强杂波背景红外弱小目标检测算法

针对强杂波背景远距离红外弱小信号目标的特点,提出了一种基于自适应滤波的红外弱小信号检测方法。算法首先对图像进行消噪声处理,其次运用自适应滤波方式消除背景增强目标信号,最后进行基于点源目标(试验采集)成像信号特性的判决法则删除虚假目标,算法有效解决了光电探测设备高检测概率与低虚警率的矛盾。实验结果表明:该方法能够在单帧图像上有效提取出小区域信噪比为4的弱小信号目标,检测概率不低于0.75,虚警率不高于1次/100帧。     

基于多结构元素形态滤波与自适应阈值分割相结合的红外弱小目标检测

针对低信噪比灰度图像中弱小目标检测的难题,分析了红外弱小目标成像的特点,提出了基于多结构元素形态滤波与自适应阈值分割相结合的目标检测算法.利用目标运动的连续性、规律性和噪音产生的随机性,结合数学形态学结构元素的特点,研究了一种多结构元素形态滤波的管道滤波方法,通过流水线管道检测目标运动轨迹.实验结果表明,该算法应用于复杂背景下低信噪比的红外弱小目标图像能够得到较理想的结果,并且目标检测概率高,速度快,虚警率低.     

变阈值检测对无扫描距离选通激光雷达探测概率的影响

根据无扫描成像激光雷达光电阴极输出光电子计数分布满足泊松统计的特点,由极大似然估计方法,给出判定是否接收到回波的似然比判据阈值的计算方法。并针对典型无扫描距离选通成像激光雷达情况,给出了系统探测概率、虚警概率和漏警概率的近似计算方法,并采用蒙特-卡罗模拟方法进行验证。分析了系统阈值对探测概率的影响,并提出变阈值的方法提高探测系统探测概率。而受系统硬件的限制,在满足探测要求的情况下,尽量缩短探测距离和减少探测器像素数,增加接收目标的有用信号光子数,并同时根据极大似然估计法来确定阈值,达到提高探测概率的目的。     

基于Gm-APD的成像激光雷达目标探测特性

针对采用盖革模式雪崩光电二极管（Gm-APD）作为探测器的成像激光雷达,介绍了其测距原理及3D成像原理,并对如何提高其探测性能的方法进行了分析。以分析Gm-APD触发信号的统计特性为基础,对出现在距离门内不同位置目标的探测概率和虚警概率进行了研究与仿真,结果表明,目标处在距离门最前面时,探测概率受噪声水平影响最小,虚警概率受信号强度影响最大;目标靠近距离门中间位置时,探测概率随噪声水平增大下降缓慢,虚警概率随信号强度增大下降缓慢;目标处在距离门末尾时探测概率受噪声水平影响最大,而虚警概率几乎与回波信号强度无关。     

激光目标指示器的信噪比和最小可探测信号的分析

对激光目标指示器的工作原理及遇到的噪声进行了阐述,使用阈值、探测器概率及平均虚警等激光目标指标的信噪比和最小可探测信号的功率进行了分析并给出了计算公式。     

匹配滤波器优化设计及在红外弱小点目标检测中的应用

针对红外传感器成像信噪比低且易受噪声、背景杂波干扰的问题,结合红外图像中点目标成像的特性,充分利用目标、背景杂波及噪声在空间域中的分布特性,进行空间匹配滤波器的优化设计.首先对红外点目标特性进行了分析,在此基础上进行一维匹配滤波器的优化设计,进而构建了优化设计的空间匹配滤波器.结合优化设计匹配滤波器、形态学背景抑制和自适应门限的红外弱小目标枪测算法由于充分考虑了红外点目标的衍射效应和目标与背景的灰度差异,使滤波过程智能地融入了目标和背景的特性,极大地提高了红外弱小目标的检测性能.实测数据验证表明,本检测算法对低信噪比(f<,SNR>≤2)的红外图像,在保证10<'-5>虚警概率前提下,检测概率不小于95%.     

