Noise reduction based on iterative hybrid vector filter and detail correction employing correlation among RGB components

One of the important image processing tasks is to effectively reduce a noise from a digital image while keeping its features intact. In this paper, a new noise removal method for color images corrupted by the mixture of the impulsive and Gaussian noises is proposed. In the proposed method, firstly, a tentative output image, in which the noise is removed almost perfectly, is obtained by using the iterative robust switching vector median-based vector $\epsilon \mathrm{-}\mathrm{filter}$, which is realized by hybridizing the robust switching vector median filter and the vector $\epsilon \mathrm{-}\mathrm{filter}$ and is newly proposed here. Then the residual components between the input and the tentative output images are calculated, and image components constituting edges, corner and other image details are extracted from the residual components by using the correlation characteristic in RGB components. Consequently, a final output is obtained by adding the extracted image components into the tentative output image. The effectiveness and the validity of the proposed method are verified by some experiments using the natural color images.     

Discrete time robust detection of stochastic signals in non-Gaussian contaminated noise

For many applications in signal detection, imprecise knowledge of the underlying noise process often makes desirable the employment of a robust detector. In this paper we consider the discrete time detection of stochastic signals in white noise, where the univariate noise density is known perfectly only on an interval about the origin. We present a method to enhance the asymptotic performance of the detector by exploiting this knowledge, and at the same time preserve robustness properties of the detector to the remaining inexact knowledge of the univariate noise density via a saddlepoint condition. We then provide examples to show that improved performance is indeed obtained.     

Combined state and parameter estimation for a bilinear state space system with moving average noise

This paper considers the identification problem of bilinear systems with measurement noise in the form of the moving average model. In particular, we present an interactive estimation algorithm for unmeasurable states and parameters based on the hierarchical identification principle. For unknown states, we formulate a novel bilinear state observer from input-output measurements using the Kalman filter. Then a bilinear state observer based multi-innovation extended stochastic gradient (BSO-MI-ESG) algorithm is proposed to estimate the unknown system parameters. A linear filter is utilized to improve the parameter estimation accuracy and a filtering based BSO-MI-ESG algorithm is presented using the data filtering technique. In the numerical example, we illustrate the effectiveness of the proposed identification methods.     

Finite impulse response filter based fault estimation with computational efficiency for linear discrete time-varying systems subject to multiplicative noise

The robust fault estimation problem for linear discrete time-varying (LDTV) systems subject to multiplicative noise is investigated by means of finite impulse response (FIR) filter. A novel analytical redundancy, expressed via all states of the previous time window, is originally established to construct the fault estimator. To ensure the satisfactory fault estimation accuracy in stochastic sense under the interference of random uncertainty, a new performance index in forms of matrix trace function is proposed. An easy-to-check necessary and sufficient condition is presented to obtain the optimal filter gain via minimizing the performance index at each time instant. It is analytically demonstrated that, the newly proposed fault estimation algorithm enjoys obvious computational advantages in updating the filter gain, especially as the length of the time window increases for time-varying systems. Simulation results are finally provided to verify its feasibility and superiority.     

Non-parametric adaptive locally asymptotically optimum detection in additive noise

In this paper, a new approach to non-parametric signal detection with independent noise sampling is presented. The present approach is based on the locally asymptotically optimum (LAO) methodology, which is valid for vanishingly small signals and very large sample sizes, and on semi-parametric statistics. Its unique feature and essential difference from other techniques is that LAO non-parametric detectors are optimum according to the Neyman-Pearson criterion by being asymptotically uniformly most powerful at false alarm level α (AUMP (α)) and adaptive in the sense that no loss in Fisher's information number is incurred when the underlying noise process is no longer parametrically defined. Accordingly, they are robust against deviations from the postulated noise model and, unlike other non-parametric detectors, are distribution-free under both hypotheses H₀ (“noise only present”) and H₁ (“signal and noise present”). Non-parametric LAO detectors are derived from an asymptotic stochastic expansion of the log-likelihood ratio for coherent and narrowband incoherent “on-off” signals. Moreover, under the present framework it is shown that, in direct contrast to already known results, the non-parametric sign detector is AUMP (α) and adaptive even for non-constant signal samples.     

Model recovery for multi-input signal-output nonlinear systems based on the compressed sensing recovery theory

This paper considers the parameter and order estimation for multiple-input single-output nonlinear systems. Since the orders of the system are unknown, a high-dimensional identification model and a sparse parameter vector are established to include all the valid inputs and basic parameters. Applying the data filtering technique, the input-output data are filtered and the original identification model with autoregressive noise is changed into the identification model with white noise. Based on the compressed sensing recovery theory, a data filtering-based orthogonal matching pursuit algorithm is presented for estimating the system parameters and the orders. The presented method can obtain highly accurate estimates from a small number of measurements by finding the highest absolute inner product. The simulation results confirm that the proposed algorithm is effective for recovering the model of the multiple-input single-output Hammerstein finite impulse response systems.     

