红外微弱目标的超强增强方法研究与仿真

红外目标图像增强在军事上有着重要的应用.红外微弱目标图像在远距离采集时,会形成微弱目标,导致背景红外图像存在大量的杂波,传统红外微弱目标增强方法在背景图像呈连续分布的前提下,采用图像融合技术实现增强处理,一旦背景红外图像出现非连续波动,容易产生重叠现象,造成图像模糊,不能有效的增强红外微弱目标.提出了一种依据宏观模糊集合的红外微弱目标自适应超强增强方法,给出了红外图像的宏观模糊集与模糊特征平面,对红外图像宏观模糊空间进行调整,完成红外图像模糊空间内对比度的增强操作,采用巴特沃斯低通滤波器对红外图像中的噪声进行滤波处理,利用反正切函数作为映射,将空间域的灰度红外图像变换为对应的广义隶属函数,实现外红微弱目标的自适应增强.实验结果表明,所提方法不仅能有效地增强红外微弱目标,还能自适应地增强红外图像局部区域不同层次的边缘和细节,使得图像更加清晰.     

远距离红外弱小运动目标检测

针对远距离红外弱小运动目标的检测,本文提出了一种空域-时域联合算法。该方法首先基于结构元素大小自适应的形态学顶帽变换进行红外复杂背景抑制,增强了图像的信噪比;接下来,利用平均迭代算法精确确定门限阈值,实现单帧图像分割;在此基础上,利用邻域多帧图像相与判决算法,实现基于时间序列图像的弱小运动目标检测。实验表明,本文提出的方法能够较好地进行背景抑制和消除噪声,准确有效地检测出红外运动弱小目标。     

成像导引头运动点目标检测

在仿真分析的基础上，针对红外成像空空导弹导引头的应用特点，对现有基于形态学的算法进行改进，提出一种新算法。新算法通过计算局部图像的相似度，鉴别背景点与目标点，提高检测和识别概率。算法分四步：(1)进行背景预测，通过消除背景，获得输入图像中相对背景较亮的部分；(2)利用自适应阈值分割，消除大量低灰度噪声点、背景，获得候选目标；(3)利用点-航迹关联，根据点目标运动的连续性剔除噪声；(4)对原始图像中以候选目标为中心的子图像进行相邻帧的相似度计算，根据子图像匹配程度剔除剩下的候选目标中的背景，从而检测出真正的目标。仿真结果证实了新方法对低信噪比复杂背景中点目标检测与识别的有效性。     

基于模糊理论的椒盐噪声滤波算法

针对图像中的椒盐噪声,基于模糊理论设计了一种滤波算法。首先结合椒盐噪声特点,借助窗口进行噪声检测,其次设计了自适应的方法消除噪声,最后采用图像进行实验,定性和定量分析结果表明该方法对于椒盐噪声的消除可行有效。     

基于模糊理论的椒盐噪声滤波算法

针对图像中的椒盐噪声,基于模糊理论设计了一种滤波算法。首先结合椒盐噪声特点,借助窗口进行噪声检测,其次设计了自适应的方法消除噪声,最后采用图像进行实验,定性和定量分析结果表明该方法对于椒盐噪声的消除可行有效。     

基于小波和形态学的自然地面背景红外目标检测方法

本文采用小波变换和形态Top-Hat滤波相结合的方法抑制红外图像的混合噪声及大面积的背景干扰,减少图像模糊和增强目标;通过选择适当的结构元素进行系列形态组合运算,去掉虚警点而获得少量候选目标;搜索局部极大值并确定阈值,分离出真正的目标.实验结果表明,该方法能有效检测和分割出低信噪比复杂自然背景红外图像中的目标.     