光子成像系统信号与噪声的统计特性分析

在去除光子图像的本底噪声时,根据微弱点目标的探测统计特性,以泊松分布均值与方差相等的特点为判据,提出了一种确定阈值选取范围的新方法。利用点过程的分析方法研究了光子受限点目标的探测统计特性,并进行了实验研究,结果表明光子受限点目标的探测统计特性服从平稳泊松点过程。利用以上分析方法可以快速地获得微弱点目标事件的期望发生率,以及在下一段时间内目标至少再出现一次的概率,从而为后续的目标探测、跟踪以及位置预测奠定了基础。     

红外点目标检测的小波变换方法研究

在对红外图像目标特性及背景特性分析的基础上,提出了基于小波变换高频子图像处理算法及基于小波变换的区域相关算法,着重解决当目标距离成像系统较远时的点目标检测问题.实验结果表明:将区域相关算法和图像局部区域阈值的自适应滤波检测技术相结合,可显著提高红外点目标检测概率,实现较远距离的点目标检测.     

基于引导滤波和分块自适应阈值的单帧红外弱小目标检测

为了在有效地检测复杂场景下红外弱小目标的同时保持较低虚警率,在满足算法实现实时性的前提下,提出一种基于引导滤波和分块自适应阈值的单帧红外弱小目标检测。首先,为缓解边缘杂波干扰,采用具有保边特性的引导滤波对图像进行背景估计;然后,利用弱小目标具备的局部灰度最大特性,提出基于软阈值非极大值抑制的九宫格滤波计算目标的概率。通过加权的方式进一步剔除背景,抑制结果中不满足目标特性的区域;最后,针对复杂场景目标检测虚警率和漏检率高的问题,提出一种分块自适应阈值分割方法提取候选目标。实验结果表明,在公开数据集上与Top-Hat、LCM和Max-Median等经典方法相比,所提方法性能优于其他方法,恒虚警下不同复杂度场景的召回率分别达到87.97%、84.93%和86.22%,可有效抑制背景,增强目标信号,提高红外弱小目标检测的召回率,且具有更好的场景鲁棒性。     

基于改进遗传算法优化的形态学修正TOP-HAT滤波器设计方法

针对红外序列图像中运动弱小点目标的检测问题,设计了一种基于改进遗传算法优化的修正Top-Hat形态学滤波器算子。其中,优化的修正Top-Hat形态学滤波器可以很好地抑制背景和噪声的影响;改进遗传算法采用新的区间离散化编码和自适应的主次式交叉与变异算子,通过优化搜索全局空间得到的形态学滤波器参量具有较好的滤波性及时效性。并且针对不同信噪比的点目标检测建立了自适应门限。实测数据的处理结果表明:在虚警概率小于5%情况下,优化的修正Top-Hat形态学滤波器算子对信噪比约为2的复杂图像检测概率大于等于70%,与固定结构元素的Top-Hat形态学滤波器相比检测概率提高了近10%,与用经典遗传算法训练的传统Top-Hat形态学滤波器相比检测概率提高了4%。     

一种利用十字窗口识别红外弱小目标的新方法

为有效地识别红外图像中的弱小目标,在分析传统识别方法的基础上,提出了一种利用十字窗口识别红外弱小目标的新方法.该方法在待识别点上、下、左、右四个方向以目标大小选取四个参考点构造十字形识别窗口,根据待识别点与参考点之间的灰度梯度识别该像素点是否为目标像素.该方法无需预测背景,可以在低信噪比、强度变化剧烈的图像中获得较高的识别率与较低的虚警率.实验结果表明了该算法的有效性.     

光子计数激光雷达的时间相关卡尔曼深度估计

在高背景噪声和低积分时间的激光雷达远距离成像场景中,针对传统方法得到的深度图像目标被噪声淹没和深度估计偏差较大的问题,提出了一种基于信号光子时间相关性和自适应卡尔曼滤波器的深度信息估计方法。首先,提取在时间上具有聚集特征的光子计数形成集合;然后,分析了影响信号光子在时间上分布的因素并使用静态高斯线性模型来描述该集合;最后将集合中的所有光子飞行时间乱序,输入改进的自适应卡尔曼滤波器,从而迭代估计深度值。在信号噪声比为1的室内,积分时间分别为10 ms和1 ms时,本文方法相对传统的最大似然方法在均方根误差指标上提升了40%和38%。在信噪比约为0.135的室外2 km目标成像实验中,在信号光子数分别为100、33和17的情况下,本文方法成像效果都优于传统最大似然估计方法和时间相关光子快速去噪方法,得到的深度图像都更清晰,噪声更低。在高噪声和短积分时间下,本文方法可以被运用于激光雷达远距离成像的深度信息估计和图像恢复中。     

Prediction of the performance of an algorithm for the detection of small targets in infrared images

Detection of small targets in infrared (IR) images is important in IR image processing. For the prediction of performance of a detection algorithm, it is necessary to calculate the probability of detection and probability of false alarm. A method is developed to calculate the probabilities in this paper. The detection is divided into two parts: the first part, which is called pre-detection, is to find out candidates for targets in a single frame of an image; and the second part is to localize the target in multiple frames of the image. Under some assumptions, the pre-detection probability, the false detection probability of single frame, detection probability and false alarm probability are derived. The algorithm for the detection of small target in IR image, which is used for the derivation of the probabilities, is contrast threshold detection based on background prediction, and a pipeline filter is used for multiframe image processing. The results show the relationship of the probabilities to the contrast of target to background, SNR, and contrast threshold.     