一种X波段低相噪频率合成器的设计

文章介绍了一种X波段频率合成器的设计方法,该方法是通过混合式频率合成方法优化M/N参数,以减小数字鉴相器、分频器等部件附加相位噪声对输出相位噪声性能的影响,使该频率合成器有低杂散、低相位噪声、小型化等特点,具有良好的工程应用价值。     

Asymptotic memoryless detection of random signals in dependent noise

Design of detectors for strong mixing signals in strong mixing noise is considered, where a large degree of dependency may occur between the signal and noise. Under the criterion of asymptotic relative efficiency, it is shown that this design reduces to determining the solution of an integral equation, where only knowledge of the second order statistics of the randon processes involved is required. In particular, if the signal is independent of the noise and has nonzero mean, the optimal detector is the same as in the known constant signal case. It is also shown that it is possible to delete several regularity conditions which may be difficult to check in practice in the slightly more restrictive case where the maximal correlation coefficients of the signal and noise tend to zero.     

基于点处理的图像脉冲噪声滤除方法研究

在分析含噪图像恢复方法和脉冲噪声概率密度函数的基础上,提出了一种基于点处理理论的脉;中噪声污染图像的恢复算法。该算法依次扫描图像的像素点,判断当前的像素点是否为脉冲噪声数据点,如果为噪声点,则计算空域滤波器的输出结果,并替代该像素点的值;否则,该像素点的值保持不变。实验结果表明,该方法基于像素点的空域处理,具有无排序操作、算法执行速度快和保持图像细节的优点。     

Variational Bayesian based adaptive PDA filter in scenarios with unknown detection probability and heavy-tailed process noise

For target tracking systems, the probability of detecting a target is difficult to determine, and the process noise often has non-Gaussian heavy-tailed characteristics owing to interference from outliers. To address the issues associated with single target tracking within clutters in scenarios with an unknown detection probability and heavy-tailed process noise, this paper presents a variational Bayesian-based adaptive probabilistic data association filter (VB-APDAF). The beta distribution, Pearson type VII distribution and multinomial distribution are used to model the detection probability, the process noise, and the association events, respectively. To guarantee the conjugation, a novel parameter estimation strategy is employed. In this strategy, the previous state is introduced in the state update process to construct the joint probability density function of parameters to be estimated and data set. The VB framework is used to estimate the target state, detection probability, and associated events. An experiment was performed under simulated conditions to demonstrate the effectiveness of the proposed filter.     

基于RGB-H-CbCr新颜色空间的肤色检测算法研究

针对以往肤色检查算法很少考虑亮度,仅靠色度信息检测肤色,忽略了由于不同人种和亮度差异而导致的肤色像素在颜色空间上的分布不同,本文提出了一种基于RGB-H-CbCr新颜色空间的肤色检测算法。该算法综合了肤色像素在RGB、YCbCr和HSV三种颜色空间上的分布特征,构建出RGB-H-CbCr新颜色空间肤色模型,来辨别肤色和非肤色点。文中利用Matlab分别对不同人种的肤色图像进行实验仿真,实验结果表明该方法能有效检测人体肤色,能有效提高肤色检测的正检率。     

Bias-compensated affine-projection-like algorithm based on maximum correntropy criterion for robust filtering

This article proposes an affine-projection-like maximum correntropy (APLMC) algorithm for robust adaptive filtering. The proposed APLMC algorithm is derived by using the objective function based on the maximum correntropy criterion (MCC), which can availably suppress the bad effects of impulsive noise on filter weight updates. But the overall performance of the APLMC algorithm may be decreased when the input signal is polluted by noise. To compensate for the deviation of the APLMC algorithm in the input noise interference environment, the bias compensation (BC) method is introduced. Therefore, the bias-compensated APLMC (BC-APLMC) algorithm is presented. Besides, the convergence of the BC-APLMC algorithm in the mean and the mean square sense is studied, which provides a constraint range for the step-size. Computer simulation results show that the APLMC, and BC-APLMC algorithms are valid in acoustic echo cancellation and system identification applications. It also shows that the proposed algorithms are robust in the presence of input noise and impulse noise.     

Optimal parameter estimation of ε-filter employing distribution distance

As the filter which can effectively remove the small amplitude noises on digital images, the $\epsilon \mathrm{-}\mathrm{filter}$ has been proposed. In order to effectively use this filter, a smoothing parameter $\epsilon \mathrm{-}\mathrm{filter}$ should be appropriately estimated before applying it. To address this problem, the authors proposed the parameter estimation method based on Hellinger distance (HD). In the method, HD between a residual signal and assumed noise distribution was evaluated, and a parameter $\epsilon$ of the $\epsilon \mathrm{-}\mathrm{filter}$ was estimated by finding the value giving minimum distance. However, the enough discussion on use of HD has not been made.In this paper, it is attempted to utilize not only the HD, but also various distribution distances in the parameter estimation, and their performances and characteristics are compared and analyzed experimentally. Furthermore, the parameter estimation method is extended to be applicable for the vector $\epsilon \mathrm{-}\mathrm{filter}$ for the color images. Consequently, through the experiments, it is shown that L1-norm or maximum norm is appropriate as the distribution distance used in the parameter estimation methods from the view points of the simplicity of the calculation and MSE performance in the filtering.     