应用模糊增强及均值漂移实现红外目标分割

针对复杂环境下红外图像信噪比和对比度低,边缘模糊,目标分割困难的情况,提出一种基于模糊增强和均值漂移图像滤波的红外目标分割方法。首先定义新的隶属度函数,运用模糊集理论进行红外图像增强,避免了传统模糊增强算法的弊病,有效提高目标与背景的对比度;之后利用ICI（交叉置信区）规则确定均值漂移的带宽参数,提出一种新的自适应带宽均值漂移图像滤波方法,实现图像的进一步平滑和聚类;最后利用自适应阈值实现红外目标分割。实验结果表明,算法能够正确有效地分割出复杂环境下的红外目标,并且很好地保持了目标的轮廓细节。     

Fuzzy-ART背景抑制的单帧红外弱小目标检测

针对现有背景抑制算法未能有效地抑制背景而导致目标检测率低的问题,提出一种基于模糊自适应共振理论(Fuzzy-ART)进行背景抑制、基于行列k均值(k-means)聚类实现阈值分割的单帧红外弱小目标检测算法.首先依据红外成像原理仿真生成红外弱小目标训练样本;然后采用Fuzzy-ART神经网络建立目标模型,并以此分析各像素点的目标模糊隶属度来抑制背景杂波;最后采用基于行列k-means聚类的自适应阈值分割算法来检测真实目标.实验结果表明,该算法能有效地抑制背景杂波和突显目标,并能有效地提高信噪比检测弱小目标.     

小波神经网络在微弱信号检测中的应用

因为噪声总是会影响检测的结果,所以低信噪比下的信号检测是目前检测领域的热点,而强噪声背景下微弱信号的提取又是信号检测的难点。小波神经网络比数字滤波器更加适合检测微弱信号。小波神经网络是一种时频分析的自适应系统,它能检测信号中的微小变化。该文提出了一种新的检测白噪声中微弱信号的方法。仿真结果表明,小波神经网络在检测微弱信号的特征和改善信噪比方面是一种十分有效的方法。     

基于红外面阵传感器的小目标检测算法

研究了红外面阵传感器图像中小目标的特征,针对目标低信噪比的问题,提出了一种新的单帧红外图像小目标的检测方法。首先用改进的中值滤波对图像进行处理,抑制孤立噪声,然后对图像进行基于提升小波的分解,并用形态学对图像进行背景抑制,最后通过自适应阈值进行二值化分割检测出小目标。实验结果表明：该方法对面阵传感器红外弱小目标有良好的检测效果。     

基于小波滤波背景预测的红外弱小目标检测方法

红外弱小目标检测是红外图像研究领域的热点与难点。有效地从背景中检测出弱小目标对于后续的跟踪、识别工作具有十分重要的意义。针对现有检测方法的不足,提出了一种基于小波滤波背景预测的红外弱小目标检测方法。该方法利用小波滤波去噪的优良特性将目标作为噪声滤除,然后构建近似的前景分布图与背景分布图,最后基于连通体筛选与对比度门限完成弱小目标的提取。采用实测光电图像对该方法进行了验证,结果表明,提出的方法能够有效抑制噪声,完成背景预测以及红外弱小目标的检测。     

基于模糊细胞神经网络的彩色图像形态学重构

利用彩色图像的ＲＧＢ空间分解,在模糊细胞神经网络上实现了彩色图像数学形态学的基本算子,并讨论了该实现相对于常规串行计算机算法的优越性和局阴,进一步地利用按分量的灰度重构,实现了彩色重构算法,最后讨论了该重构算法在抑帛镐频噪声中的应用,给出的仿真结果对对于推广模糊细胞神经网络在彩色图像实时处理和硅眼等模拟逻辑中的应用有着重要的意义。     

Analysis of new top-hat transformation and the application for infrared dim small target detection

To improve the performance of top-hat transformation for infrared dim small target detection in a simple and effective way, the definition, properties, multi-scale operations of new top-hat transformation and the application for infrared dim small target detection are addressed in this paper. The definition of new top-hat transformation uses two different but correlated structuring elements to reorganize the classical top-hat transformation, and takes into account of the difference information between the target and surrounding regions. Given this definition, the new top-hat transformation has some special properties and three types of multi-scale operations, which are discussed in detail. Subsequently, one application case of multi-scale operation for noise suppression is given. Good performance of the application for infrared dim small target detection is obtained, which could be ascribed to the proper selection of structuring elements based on the properties. The experimental results of the application demonstrate that new top-hat transformation can detect infrared dim small target more efficiently than classical top-hat transformation and some other widely used methods.     

