Optimum filter for detection of a target in nonoverlapping scene noise

A filter function is derived for input signals containing a target that is spatially disjoint (that is, nonoverlapping) with the input scene noise. The optimization metric is the ratio of the square of the expected value of the correlation peak to the expected value of the output signal energy. In this model the effects of the nonwhiteness of the scene noise, the nonstationarity of the scene noise that is due to the limited size of the input scene, the nonoverlapping of the target and the scene noise, and the unknown variations of the target illumination are considered. We show that, for the nonoverlapping target and the scene noise, the target window and the scene-noise window strongly influence the optimum filter function.     

Performance of minimum-mean-square-error filters for spatially nonoverlapping target and input-scene noise

Using computer simulations, we investigate the performance of a minimum-mean-square-error filter for input-scene noise that is spatially nonoverlapping (disjoint) with a target for a limited set of images. Different input-scene-noise statistics are used to test the filter performance. We show that in the presence of spatially nonoverlapping target and input-scene noise, the output of the minimummean- square-error filter has a well-defined correlation peak, small sidelobes, and a high peak-to-correlationenergy ratio compared with other widely used filters such as the classical matched filter, the phase-only filter, and the inverse filter. We also test the robustness of the minimum-mean-square-error filter to errors in noise statistics used in the filter design. We show that, for the images tested here, the performance of the minimum-mean-square-error filter is not sensitive to errors in noise statistics and the filter can detect the target even if a considerable error exists. The discrimination capability and the illumination sensitivity of the minimum-mean-square-error filter are also tested.     

Performance of an optimum receiver designed for pattern recognition with nonoverlapping target and scene noise

The design of an optimum receiver for pattern recognition is based on multiple-alternative hypothesis testing with unknown parameters for detecting and locating a noisy target or a noise-free target in scene noise that is spatially nonoverlapping with this target. The optimum receiver designed for a noise-free target has the interesting property of detecting, without error, a noise-free target that has unknown illumination by using operations that are independent of the scene-noise statistics. We investigate the performance of the optimum receiver designed for nonoverlapping target and scene noise in terms of rotation and scale sensitivity of the input targets and discrimination against similar objects. Because it is not possible in practical systems to have a completely noise-free target, we examine how the performance of the optimum receiver designed for a noise-free target is affected when there is some overlapping noise on the target. The application of the optimum receiver to binary character recognition is described. Computer simulation results are provided.     

Optimal multichannel estimation of the location of a target with nonoverlapping noise

We propose a new algorithm for estimating the location of an object in multichannel images when the noise is spatially disjointed from (nonoverlapping with) the target. This algorithm is optimal for nonoverlapping noise and for multichannel images in the maximum-likelihood sense. We consider the case in which the statistical parameters of the input scene are unknown and are estimated by observation. We assess the results for simulated images with white and Gaussian background, for a large scale of variances of the background noise, and different values of the contrast in the scene. We compare the results of this algorithm with the results obtained with two other algorithms, the optimal algorithm for monochannel nonoverlapping noise and the optimal algorithm for multichannel additive noise, and we show that in both cases improvement can be obtained. We show the efficiency of the estimation for real input scenes when the background noise is correlated clutter noise. This algorithm has the same complexity as correlation, and the improvement is obtained with no more calculation cost than with classic methods.     

Distortion-invariant fringe-adjusted joint transform correlation

A distortion-invariant joint transform correlator based on the concepts of the fringe-adjusted joint transform correlator and the synthetic discriminant function is presented. Computer-simulation results show that the proposed joint transform correlator is distortion-invariant for the target image from the training set and produces sharper correlation peaks and lower sidelobes compared with the classical joint transform correlator.     

归一化算法实现畸变不变图像识别研究

在光电混合联合变换相关器图像识别系统中,对与参考图像完全相同的真目标图像和不同的假目标图像能实现完全识别,但对于与参考图像间存在旋转和比例变化的目标图像却很难识别,甚至无法识别。由此,提出了基于图像质心和最长边的归一化图像处理新算法:根据图像像素总值求取图像质心,根据质心和图像边缘轮廓点之间的长度求出图像的最长边和旋转角度,然后根据统一标准将目标图像和参考图像调整至同一长度和旋转方向的完全相同的图像。最后给出了模拟和实验的相关结果,结果表明,该方法能实现存在旋转和比例变化的图像的畸变不变处理,从而提高了系统的识别能力。     

