Statistical algorithm for nonuniformity correction in focal-plane arrays

A statistical algorithm has been developed to compensate for the fixed-pattern noise associated with spatial nonuniformity and temporal drift in the response of focal-plane array infrared imaging systems. The algorithm uses initial scene data to generate initial estimates of the gain, the offset, and the variance of the additive electronic noise of each detector element. The algorithm then updates these parameters by use of subsequent frames and uses the updated parameters to restore the true image by use of a least-mean-square error finite-impulse-response filter. The algorithm is applied to infrared data, and the restored images compare favorably with those restored by use of a multiple-point calibration technique.     

Contourlet变换和遗传算法相结合的沥青红外图像增强方法

沥青施工过程中,采集的红外图像容易受到周围环境噪声的影响,使图像变得模糊、信噪比低,从而导致后续图像处理分析的准确度降低。针对该噪声特性,提出了一种Contourlet变换和遗传算法相结合的红外图像增强方法。首先对原始红外图像进行Contourlet变换,得到带有多尺度、多方向信息的带通子带,然后对其进行模糊增强,并通过自适应遗传算法优化模糊增强参数,最后对增强后的带通子带进行Contourlet逆变换,得到效果增强的红外图像。实验结果表明,与其它几种常用的红外图像增强方法相比,此方法能更有效地抑制噪声,提高清晰度,取得了较好的增强效果。     

Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors

We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.     

Two-dimensional restoration of motion-degraded intensified CCD imagery

A Wiener filter-based deconvolution algorithm is developed to restore vibration-degraded video imagery from an intensified CCD camera. The method is based on the use of azimuth and elevation angular optical line-of-sight data recorded from external sensors to estimate a two-dimensional vibration-blur impulse response on a per frame basis. Flight conditions are reproduced in the laboratory by use of prerecorded in-flight vibration data. The performance of the algorithm varies from frame to frame, following the time-varying characteristics of the vibration-blur impulse response. However, real-time display of the restored video minimizes these effects because of eye integration, and near-full restoration of the original uncorrupted imagery is observed for both high-light- and low-light-level conditions with minimal amplification of noise.     

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss-Markov process. The proposed Gauss-Markov framework provides a mechanism for capturing the slow and random drift in the fixed-pattern noise as the operational conditions of the sensor vary in time. With a temporal stochastic model for each detector's gain and bias at hand, a Kalman filter is derived that uses scene data, comprising the detector's readout values sampled over a short period of time, to optimally update the detector's gain and bias estimates as these parameters drift. The proposed technique relies on a certain spatiotemporal diversity condition in the data, which is satisfied when all detectors see approximately the same range of temperatures within the periods between successive estimation epochs. The performance of the proposed technique is thoroughly studied, and its utility in mitigating fixed-pattern noise is demonstrated with both real infrared and simulated imagery.     

Spatially adaptive iterative algorithm for the restoration of astronomical images

This article develops an iterative spatially adaptive regularized image restoration algorithm. The proposed algorithm is based on the minimization of a weighted smoothing functional. The weighting matrices are defined as functions of the partially restored image at each iteration step. As a result, no prior knowledge about the image and the noise is required, but the weighting matrices as well as the regularization parameter are updated based on the restored image at every step. Conditions for the convexity of the weighted smoothing functional and for the convergence of the iterative algorithm are established for a unique global solution which does not depend on initial conditions. Experimental results are shown with astronomical images which demonstrate the effectiveness of the proposed algorithm.     

多普勒计程仪的数据降噪算法

多普勒计程仪输出的船速数据中含有偏差较大的点,即野点,在数据处理时必须将其去除,否则可能会导致组合导航系统中的卡尔曼滤波发散。同时输出数据由于受到随机误差的影响,会导致数据的平滑性能变差。提出了一种多普勒计程仪的数据降噪算法,该算法首先利用改进的中值滤波方法去除数据中的野点,再利用小波阈收缩去噪算法去除随机误差。仿真结果表明,与传统的中值滤波相比,该算法能极大地提高处理增益,并且有很高的应用价值。     

水声信号动态阈值正交匹配追踪降噪方法

为了对实时采集的水声信号进行数据压缩的同时实现信号降噪,提出了一种动态阈值正交匹配追踪方法(Dynamic Threshold Orthogonal Matching Pursuit,DTOMP)。该方法将稀疏分解原理应用于水声信号的预处理,通过在正交匹配追踪算法中引入阈值约束,并根据噪声分布特性将其分为两部分,用以控制预设置的参数。通过对加噪正弦信号、实测鲸鱼叫声和舰船辐射噪声信号的降噪实验,表明该方法能够在对原始水声信号进行压缩的同时提高信噪比,且在较宽的信噪比变化范围内比小波方法具有更好的降噪性能。     

