern carrying the information on the measurand is treated as a data matrix which can either be complex or real valued. In the first approach, the noise filtering is performed pixel-wise in a windowed data segment generated around each pixel. The singular value decomposition of an enhanced form of this data segment is performed to extract the signal component from a noisy background. This enhancement of matrix has an effect of noise subspace inflation which accommodates maximum amount of noise. In another computationally efficient approach, the data matrix is divided into number of small-sized blocks and filtering is performed block-wise based on the similar noise subspace inflation method. The proposed method has an important ability to identify the spatially varying fringe density and regions of phase discontinuities. The performance of the proposed method is validated with numerical and experimental results.     

Rician noise removal in magnitude MRI images using efficient anisotropic diffusion filtering

In this article, a new methodology for denoising of Rician noise in Magnetic Resonance Images (MRI) is presented. MRI imaging creates a distinctive view into the interior of a human body and has become an essential tool of clinical diagnosis. However, Rician noise is a type of artifact inherent to the acquisition process of the magnitude MRI image, making diagnosis difficult. We proposed a moment‐based Rician noise reduction technique in anisotropic diffusion filtering. We extend the work of the classical anisotropic diffusion filter and have customized it to remove Rician noise in the magnitude MRI image in 3D domain space. Our proposed scheme shows better results against various quality measures in terms of noise removal and edge preservation while retaining fine textures.     

Removal of noise in MRI images using a block difference-based filtering approach

Magnetic resonance imaging (MRI) images are frequently sensitive to certain types of noises and artifacts. The denoising of MRI images is essential for improving visual quality and reliability of the quantitative analysis of diagnosis and treatment. In this article, a new block difference-based filtering method is proposed to denoise the MRI images. First, the normal MRI image is degraded by a certain percentage of noise. The block difference between the intensity of the normal and noisy MRI is computed, and then it is compared with the intensity of the blocks of the normal MRI image. Based on the comparison, the pixel weights are updated to each block of the denoised MRI image. Observational results are brought out on the BrainWeb and BraTS datasets and evaluated by performance metrics such as peak signal-to-noise ratio, structural similarity index measures, universal quality index, and root mean square error. The proposed method outperforms the existing denoising filtering techniques.     

Linear filtering with Ornstein-Ulhenbeck process as noise

We consider a linear filtering model (with feedback) when the observation noise is an Ornstein-Ulhenbeck (OU) process with parameter β. The coefficients appearing in the model are all assumed to be bounded. In addition, the coefficients appearing in the observation equation are also assumed to be differentiable. We consider the general case when the OU noise is also correlated with the signal. Under these conditions, we derive the filtering equations for the optimal filter.     

Optimal reduction of noise in image processing using collaborative inpainting filtering with Pillar K-Mean clustering

Digital image processing is a mechanism for analysing and modifying the image in order to improve the quality and also to manage the unwanted involvement of noises. In image processing, noise is characterized as an unwanted disturbance which occurs while capturing the actual image thus affecting the quality of the image. Hence, noise formation is considered as a perilous issue and the reduction of noise is considered as an awkward process. Nowadays, almost in all fields of science and technology, digital image processing is increasing rapidly, so there arises the need for de-noising to cure the noised image. The main objective of this paper is to overcome the issue of noise and also to increase the quality and pixel value of the image. An advanced methodology known as collaborative filtering and Pillar K-Mean clustering is discussed in this paper to overcome the abovementioned problem. Initially, distinct pure images are taken as the dataset and three types of noises are added to the corresponding image to make it as a noised one. Hence, the unspecified noise is resolved on the basis of a hybrid combination of algorithms of collaborative filtering with the image inpainting method. Sequentially, the low-density noises, such as random noise and poison noise, are recovered by the implementation of collaborative filtering, and the high-density salt and pepper noise are recovered by the image inpainting method. Based on the GLCM (Grey Level Co-occurrence Matrix) feature, the normal image and the noised image are used for the clustering process. Then the de-noised image is evaluated to find the efficiency on the basis of few parameters such as SNR (Signal to Noise Ratio), MSE (Mean Square Error), PSNR (Peak Signal to Noise Ratio) and SSI (Structural Similarity Index). Accordingly, the evaluated images are further withstood for clustering to differentiate the noises by applying the proposed clustering methodology. Then the evaluated images are verified on the basis of a few parameters such as Silhouette Width, Davies–Bouldin Index and Dunn Index. The proposed methodology is run on the platform of Mat Lab. Finally, the proposed methodology is considered as an efficient method for settling the issue in digital image de-noising.     