A robust infrared dim target detection method based on template filtering and saliency extraction

Dim target detection in infrared image with complex background and low signal-clutter ratio (SCR) is a significant and difficult task in the infrared target tracking system. A robust infrared dim target detection method based on template filtering and saliency extraction is proposed in this paper. The weighted gray map is obtained from the infrared image to highlight the target which is brighter than its neighbors and has weak correlation with its background. The target saliency map is then calculated by phase spectrum of Fourier Transform, so that the dim target detection could be converted to salient region extraction. The potential targets are finally extracted by combining the two maps. Moreover, position discrimination between targets in the two maps is used to exclude the false alarms and extract the targets. Experimental results on measured images indicate that our method is feasible, adaptable and robust in different backgrounds. The ROC (Receiver Operating Characteristic) curves obtained from the simulated images demonstrate the proposed method outperforms some existing typical methods in both detection rate and false alarm rate, for target detection with low SCR.     

Detection of infrared small target based on fusion of multi-filters北大核心CSCD

Aiming at solving accuracy problem of infrared small target detection in sky and ocean background scenarios of infrared image sequences, a novel infrared small target detection based on multi-filters algorithm fusion method is presented in this paper. Firstly infrared small target and imaging, time and space characteristics of the corresponding background noise are analyzed. Tophat algorithm with improved Robinson guard filter are then integrated to highlight target and suppress clutter background by using infrared small target imaging features. Adaptive threshold segmentation is used to extract candidate targets, while Unger smoothing filter and multi-objects association filter are used to eliminate random noise and false targets in the candidate targets. Multiple experiments of infrared small target image sequences are implemented, and experimental results show that proposed method can detect infrared small targets at 99% detection rate with high reliability and good real-time performance. © 2017, Editorial Board, Journal of Applied Optics. All right reserved.     

小样本光子图像的统计处理

讨论了一种对小样本光子图像的统计处理方法。在超微弱发光的研究中（例如细胞的超微弱荧光）,由于发光强度极弱,需要用像增强器对超微弱发光图像进行增强得到可视图像,超微弱发光图像不可避免地受到像增强系统暗噪声及背景噪声的影响,使光子图像湮没在噪声中。为从原始图像中检验出信号,根据信号光子和噪声光子的不同统计分布,运用信号检测与的方法判断光子是否属于信号光子,并得到一简明的判据,由此判据剔除图像中的噪声光子,得到信噪比改善的光子图像。并用此方法处理了人掌的超微弱发光光子图像。     

光子图像统计处理

讨论了一种对有限光子图像累加的统计处理方法。在超微弱发光的研究中（例如细胞的超微弱光），由于发光强度较弱，需要用像增强器对超微弱发光图像进行增强得到可视光子图像，超微弱发光的光子图像不可避免地受到像增强系统暗噪声及背景噪声的影响，使光子图像湮没在噪声中。为从原始图像中检验出信号，常将采集到的有限光子图像进行累加以增加样本数，根据信号光子和噪声光子的不同统计分布，运用似然比检验方法得到有效判断累加图像中某像素是否含有信号光子，由此剔除图像中的噪声光子，得到信噪比改善的光子图像。     

An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system

To achieve higher detection rate and lower false alarm rate in dim and small target detection, this paper proposed an improved algorithm based on the contrast mechanism of human visual system (HVS) for infrared small target detection in an image with complicated background. According to the contrast mechanism of HVS, Laplacian of Gaussian (LoG) filter is exploited to deal with the input image, which can not only suppress the background noise and clutter but also enhances the target intensity significantly. As a result it increases the contrast ratio between target and background. To further eliminate residual clutter, we process the filtered image with morphological method in all directions. True target is finally obtained by applying local thresholding segmentation to the pre-processed image. Experimental results demonstrate its superior and reliable detection performance by high detection rate and low false alarm rate.