基于PSO优选参数的SVR水质参数遥感反演模型

为进一步提高多光谱图像水质反演的精度,提出了一种基于PSO优选参数的SVR水质参数遥感反演模型。该模型利用高分辨率多光谱遥感SPOT-5数据和水质实地监测数据,采用CV估计模型推广误差,并使用PSO优选SVR模型参数,实现了模型参数的自动全局优选,在训练好的SVR模型基础之上对水质进行反演。以渭河陕西段为例进行实证研究,实验结果表明,所提出的水质反演模型较常规的线性回归模型有更高的反演精度,为内陆河流环境遥感监测提供了一种新方法。     

Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance

Stochastic resonance can use noise to enhance weak signals, effectively reducing the effect of noise signals on feature extraction. In order to improve the early fault recognition rate of rolling bearings, and to overcome the shortcomings of lack of interaction in the selection of SR (Stochastic Resonance) method parameters and the lack of validation of the extracted features, an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed. compared with the existing methods, the AGSR (Adaptive Genetic Stochastic Resonance) method uses genetic algorithms to optimize the system parameters, and further optimizes the parameters while considering the interaction between the parameters. This method can effectively extract the weak fault features of the bearing. In order to verify the effect of feature extraction, the feature signal extracted by AGSR method was input into the Fully connected neural network for fault diagnosis. the practicality of the algorithm is verified by simulation data and rolling bearing experimental data. the results show that the proposed method can effectively detect the early weak features of rolling bearings, and the fault diagnosis effect is better than the existing methods.     

Effective time averaging of multiplexed measurements: a critical analysis

Multiplexing and time averaging of signal are effective noise reduction protocols applied in many analytical measurement systems. The efficacy of these protocols may be reduced by random occurrences of high-magnitude noise that do not conform to the statistical distribution of noise for all other measurements in the data set. This high-magnitude noise, which may have an insignificant probability of occurrence for a single measurement, almost certainly affects data collected in a multichannel, multiplexed modality, such as Fourier transform infrared (FT-IR) spectroscopic imaging employing focal plane array detectors. To recover time-averaging advantages in these cases, we present a general coaddition method that uses two statistical measures, the mean and median of the ensemble of measurements of a signal, to obtain a better estimate of the true signal than that estimated by time averaging alone. This method, termed median filtered time averaging, is shown to be an effective noise removal procedure for FT-IR imaging data. The effects of noise removal on time averaging and multiplexing are examined theoretically and are demonstrated for hyperspectral infrared microspectroscopic imaging data obtained from human skin biopsies by using a rapid data acquisition procedure.     

Linearized model for error-compensated kinetic determinations without prior knowledge of reaction order or rate constant

This paper describes a new algorithm for calculation of reaction orders, rate constants, and initial and final values of detector signal from several signal vs time data points. The algorithm utilizes a linearized version of the rate equation and is intended primarily to provide initial estimates of these kinetic parameters for other curve-fitting methods. However, under some circumstances, the linearized model can provide sufficiently reliable results that subsequent processing by other methods is not needed. Simulated data with different levels of superimposed noise, data densities, reaction orders, rate constants, and signal change are used to evaluate the algorithm both for its primary purpose of providing initial estimates for other curve-fitting methods and as an independent method. Results are compared with those obtained with a nonlinear least-squares method and two initial-rate methods. The new algorithm provides less reliable results than those obtained by the nonlinear curve-fitting method for some situations (e.g. reaction orders greater than two, low data densities) but has the advantage that it is applicable to reaction orders at and near unity where the nonlinear method to which it is compared fails.     

Multivariate numerical derivative by solving an inverse heat source problem

A method for approximating multivariate numerical derivatives is presented from multidimensional noise data in this paper. Starting from solving a direct heat conduction problem using the multidimensional noise data as an initial condition, we conclude estimations of the partial derivatives by solving an inverse heat source problem with an over-specified condition, which is the difference of the solution to the direct problem and the given noise data. Then, solvability and conditional stability of the proposed method are discussed for multivariate numerical derivatives, and a regularized optimization is adopted for overcoming instability of the inverse heat source problem. For achieving partial derivatives successfully and saving amount of computation, we reduce the multidimensional problem to a one-dimensional case, and give a corresponding algorithm with a posterior strategy for choosing regularization parameters. Finally, numerical examples show that the proposed method is feasible and stable to noise data.     