消除图像脉冲噪声的模糊结合滤波器

提出模糊结合滤波用于消除脉冲噪声方法。它由噪声率检测、噪声污染程度W评价和模糊结合滤波器组成。根据选择中值滤波或最大最小排除均值滤波。由W确定模糊隶属函数及模糊判决规则。建立了模糊结合滤波的数学模型。模拟实验表明,椒盐噪声概率为80%时,滤波输出的峰值信噪比为25.8dB,均方误差为171,而且能很好地保护图像细节。     

去除脉冲噪声的自适应开关中值滤波

为消除图像中的脉冲噪声,提出了自适应开关中值(ASM)滤波算法。该算法采用一种新的噪声检测方法将图像中的像素分为信号点和噪声点两类。对检测出的噪声点统计其个数并由此估算图像中的噪声密度,根据估计的噪声密度自适应确定滤波窗口尺寸,采用改进的中值滤波对检测出的噪声点进行处理;而信号点则保留其灰度值不予处理。对ASM滤波进行仿真实验,结果表明,它能在有效去除噪声的同时很好地保护图像细节,较传统中值滤波及其它改进中值滤波算法有更优的滤波性能。     

Distortion-invariant pattern recognition with Fourier-plane nonlinear filters

The use of nonlinear techniques in the Fourier plane of pattern-recognition correlators can improve the correlators' performance in terms of discrimination against objects similar to the target object, correlation-peak sharpness, and correlation noise robustness. Additionally, filter designs have been proposed that provide the linear correlator with invariance properties with respect to input-signal distortions and rotations. We propose simple modifications to presently known distortion-invariant correlator filters that enable these filter designs to be used in a nonlinear correlator architecture. These Fourier-plane nonlinear filters can be implemented electronically, or they may be implemented optically with a nonlinear joint transform correlator. Extensive simulation results are presented that illustrate the performance enhancements that are gained by the unification of nonlinear techniques with these filter designs.     

QR分解的最小二乘格型自适应滤波算法在噪声主动控制中应用

在噪声主动控制系统中?,滤波-x递归最小二乘(FxRLS)算法收敛速度快但计算量大。本文提出了格型联合估计滤波器结构与基于QR分解的最小二乘格型(QRD-LSL)自适应滤波算法相结合的噪声控制方法,该方法对联合估计过程进行了改进并得到了基于各阶估计误差的联合过程估计权系数更新关系,格型联合估计器结构简单,QRD-LSL自适应滤波算法数值稳定性好。仿真结果表明本文提出的噪声控制方法有良好的噪声控制效果,收敛速度快,计算量小,稳态误差小,跟踪性能好。     

基于通用切比雪夫滤波器的有源噪声控制研究

针对前馈管道非线性有源噪声控制系统,提出一种基于通用切比雪夫滤波器的次级通道建模方法和通用切比雪夫滤波x最小均方误差算法(GCFXLMS,general Chebyshev filtered-x least mean square)。通用切比雪夫滤波器由第一类切比雪夫滤波器扩展获得,交叉项部分可通过对角结构实现,根据对角结构的性质,可以采用减少通道信号的实现策略以降低结构复杂度;使用该滤波结构建模次级通道,并给出了稀疏虚拟次级通道模型,基于此模型推导了GCFXLMS算法。该方法性能比较包括计算复杂度对比和控制效果对比。实验结果表明,在非线性有源噪声控制系统中,通用切比雪夫滤波器可达到与Volterra次级通道建模类似的建模效果,相比于传统的前馈滤波器,通用切比雪夫滤波器具有更优的控制性能。     

基于LSM-Kalman滤波器的湍流噪声抑制

针对大气湍流效应所引起的光无线通信系统中的衰落问题,建立了与大气湍流噪声及其它加性噪声有关的信号模型。根据最大似然比准则,推导了强度调制/直接探测(IM/DD)的光无线通信系统的最佳判决门限。采用线性状态模型的自适应Kalman滤波器和同态滤波器实现了对探测门限中所含信号和湍流的统计值的预测,可以在不同的信噪比条件下实现自适应门限检测,对大气湍流噪声有很好的抑制作用,信噪比在10~20dB时,可以降低系统的误码率到10-5以下。     

干式空心滤波电抗器降噪技术研究与实施

针对干式空心电抗器的运行噪声问题,研究总结出一系列的降噪解决方案,并分别予以试制和试验验证.研究过程首先对干式空心电抗器噪声产生机理进行了研究,然后根据其噪声特点分别提出五种降噪措施,通过试品试制、试验验证及分析,不断改进设计方案并完成低噪声干式空心电抗器的设计.最终通过对该低噪声电抗器的性能试验测试,验证了此项降噪方...     

