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.     

Nonlinear filtering for recognition of phase-encoded images

We investigate the use of Fourier plane nonlinear filtering for phase-encoded images. We investigate the performance of the nonlinear joint transform correlator and the nonlinearly transformed matched filter for phase-encoded images with different types of input noise. We use the peak-to-output-energy ratio, peak-to-sidelobe ratio, and discrimination ratio as the metrics for measuring the performances. We mathematically analyze the peak-to-output-energy ratio of the nonlinearly transformed matched filter for phase-encoded images with spatially nonoverlapping white noise. Computer simulations are provided to show the performance improvements of the nonlinear filtering techniques for the phase-encoded images. In comparison with linear filtering techniques, we find that the nonlinear filtering techniques substantially improve the performance metrics. From the computer-simulation results it can be seen that the nonlinear joint transform correlator performs better than the nonlinearly transformed matched filter in detecting phase-encoded targets in the presence of different types of noise, such as additive overlapping white noise, spatially nonoverlapping white background noise, spatially nonoverlapping colored background noise, and nontarget objects.     

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.     

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.     

Optimum nonlinear composite filter for distortion-tolerant pattern recognition

We describe a nonlinear distortion-tolerant filter for pattern recognition that is optimum in terms of tolerance to input noise and discrimination capability. This filter was derived by minimization of the output energy that is due to the overlapping additive noise and the input scene, and the output of the filter meets the design constraints obtained from the training data set. The performance of this filter was tested with an input scene containing one of the training data sets, a nontraining true target, and a false object in the presence of overlapping additive noise and nonoverlapping background noise. We carried out Monte Carlo runs to measure the statistical performance of the filter and obtained receiver operating characteristics curves to show the detection capabilities of the filter.     

Design of correlation filters for recognition of linearly distorted objects in linearly degraded scenes

Generalized correlation filters are proposed to improve recognition of a linearly distorted object embedded in a nonoverlapping background when the input scene is degraded with a linear system and additive noise. Several performance criteria defined for the nonoverlapping signal model are used for the design of filters. The derived filters take into account information about an object to be recognized, disjoint background, noise, and linear degradations of the target and the input scene. Computer simulation results obtained with the proposed filters are discussed and compared with those of various correlation filters in terms of discrimination capability, location errors, and tolerance to input noise.     

Generalizations of the maximum average correlation height filter

Recently several approaches have been presented in which the shape of the correlation peak is used to distinguish between target and clutter. The well-known maximum average correlation height (MACH) filter was specifically designed to produce similar correlation planes for target variations present in the training set. Results are presented of a study of certain generalizations of the MACH filter intended to enhance the performance in clutter. It is shown that by taking into account the nonoverlapping character of the background noise and focusing the MACH correlation plane similarity requirement to the peak neighborhood, it is possible to simultaneously achieve a small variation in correlation peak shape and high peak-to-sidelobe ratios for cluttered images.     

Reduction in correlation-filter sensitivity to background clutter by the automatic spatial frequency selection algorithm

We propose an automatic spatial frequency selection correlation filter that reduces the sensitivity to nonoverlapping noise or background clutter. This is achieved by inclusion of distorted versions of the reference images surrounded by nonoverlapping background clutter. Furthermore, we impose that the window functions of the reference images give response zero-correlation amplitudes. Simulation results are provided in the case of a two-class pattern-recognition problem and show that the results are appreciably increased. The results are compared with a normal automatic spatial frequency selection.     

Detecting three-dimensional location and shape of noisy distorted three-dimensional objects with ladar trained optimum nonlinear filters

We propose a filtering technique that uses laser radar (ladar) data to detect a target's three-dimensional (3D) coordinates and shape within an input scene. A two-dimensional ladar range image is converted into 3D space, and then the 3D optimum nonlinear filtering technique is used to detect the 3D coordinates of targets (including the target's distance from the sensor). The 3D optimum nonlinear filter is designed to detect distorted targets (i.e., out-of-plane and in-plane rotations and scale changes) and to be noise robust. The nonlinear filter is derived to minimize the mean of the output energy in response to the input scene in the presence of disjoint background noise and additive noise and to maintain a fixed output peak for the members of the true-class target training set. The system is tested with real ladar imagery in the presence of background clutter. The background clutter used in the system evaluation includes false objects that are similar to the true targets. The correlation output of ladar images shows a dominant peak at the target's 3D coordinates.     