基于VC++的光谱分析软件设计与实现

 为了对红外干涉型光谱分析仪采集得到的干涉条纹进行分析,准确地反演相应的光谱分布函数,设计了基于迈克尔逊干涉结构的光谱分析算法,并通过VC++完成了对应的光谱分析软件．实验对830,940,1 064 nm三个常用近红外波长进行测试,分别与FTIR500型光谱仪的检测数据和MATLAB仿真数据进行对比．实验结果显示,本算法获得的光谱数据在主波长位置选择以及幅值探测上与FTIR500型光谱仪相近,而在噪声、杂波抑制方面优于FTIR500型光谱仪．数据处理速度略低于MATLAB,但光谱分布函数信噪比要高于MATLAB的数据处理结果,证明本系统具有一定的优越性．     

Scene-based nonuniformity correction for focal plane arrays by the method of the inverse covariance form

What is to our knowledge a new scene-based algorithm for nonuniformity correction in infrared focal-plane array sensors has been developed. The technique is based on the inverse covariance form of the Kalman filter (KF), which has been reported previously and used in estimating the gain and bias of each detector in the array from scene data. The gain and the bias of each detector in the focal-plane array are assumed constant within a given sequence of frames, corresponding to a certain time and operational conditions, but they are allowed to randomly drift from one sequence to another following a discrete-time Gauss-Markov process. The inverse covariance form filter estimates the gain and the bias of each detector in the focal-plane array and optimally updates them as they drift in time. The estimation is performed with considerably higher computational efficiency than the equivalent KF. The ability of the algorithm in compensating for fixed-pattern noise in infrared imagery and in reducing the computational complexity is demonstrated by use of both simulated and real data.     

Uncertainty analysis in the measurement of the spatial responsivity of infrared antennas

The measurement of a two-dimensional spatial responsivity map of infrared antennas can be accomplished by use of an iterative deconvolution algorithm. The inputs of this algorithm are the spatial distribution of the laser beam irradiance illuminating the antenna-coupled detector and a map of the measured detector response as it moves through the illuminating beam. The beam irradiance distribution is obtained from knife-edge measurements of the beam waist region; this data set is fitted to a model of the beam. The uncertainties, errors, and artifacts of the measurement procedure are analyzed by principal-component analysis. This study has made it possible to refine the measurement protocol and to identify, classify, and filter undesirable sources of noise. The iterative deconvolution algorithm stops when a well-defined threshold is reached. Spatial maps of mean values and uncertainties have been obtained for the beam irradiance distribution, the scanned spatial response data, and the resultant spatial responsivity of the infrared antenna. Signal-to-noise ratios have been defined and compared, and the beam irradiance distribution characterization has been identified as the statistically weakest part of the measurement procedure.     

蚁群算法求解弹性本构参数区间反问题

建立了弹性本构参数区间反问题的数值模型,利用区间参数摄动有限元方法和基于网格划分策略的连续域蚁群算法进行求解,探讨了非均质、不确定区间半径、初值选择及数据噪音对反演结果的影响,数值验证给出令人满意的结果。     

Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays

An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors' parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.     

Structural damage detection using ARMAX time series models and cepstral distances

A novel damage detection algorithm for structural health monitoring using time series model is presented. The proposed algorithm uses output-only acceleration time series obtained from sensors on the structure which are fitted using Auto-regressive moving-average with exogenous inputs (ARMAX) model. The algorithm uses Cepstral distances between the ARMAX models of decorrelated data obtained from healthy and any other current condition of the structure as the damage indicator. A numerical model of a simply supported beam with variations due to temperature and operating conditions along with measurement noise is used to demonstrate the effectiveness of the proposed damage diagnostic technique using the ARMAX time series models and their Cepstral distances with novelty indices. The effectiveness of the proposed method is validated using the benchmark data of the 8-DOF system made available to public by the Engineering Institute of LANL and the simulated vibration data obtained from the FEM model of IASC-ASCE 12-DOF steel frame. The results of the studies indicate that the proposed algorithm is robust in identifying the damage from the acceleration data contaminated with noise under varied environmental and operational conditions.     

Data-Aided Timing Recovery for Recording Channels With Data-Dependent Noise

In high-density data storage systems, noise becomes highly correlated and data dependent as a result of media noise, channel nonlinearities, and front-end filters. In such environments, conventional timing recovery schemes will exhibit large residual timing jitter and, especially, data-dependent timing jitter. This paper presents a new data-aided timing recovery algorithm for data storage systems with data-dependent noise. We derive a maximum-likelihood timing recovery scheme based on a data-dependent Gauss-Markov model of the noise. The timing recovery algorithm incorporates data-dependent noise prediction parameters in the form of linear prediction filters and prediction error variances. Moreover, because noise can be nonstationary in practice, we propose an adaptive algorithm to estimate and track the noise prediction parameters. Simulation results, for an idealized optical storage channel incorporating a simple model of media noise, illustrate the merits of our algorithm