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.     

基于Volterra自适应滤波器的发动机运转噪声预测

摘 要：发动机运转噪声的预测对于减小发动机运转噪声干扰、提高发动机异响故障诊断正确率具有重要意义。基于相空间重构理论,采用三阶Volterra自适应滤波器,提出了两种分别采用双传感器、单传感器的发动机运转噪声建模与预测方法。所提方法有效利用了Volterra级数强大的非线性映射能力,充分考虑了发动机运转噪声内在的循环相关性、周期及相位信息等先验知识。试验结果表明,所提方法具有较好的鲁棒性,其精度可以满足实际应用要求。     

Localized Fourier transform filter for noise removal in electronic speckle pattern interferometry wrapped phase patterns

This article is concerned with frequency filtering for electronic speckle pattern interferometry wrapped phase patterns. We propose a robust localized Fourier transform filter which is an extension of the root filtering method (RFM). We improve the RFM from a simple technical process and a filter function in the frequency domain. In our method, the proposed filter function is taken as the power spectrum of the convolution of an image and a Gaussian function to the power α. We test the proposed method on two computer-simulated wrapped phase fringe patterns and one experimentally obtained wrapped phase pattern, and compare our models with the widely used, well-known RFM and windowed Fourier filtering (WFF). The experimental results have demonstrated that our localized Fourier transform filter outperforms the RFM and is comparable to WFF. Our method depends on fewer parameters, as compared with WFF, and can achieve a better balance between the computational complexity and the filtered results.     

Optical correlation of images with signal-dependent noise using constrained-modulation filter devices

Images with signal-dependent noise present challenges beyond those of images with additive white or colored signal-independent noise in terms of designing the optimal 4-? correlation filter that maximizes correlation-peak signal-to-noise ratio, or combinations of correlation-peak metrics. Determining the proper design becomes more difficult when the filter is to be implemented on a constrained-modulation spatial light modulator device. The design issues involved for updatable optical filters for images with signal-dependent film-grain noise and speckle noise are examined. It is shown that although design of the optimal linear filter in the Fourier domain is impossible for images with signal-dependent noise, proper nonlinear preprocessing of the images allows the application of previously developed design rules for optimal filters to be implemented on constrained-modulation devices. Thus the nonlinear preprocessing becomes necessary for correlation in optical systems with current spatial light modulator technlogy. These results are illustrated with computer simulations of images with signal-dependent noise correlated with binary-phase-only filters and ternary-phase-amplitude filters.     

A novel learning-based switching median filter for suppression of impulse noise in highly corrupted colour images

A novel switching median filter integrated with a learning-based noise detection method is proposed for suppression of impulse noise in highly corrupted colour images. Noise detection employs a new machine learning algorithm, called margin setting (MS), to detect noise pixels. MS detection is achieved by classifying noise and clean pixels with a decision surface. MS detection yields very high detection accuracy, i.e. a zero miss detection rate and a fairly low over detection rate for a wide range of noise levels. After noise detection, a new filter scheme called the noise-free two-stage (NFTS) filter is triggered. NFTS corrects the noise pixels using the median of the noise-free pixels in two stages. The results of experiments have demonstrated that the MS based NFTS (MSN) filter is superior to the support vector machine and neural network for denoising highly corrupted images, in terms of noise suppression and detail preservation.     

Optimal measurement of signal over noise ratio with constrained filter transfer functions

The problem of optimal measurement of a signal in presence of noise is treated in detail by Baldinger and Franzen (Adv. Electron. Electron Phys. 8 (1956) 225), Radeka and Karlovac (Nucl. Instr. and Meth. 52 (1967) 86) and Gatti and Manfredi (La Rivista Nuovo Cimento 9(1) (1986) 1), and the filter transfer function optimizing the signal over noise ratio is well known. These calculations deals with unconstrained optimization, that is the filter transfer function may assume any value. In this paper functional analysis techniques are applied to optimize the filter transfer function in presence of linear constraints.     

脉冲噪声下基于Myriad滤波及分形盒维数的频谱感知

为了实现脉冲噪声背景下频谱感知,本文提出基于Myriad滤波及分形盒维数的频谱感知方法。Alpha稳定分布噪声影响信号的分形盒维数值。由于Myriad滤波可以抑制Alpha稳定噪声,所以先对接收信号Myriad滤波,再计算其分形盒维数值作为检验统计量。仿真结果表明,该方法在Alpha稳定分布噪声下具有良好的频谱感知性能