Quantum filtering of noise in the Wigner function

Abstract

A filtering algorithm which takes the measured Radon transform of the Wigner function of a quantum signal with thermal noise and produces the Wigner function of the corresponding ‘clean’ (noiseless) signal, is proposed. The general method is exemplified for number eigenstates with thermal noise and also for Schrödinger cats with thermal noise.     

Adaptive-neighborhood filtering of images corrupted by signal-dependent noise

In many image-processing applications the noise that corrupts the images is signal dependent, the most widely encountered types being multiplicative, Poisson, film-grain, and speckle noise. Their common feature is that the power of the noise is related to the brightness of the corrupted pixel. This results in brighter areas appearing to be noisier than darker areas. We propose a new adaptive-neighborhood approach to filtering images corrupted by signal-dependent noise. Instead of using fixed-size, fixed-shape neighborhoods, statistics of the noise and the signal are computed within variable-size, variable-shape neighborhoods that are grown for every pixel to contain only pixels that belong to the same object. Results of adaptive-neighborhood filtering are compared with those given by two local-statistics-based filters (the refined Lee filter and the noise-updating repeated Wiener filter), both in terms of subjective and objective measures. The adaptive-neighborhood approach provides better noise suppression as indicated by lower mean-squared errors as well as better retention of edge sharpness than the other approaches considered.     

Adaptive clutter rejection filtering in ultrasonic strain-flow imaging

This paper introduces strain-flow imaging as a potential new technique for investigating vascular dynamics and tumor biology. The deformation of tissues surrounding pulsatile vessels and the velocity of fluid in the vessel are estimated from the same data set. The success of the approach depends on the performance of a digital filter that must separate echo signal components caused by flow from tissue motion components that vary spatially and temporally. Eigenfilters, which are an important tool for naturally separating signal components adaptively throughout the image, perform very well for this task. The method is examined using two tissue-mimicking flow phantoms that provide stationary and moving clutter associated with pulsatile flow.     

PCNN赋时矩阵高斯噪声滤除算法

为消除图像降噪过程中传统降噪方法对图像边缘和细节的影响,提出一种基于改进脉冲耦合神经网络(pulse coupled neural network,PCNN)赋时矩阵的有效滤除高斯噪声算法。该算法将PCNN模型的突触联结强度改进为随神经元与其周围神经元相似程度不同而变化的可变值,并将PCNN神经元的点火时间记录在赋时矩阵中,根据点火时刻判断噪声点,选择滤波方式。实验结果表明:该算法能够有效去除高斯噪声,具有较强的降噪性能及很好的边缘与细节保护能力。     

周期间隙性排气噪声滤波消声器的试验研究

本文分析了周期间隙性排气噪声的声学特性，提出对该噪声滤波消声器的要求，试验研究了不同滤波单元的滤波特性，在此基础上建立了降低该类噪声滤波消声器总成结构，并对其进行了台架及人工实际使用验证，证明该滤波消声器具有较高的实际应用与推广价值。     

海洋合成孔径雷达图像斑点噪声的滤除

针对海洋合成孔径雷达(SAR)图像结构信息不明显、水下目标识别困难这一问题,提出了基于经验模式分解(EMD)的算法和H(o)lder指数调整相融合的框架,该框架可以有效地滤除海洋SAR图像的斑点噪声并增强其结构信息,使得人眼可以分辨其特征信息.该融合框架利用EMD将海洋SAR图像分解成不同频率成分的分量,不同层次的分量根据其结构信息和噪声的特征用不同的H(o)lder指数来调整,H(o)lder指数的大小随着分量层数的增加而减小,即在不同尺度下分别抑制斑点噪声,从而恢复其中所包含的结构信息.试验结果表明,利用该框架可以有效抑制SAR图像中的斑点噪声和增强与水下目标相关的结构信息,使人眼可以分辨海洋SAR图像的特征结构.     

A study of noise phenomena in microwave components using an advanced noise measurement system

A novel 9 GHz measurement system with thermal noise limited sensitivity has been developed for studying the fluctuations in passive microwave components. The noise floor of the measurement system is flat at offset frequencies above 1 kHz and equal to -193 dBc/Hz. The developed system is capable of measuring the noise in the quietest microwave components in real time. We discuss the results of phase and amplitude noise measurements in precision voltage controlled phase shifters and attenuators. The first reliable experimental evidences regarding the intrinsic flicker phase noise in microwave isolators are also presented.     

