基于小波变换法的分形维数估计在有噪图像边缘检测中的应用

文中研究了在加性高斯白噪声情况下如何用分形方法检测图像边缘，由于自然场景除边缘外外满足离散分数布朗随机场模型，因此可作为检测的标准。分形维数的时域估计法易受噪声影响，采用小波变换法计算分形维数，既简单易实现，又有较强噪声抑制能力，本方法相对经典的图象边缘检测方法不仅有更好的抗干扰性能，而且检测的图像边缘细节丰富。     

一种新的强鲁棒性的边缘检测方法

针对图像同时叠加脉冲噪声和高斯白噪声的非标准分布噪声的情况,本文提出一种新的基于统计向量和神经网络的强鲁棒性的边缘检测方法。首先选取窗口子区域内若干中间值像素点构造了由4个统计量组成的统计向量。然后计算训练图像的统计向量作为样本,对不加噪的训练图像的统计向量降维并作双阈值处理得到学习边缘图,对BP神经网络训练。最后将训练的BP神经网络直接用于边缘检测。新方法对脉冲噪声和高斯白噪声均具有较好的鲁棒性,BP神经网络的结构和训练都比较简单,而且不需要设定阈值检测边缘。     

基于分形的图像边缘检测研究

提出了一种新的图像边缘分形检测方法。该方法以具有各向同性的ＤＦＢＲ场模型作为图像区域的数学模型，定义了一种新的适用于图像边缘检测的分形参数，并将它用于图像边缘检测，实验结果证实了该方法的有效性。     

一种基于自适应Canny算子的舰船红外图像边缘检测方法

提出了一种用于舰船红外图像边缘检测的自适应Canny算子边缘检测方法。先利用图像的局部最大标准差值为参考值设定高斯滤波尺度参数,然后采用基于梯度幅度直方图的类间方差最大法计算Canny算子的高低闽值,从而实现边缘的自动检测。实验结果表明,采用该方法检测出的舰船边缘清晰连续,噪声较少,优于传统的Canny算子边缘检测方法,并且具有很好的工程适用性。     

太赫兹成像三维块匹配自适应Canny边缘检测算法

针对传统Canny算子在滤波时会模糊边缘且需要人工设置高低阈值的缺点，提出了一种基于三维块匹配的改进自适应阈值Canny边缘检测算法，并用于太赫兹三维层析成像。该算法一方面对滤波方法进行了改进，用三维块匹配(BM3D)滤波算法结合引导滤波算法代替高斯滤波算法以减少图像边缘信息的丢失；另一方面，针对传统人工设定阈值的不确定性，将梯度图进行块匹配后对三维图像块组使用最大类间方差法(OTSU)以自适应确定高低阈值。最后利用该算法对含有噪声的图像进行边缘检测处理，发现在高斯噪声方差为20时滤波后的峰值信噪比(PSNR)从22.202提升至27.151，验证了该算法去除噪声的有效性。三维块匹配改进自适应阈值Canny边缘检测算法(BM-OTSU-Canny)减少了错误边缘的数量，同时保留了连接性较好的边缘点，改善了边缘细节信息的提取效果。     

基于分形理论的红外图像边缘检测

提出了利用分形理论进行红外图像边缘检测的方法,比较了根据单一分形维数和根据多尺度分形维数两种方法的边缘检测结果,从而得出：分表理论可以用于红外图像的边缘检测,并且根据多尺度维数的方法能获得较好的边缘检测结果。     

基于LoG算子的图像边缘增强算法的改进

图像边缘增强的目的是为了突出图像所包含的信息.图像的许多有用信息集中在图像的边缘部分,从频谱上说就是图像的高频部分,因而可以用加重高频来增强图像,常规方法是直接对图像锐化,这会加剧噪声对图像的影响.本文先用高斯-拉普拉斯算子检测出图像的高频部分,然后用最大方差比方法确定图像边缘,再针对边缘对图像进行增强处理.实验表明,该方法可有效抑制边缘增强后的噪声问题.     

含噪声且较模糊的印刷电路板光电图像边缘检测研究

针对印刷电路板(PCB)光电图像在获取过程中 含有噪声和较模糊的边缘等各种原因,提出 了一种基于图像融合的含噪声且较模糊PCB光电图像边缘检测方法。首先,讨论了基于小波 变换 及Canny边缘检测算子的图像边缘检测法,分析了其基本原理。然后,结合两种方法的优点 ,提 出了基于图像融合检测方法的PCB光电图像边缘检测基本原理及中值滤波、增强去模糊、小 波分解、图像融合、图像去噪等步骤。最后,将由CCD成像系统及显微镜获取的主要含有高 斯噪声且较模糊的PCB光电图像用三种检测方法进行了主观实验对比,采用本文融合方法得 到的边缘图像效果最好,在抑制噪声的同时得到了连续的边缘;为了客观地评价PCB光电图 像边缘检测的效果,用峰值信噪比及图像边缘信息熵作为评价指标,采用本文方法的峰值信 噪比及信息熵的实验结果值都是最大的。     

改进的Canny算法

Canny边缘检测算法是一种综合评价比较好的边缘检测算法，但在应用时由于用高斯滤波器对图像进行平滑，往往引起定位精度下降并使角点变圆。针对上述问题，采用各向异性高斯滤波对Canny算法进行了改进。实验结果表明，改进后的算法在对噪声的鲁棒性、定位精度和单一响应方面均优于原始的Canny算法。     

基于小波变换的图像边缘检测

图像边缘检测是图像分割、图像识别的基础,传统的边缘检测方法对噪声非常敏感.讨论一种基于小波变换的图像边缘检测方法,仿真结果表明,该方法明显优于传统的边缘检测算法,对噪声的抑制能力强.     