Topology constraint free fuzzy gated neural networks for patternrecognition

A novel topology constraint free neural network architecture using a generalized fuzzy gated neuron model is presented for a pattern recognition task. The main feature is that the network does not require weight adaptation at its input and the weights are initialized directly from the training pattern set. The elimination of the need for iterative weight adaptation schemes facilitates quick network set up times which make the fuzzy gated neural networks very attractive. The performance of the proposed network is found to be functionally equivalent to spatio-temporal feature maps under a mild technical condition. The classification performance of the fuzzy gated neural network is demonstrated on a 12-class synthetic three dimensional (3-D) object data set, real-world eight-class texture data set, and real-world 12 class 3-D object data set. The performance results are compared with the classification accuracies obtained from a spatio-temporal feature map, an adaptive subspace self-organizing map, multilayer feedforward neural networks, radial basis function neural networks, and linear discriminant analysis. Despite the network's ability to accurately classify seen data and adequately generalize validation data, its performance is found to be sensitive to noise perturbations due to fine fragmentation of the feature space. This paper also provides partial solutions to the above robustness issue by proposing certain improvements to various modules of the proposed fuzzy gated neural network.     

基于多传感器数据融合的智能小车避障的研究

针对智能小车避障问题,提出了一种将模糊逻辑和神经网络相结合的融合方法—Takagi-Sugeno(T-S)模糊神经网络方法。基于此方法的数据融合算法应用在智能小车避障运动中,采用多只超声波传感器和红外线传感器探测障碍物的距离和方向,采集的各种数据利用T-S模糊神经网络进行融合。通过实验仿真表明:此方法能够使智能小车对障碍物的灵活避障和导航行进。     

卡尔曼滤波器结合模糊PID的控制系统

根据控制对象的特性,把卡尔曼滤波器和采用参数自适应的模糊PID算法相结合,在具有控制干扰和测量噪声的恶劣环境下,实现对PID参数在线调整的同时,基本消除干扰和噪声影响.应用MATLAB软件编程进行仿真,结果表明相对于传统PID和模糊PID控制,本控制系统对被控对象的时变性和非线性具有良好的适应能力,对控制干扰和测量噪声...     

Comparison of crisp and fuzzy character neural networks inhandwritten word recognition

Experiments comparing neural networks trained with crisp and fuzzy desired outputs are described. A handwritten word recognition algorithm using the neural networks for character level confidence assignment was tested on images of words taken from the United States Postal Service mailstream. The fuzzy outputs were defined using a fuzzy k-nearest neighbor algorithm. The crisp networks slightly outperformed the fuzzy networks at the character level but the fuzzy networks outperformed the crisp networks at the word level. This empirical result is interpreted as an example of the principle of least commitment     

Neuro-fuzzy synergism to the intelligent system for edge detection and enhancement

The paper presents a fuzzy neural network system for edge detection and enhancement. The system can both: (a) obtain edges and (b) enhance edges by recovering missing edges and eliminate false edges caused by noise. The research is comprised of three stages, namely, adaptive fuzzification which is employed to fuzzify the input patterns, edge detection by a three-layer feedforward fuzzy neural network, and edge enhancement by a modified Hopfield neural network. The typical sample patterns are first fuzzified. Then they are used to train the proposed fuzzy neural network. After that, the trained network is able to determine the edge elements with eight orientations. Pixels having high edge membership are traced for further processing. Based on constraint satisfaction and the competitive mechanism, interconnections among neurons are determined in the Hopfield neural network. A criterion is provided to find the final stable result that contains the enhanced edge measurement. The proposed neural networks are simulated on a SUN Sparc station. One hundred and twenty-three training samples are well chosen to cover all the edge and non-edge cases and the performance of the system will not be improved by adding more training samples. Test images are degraded by random noise up to 30% of the original images. Compared with standard edge detection operators and enhancement techniques, the proposed system based on the neuro-fuzzy synergism obtains very good results.     

环形激光陀螺的神经网络消噪

分别应用BP神经网络和DRNN网络对处于静止和运动状态下的环形激光陀螺输出信号中的噪声进行了消除,并应用Allan方差方法对消噪前后的陀螺信号进行了对比分析.结果表明使用神经网络对RLG进行消噪是可行的,而DRNN网络作为一种动态网络,其去噪效果及陀螺初始启动时的信号跟踪能力要优于静态BP网络.使用神经网络去噪的方法对研究环形激光陀螺的误差补偿及快速启动是有实际意义的.