Determining moisture content of wheat with an artificial neuralnetwork from microwave transmission measurements

An artificial neural network (ANN) was used to determine the moisture content of hard, red winter wheat. The ANN was trained to recognize moisture content in the range from 10.6% to 19.2% (wet basis) from transmission coefficient measurements on samples of wheat. The measurements were made at 8 microwave frequencies (10 GHz to 18 GHz) on wheat samples of varying bulk densities (0.72 g/cm³ to 0.88 g/cm³) at 24°C. The trained network predicted moisture content (%) with a mean absolute error of 0.135 (compared with oven-dried measurements)     

Adaptive and robust non-gaussian noise amplitude suppressors

Translated from Izmeritel'naya Tekhnika, No. 10, pp. 11–15, October, 1992.     

基于遗传算法的自适应噪声抵消

当有参考噪声信号时，自适应噪声抵消的实质就是求参考噪声输入通路的逆滤波器，LMS自适应滤波问题就是一个多变量函数的极值问题。LMS算法因其具有算法简单，容易实现的优点而为常用，但是算法的收敛特性和失调量受到步长参数μ的影响。而步长参数μ和最优值不易确定。遗传算法是一种应用于大规模搜索空间的有效方法，它不要求函数的解析表达式，只根据已知的测量数据便可以求得全局极值。本文以FIR滤波器为例。采用改进的实值编码遗传算法，将遗传算法用于逆滤波器的求解。计算机仿真结果表明该算法对噪声抵消取得了较满意的效果。     

Neural filtering of colored noise based on Kalman filter structure

In this paper, adaptive filtering approaches of colored noise based on the Kalman filter structure using neural networks are proposed, which need not extend the dimensions of the filter. The colored measurement noise is first modeled from a Gaussian white noise through a shaping filter. The Kalman filtering model of colored noise is then built by adopting an equivalent observation equation, which can avoid the dimension extension and complicated computations. An observation correlation-based algorithm is suggested to estimate the variance of the measurement noise by use of a single layer neural network. The Kalman gain can be obtained when a perfect knowledge of the plant model and noise variances is given. However, in some cases, the difficulties of the correlative method and the Kalman filter equations are the amount of computations and memory requirements. A neural estimator based on the Kalman filter structure is also analyzed as an alternative in this paper. The Kalman gain is replaced by a feedforward neural network whose weight adjustment permits minimization of the estimation error. The estimator has the capability of estimating the states of the plant in a stochastic environment without knowledge of noise statistics. If the noise of the plant is white and Gaussian and its statistics are well known, the neural estimator and the Kalman filter produce equally good results. The neural filtering approaches of colored noise based on the Kalman filter structure are applied to restore the cephalometric images of stomatology. Several experimental results demonstrate the feasibility and good performances of the approaches.     

Adaptive detection mechanisms in globally statistically nonstationary-oriented noise

Studies have shown that human observers can adapt their detection strategies on the basis of the statistical properties of noisy backgrounds. One common property of such studies is that the backgrounds studied are (or are assumed to be) statistically stationary. Less is known about how humans detect signals in the more complex setting of nonstationary backgrounds. We investigated detection performance in the presence of a globally nonstationary oriented noise background. We controlled for noise-correlation effects by considering a stationary background with a power spectrum matched to the average spectrum of the nonstationary process. Performance of a nonadaptive linear filter that was unable to make use of differences in local statistics yielded constant performance in both the stationary and the nonstationary backgrounds. In contrast, performance of an ideal observer that uses local noise statistics yielded substantially higher (140%) detectability with the nonstationary backgrounds than the stationary ones. Human observers showed significantly higher (33%) detection performance in the nonstationary backgrounds, suggesting that they can adapt their detection mechanisms to the local orientation properties.     

自适应串扰噪声消除技术在小孔径钻孔超声波检测中的应用

介绍了一种基于自适应滤波的噪声消除技术,分析了超声波用于小孔径钻孔检测中存在的同频串扰噪声的生成原因以及它对测量的影响,并对串扰噪声信号和待测信号进行建模;介绍了自适应噪声对消的原理,并针对应用给出了定步长自适应噪声的消除算法以及改进后的变步长自适应噪声消除算法,对两种算法在自适应学习速率上进行了比较,并给出经自适应去噪后的信号和原始信号的对比结果。