Distortion-invariant filter for nonoverlapping noise

A new heuristic filter based on the optimum filter for disjoint noise developed by Javidi and Wang [J. Opt. Soc. Am. A 11, 2604 (1995)] is presented. In this new filter a number of optimum filters built from single training images are combined linearly by use of the synthetic discriminant function (SDF) approach into a distortion-invariant filter for disjoint noise. Like the traditional SDF approach, this summation technique makes it possible to control the height of the correlation peak easily, for example, if a uniform filter response is needed. The filter is compared with the distortion-invariant version of the optimum filter on images with low contrast and high levels of nonoverlapping clutter. The new filter shows good results, demonstrating that it is, with very simple heuristic methods, possible to improve the performance of distortion-invariant filters for nonoverlapping noise.     

Image preprocessing for rotation-invariant pattern recognition in the presence of signal-dependent noise

I propose a new method that ensures efficient rotation-invariant pattern recognition in the presence of signal-dependent noise by combining the application of rotation-invariant correlation filters with preprocessing of the noisy input images. The preprocessing uses local suboptimal estimators derived from estimation theory and implies an a priori knowledge of a model describing the noise source. The image noise sources considered are speckle and film-grain noise. Pour different metrics are used to analyze the correlation performance of the circular-harmonic filter, the phase-only circular-harmonic filter, and the binary phase-only circular-harmonic filter, with and without a preprocessing. Computer simulations show that signal-dependent noise can seriously degrade the performance of the phase-only circular-harmonic filter and the binary phase-only circular-harmonic filter. The most severe indication of correlation-performance degradation is the occurrence of false alarms in 15% to 20% of noise realizations of the correlation. Preprocessing increases the correlation-peak signal-to-noise ratio significantly and reduces the false-alarm probability by one to two orders of magnitude.     

误差白化与Kalman滤波的微硅加速度传感器动态补偿

通过模型参考的系统辨识方法建立微硅加速度传感器的动态补偿器。由于测量噪声和补偿器对传感器的频带扩展,使得补偿器的输入/输出信号存在严重的噪声干扰。在噪声干扰下,采用均方误差为代价函数的系统辨识方法,无法得到补偿器参数的无偏估计。补偿器参数的偏差和传感器频带的扩展将会使补偿器的输出信号出现严重失真和高频噪声干扰。为解决噪声对硅加速度传感器的动态补偿的影响,研究了一种新的动态补偿方法,该方法采用误差白化为代价函数的系统辨识方法得到补偿器的参数,可消除补偿器的参数在估计中的测量噪声影响,并通过卡尔曼实时滤波减小因传感器频带扩展所产生的高频噪声干扰。     

Modified fringe-adjusted joint transform correlation to accommodate noise in the input scene

A modified fringe-adjusted joint transform correlator is proposed that is able to accommodate noise in the input scene. The effect of noise in the input scene on the performance of the joint transform correlator is analyzed and quantified. When the target is embeddedin aseverely noise-corrupted input scene, it is shown that the proposed modified fringe-adjusted filter joint transform correlator delivers a better correlation performance and the capacity to accommodate this noise than does the fringe-adjusted filter-based correlator. When the power spectra of the input image and the reference image are subtracted from the power spectrum of the joint-input image, it is found that the noise effect on the output plane is independent of the objects in the input scene and originates from the convolution of the reference image and noise in the input scene.     

基于TMS320VC5402的数字图像空间滤波系统设计

针对红外成像观测系统中获取的低信噪比、背景及噪声干扰严重的小目标图像的特点,设 计一种具有保护带(5×5 G)的5×5 Robinson空间滤波器,采用TMS320VC5402进行硬件实现。实验表明,该空间滤波器能够大大提高目标的信噪比,抑制背景和噪声,保证系统实时性要求,很适用于小目标检测。     

Analysis of spatial light modulator contrast ratios and optical correlation

We have performed a general analysis of optical correlators with spatal light modulators (SLM's) whose primary defect is a finite contrast ratio (CR). Our mathematical analysis identifies three noise terms that appear in addition to the correlation term. The filter SLM contains either a phase-only filter (POF) or a binary-phase-only filter (BPOF). Insertion of a dc block at the center of the filter SLM decreases the noise background in the correlator plane; this dc block is larger than that required for the same level of performance in a correlator whose SLM's have transmissive (or reflective) dead zones. With a noise-free input and the dc block, our computer simulations that show the peak intensity falling off as the CR decreases are in quantitative agreement with the correlation term of the mathematical model. For a cluttered, disjoint noise input this agreement is only qualitative, and at low CR's the dc block is definitely required for the BPOF correlator if the secondary peaks in the output are to be brought below the correlation peak.     