基于图像的化工厂重度污染等级判定研究与仿真

在不同化工厂检测环境中,空气中的环境变化情况较为复杂,带有颜色特征的污染气体浓度会被迅速稀释,造成转化的像素特征强度衰减。传统算法多是基于采集到的某种像素特征强度进行污染等级的判断,一旦气体被稀释,颜色特征发生退化,检测准确率会降低。提出了一种基于像素支持向量机增量学习算法。通过灰度差分的支持向量机增量学习。建立对不同像素等级信号进行对应增强学习,克服像素衰退的弊端。实验证明,这种算法能够避免由于化工厂内气体大量扩散,造成的像素衰减的缺陷,提高了化工污染程度检测的准确率。     

An exact formula for the half-plane pull-in range of a PLL

A second-order phase-lock loop (PLL) that is based on a triangular-characteristic phase detector and imperfect-integrator loop filter is found in many applications where simplicity and economics are major considerations. For many of these applications, digital-logic-compatible reference and VCO signals are used, an exclusive-OR gate implements the phase detector, and the loop filter is constructed from passive components. When designing these loops, the half-plane pull-in range Ω₂ is of interest. Until now, this important loop parameter could only be calculated by using a computer-based technique that numerically integrated the nonlinear differential equation that describes the PLL model. This requirement/limitation is removed here by the development of an exact closed-form formula for Ω₂, the main contribution of this paper. More generally, the value of Ω₂ is dependent on the PLL phase detector characteristic that is used, be it triangular, sinusoidal, or something else. With regard to the value of Ω₂ produced, a comparison is given of two PLLs, both described by the same linear model so that the comparison is meaningful. The first PLL is based on a triangular-characteristic phase detector; the second loop is based on a sinusoidal phase detector.     

Event-triggered nonlinear filtering for networked systems with correlated noises

This paper focuses on the filtering problem for nonlinear networked systems with event-triggered data transmission and correlated noises. An event-triggered data transmission mechanism is introduced to reduce excessive measurements transmitted over a bandwidth-constrained network. Considering that process noise and measurement noise are one-step cross-correlated, an UKF-based filtering algorithm which depends on correlation parameter and trigger threshold is presented. Then sufficient conditions are established to ensure stability of the designed filter, where a critical value of the correlation parameter exists. Finally, the effectiveness of the proposed filtering algorithm is demonstrated by comparative simulations.     

Maximum correntropy criterion variational Bayesian adaptive Kalman filter based on strong tracking with unknown noise covariances

The performance of the current state estimation will degrade in the existence of slow-varying noise statistics. To solve the aforementioned issues, an improved strong tracking maximum correntropy criterion variational-Bayesian adaptive Kalman filter is presented in this paper. First of all, the inverse-Wishart distribution, as the conjugate-prior, is adopted to model the unknown and time-varying measurement and process noise covariances, then the noise covariances and system state are estimated via the variational Bayesian method. Secondly, the multiple fading-factors are obtained and evaluated to modify the prediction error covariance matrix to address the problems associated with inaccurate error estimation. Finally, the maximum correntropy criterion is employed to correct the filtering gain, which improves the filtering performance of the proposed algorithm. Simulation results show that the proposed filter exhibits better accuracy and convergence performance compared to other existing algorithms.     

Central suboptimal H∞ filtering for nonlinear polynomial systems with multiplicative noise

This paper presents the central finite-dimensional H_∞ filter for nonlinear polynomial systems with multiplicative noise, that is suboptimal for a given threshold γ with respect to a modified Bolza-Meyer quadratic criterion including the attenuation control term with the opposite sign. In contrast to the previously obtained results, the paper reduces the original H_∞ filtering problem to the corresponding optimal H₂ filtering problem, using the technique proposed in [1]. The paper presents the central suboptimal H_∞ filter for the general case of nonlinear polynomial systems with multiplicative noise, based on the optimal H₂ filter given in [31]. The central suboptimal H_∞ filter is also derived in a closed finite-dimensional form for third (and less) degree polynomial system states. Numerical simulations are conducted to verify performance of the designed central suboptimal filter for nonlinear polynomial systems against the central suboptimal H_∞ filters available for polynomial systems with state-independent noise and the corresponding linearized system.     

高分辨率SAR图像CFAR分割的改进方法

针对传统的基于韦布尔模型的恒虚警检测(CFAR)分割中误差大、精度低的缺点,提出了分割前对特定方向角样本进行垂直中值滤波、分割后采用区域生长滤波的改进方法.最后用区域间对比度和最终测量精度的分割评价准则,与传统CFAR分割和计数滤波的方法进行了比较.对运动和静止目标获取和识别(MSTAR)样本的实验结果表明,改进方法提高了分割精度,分割效果优于传统的CFAR分割方法.