Detection improvement in spatially filtered x-ray fluoroscopy image sequences

The effect of spatial noise-reduction filtering on human observer detection of stationary cylinders mimicking arteries, catheters, and guide wires in x-ray fluoroscopy was investigated in both single image frames and image sequences. Ideal edge-preserving spatial filtering was simulated by filtering of the noise before addition of the target cylinder. This allowed us to separate the effect of edge blurring from those of noise reduction and spatial noise correlation. We used three different center-weighted averagers that reduced pixel noise variance by factors of 0.75, 0.50, and 0.25. As compared with no filtering, the effect of filtering on detection in single images was statistically insignificant. This indicated an adverse effect of spatial noise correlation on detection that countered the effect of noise reduction. By comparison, spatial filtering significantly improved detection in image sequences and yielded potential x-ray dose savings of 26-34%. Comparison of results with two observer models suggested that human observers have an improved detection efficiency in spatially filtered image sequences as compared with white-noise sequences. Pixel noise reduction, a measure commonly used to assess filter performance, overestimated the effect of filtering on detection and was not a good indicator of image quality. We conclude that edge-preserving spatial filtering is more effective in sequences than in single images and that such filtering can be used to improve image quality in noisy image sequences such as x-ray fluoroscopy.     

低信噪比环境下的语音识别方法研究

单通道语音信号在信噪比较大的环境下经过增强后再识别,能表现出较高的识别率。但是在低信噪比环境下,增强后语音信号的识别率急剧下降。针对此种情况,提出了一种用在识别系统前端的语音增强算法,该增强算法将采集到的带噪语音信号先使用对数最小均方误差(Logarithmic Minimum Mean Square Error,Log MMSE)提高其信噪比,然后再利用改进的维纳滤波去除噪声残留并提升语音可懂度,最后用梅尔频率倒谱系数(Mel-Frequency Cepstral Coefficients,MFCC)和隐马尔科夫模型(Hidden Markov Model,HMM)对增强后的语音信号做特征提取并识别。实验分析结果表明,该方法能有效地抑制背景噪声并减少噪声残留,显著提升低信噪比环境下语音识别的准确性。     

Adaptive deblurring of noisy images

We propose a practical sensor deblurring filtering method for images that are contaminated with noise. A sensor blurring function is usually modeled via a Gaussian-like function having a bell shape. The straightforward inverse function results in the magnification of noise at high frequencies. To address this issue, we apply a special spectral window to the inverse blurring function. This special window is called the power window, which is a Fourier-based smoothing window that preserves most of the spatial frequency components in the passband and attenuates quickly at the transition band. The power window is differentiable at the transition point, which gives a desired smooth property and limits the ripple effect. Utilizing the properties of the power window, we design the deblurring filter adaptively by estimating the energy of the signal and the noise of the image to determine the passband and the transition band of the filter. The deblurring filter design criteria are (a) the filter magnitude is less than 1 at the frequencies where the noise is stronger than the desired signal (the transition band), and (b) the filter magnitude is greater than 1 at the other frequencies (the passband). Therefore the adaptively designed deblurring filter is able to deblur the image by a desired amount based on the estimated or known blurring function while suppressing the noise in the output image. The deblurring filter performance is demonstrated by a human perception experiment in which 10 observers are to identify 12 military targets with 12 aspect angles. The results of comparing target identification probabilities with blurred and deblurred images and adding two levels of noise to blurred and deblurred noisy images are reported.     

阵列信号维纳滤波用于主动声呐图像增强处理

声呐图像的噪声背景抑制是提高水下目标检测能力的重要问题.针对声呐图像背景斑点噪声强、目标轮廓模糊、目标回波对比度低等问题,利用确定性目标回波信号与随机分布的干扰噪声之间的相关统计特性差异,采用基于最小均方差准则的阵列信号维纳滤波器,通过主动最小方差无畸变响应(Minimum Variance Distortionles...