基于分形盒维数的频谱感知技术研究

为了降低频谱感知的计算复杂度,提高感知性能,该文提出基于分形盒维数的频谱感知方法。由于噪声与信号盒维数值存在差异,将分形盒维数作为检验统计量。仿真结果表明,该方法在高斯白噪声环境下,具有良好的检测性能,对噪声不敏感。并且计算复杂度低,易工程实现。     

基于三阶累积量的语音信号的基音周期检测

本文提出了一种新的语音信号的基音周期检测方法,该方法根据语音信号的三阶累积量去确定语音信号的基音周期,能有效地排除白色或有色的高斯加性噪声所带来的干扰.与传统的基音周期估计的自相关函数法或平均幅度差函数法(AMDF)相比,该方法更精确、有效,具有更强的鲁棒性.     

A further note on a general likelihood formula for random signals in Gaussian noise

A general likelihood ratio formula of the author's for the detection of signals in independent white Gaussian noise is extended to allow a "one-sided" dependence in which only the future white noise is required to be independent of past signal and noise. The assumption of Gaussian additive noise is also somewhat relaxed. The proof is based on some recent martingale theorems and on the concept of the innovations process.     

小波滤波分解层数的自适应确定方法

针对加性高斯白噪声,根据信号和噪声在小波空间上传播的特性,提出了一种基于Kolmogorov-Smirnov检验的最优分解层数自适应确定算法,对于不同数据长度的信号,可以自适应地选择小波变换的最优分解层数。仿真实验表明,该方法可以得到最优的信噪比及最小均方误差,并且对小波分解边界延拓方式的选择具有较强的适应性。     

A general likelihood-ratio formula for random signals in Gaussian noise

It is shown that the likelihood ratio for the detection of a random, not necessarily Gaussian, signal in additive white Gaussian noise has the same form as that for a known signal in white Gaussian noise. The role of the known signal is played by the casual least-squares estimate of the signal from the observations. However, the "correlation" integral has to be interpreted in a special sense as an Itô stochastic integral. It will be shown that the formula includes all known explicit formulas for signals in white Gaussian noise. However, and more important, the formula suggests an "estimator-correlator" philosophy for engineering approximation of the optimum receiver. Some extensions of the above result are also discussed, e.g., additive finite-variance, not necessarily Gaussian, noise plus a white Gaussian noise component. Purely colored Gaussian noise can be treated if whitening filters can be specified. The analog implementation of Itô integrals is briefly discussed. The proofs of the formulas are based on the concept of an innovation process, which has been useful in certain related problems of linear and nonlinear least-squares estimation, and on the concept of covariance factorization.     

Signal Detection in Cognitive Radio Networks over AWGN and Fading Channels

In this paper, we consider the problem of spectrum sensing based on energy detection method in cognitive radio over wireless communication channels when users experiences fading and nonfading effects. The closed-form analytical expressions for the detection probability are derived over nonfading additive white Gaussian noise channel, Rayleigh, Rician and Nakagami-m fading channels. The detection probability involving Marcum-Q function is replaced by closed-form expression. The probability distribution function of fading channels is used to obtain the expressions for detection probability. The new derived numerical results are simulated under various parameters.     

Statistical Performance of a TFD based Detection Scheme

This paper proposes a robust signal detection procedure based on generalised patterns in time-frequency space. The paper derives a non-linear time domain correlation process which is equivalent to a time-frequency based detection scheme. The performance of this robust detection scheme in the additive white Gaussian noise case is evaluated with the aid of receiver operating curves. Some examples are given which show that the proposed procedure under nominal conditions does not perform as well as standard detection scheme (ie matched filter) but gives better performance when the measured signal deviates from its nominal model.     

一种适用于多径信道下DSSS信号检测新方法

针对多径信道下直接序列扩频信号检测的难题,在基于自相关二阶矩的检测算法基础上提出了新的检测参数,从而将加性高斯白噪声信道下的直接序列扩频信号检测算法推广到多径信道。本文算法首先分析了由多径信道导致的能量峰值和由直扩信号的伪码特性导致的自相关峰值的特点,并根据此特点定义了新的检测参数——多径因子,最后将该参数应用到基于自相关二阶矩的直接序列扩频信号检测算法中,实现了对多径信道下的直接序列扩频信号的检测。理论分析和仿真实验表明,本文提出的检测新参数能真实的反映多径信道对信号自相关函数的影响;当信噪比大于-14dB时,本文提出的多径信道下直扩信号检测算法性能与加性高斯白噪声信道下的检测性能相当。      

Parameter estimation of 2-D random amplitude polynomial-phasesignals

Phase information has fundamental importance in many two-dimensional (2-D) signal processing problems. In this paper, we consider 2-D signals with random amplitude and a continuous deterministic phase. The signal is represented by a random amplitude polynomial phase model. A computationally efficient estimation algorithm for the signal parameters is presented. The algorithm is based on the properties of the mean phase differencing operator, which is introduced and analyzed. Assuming that the signal is observed in additive white Gaussian noise and that the amplitude field is Gaussian as well, we derive the Cramer-Rao lower bound (CRB) on the error variance in jointly estimating the model parameters. The performance of the algorithm in the presence of additive white Gaussian noise is illustrated by numerical examples and compared with the CRB     

带幅频反馈的单音信号相位翻转检测

提出了一种针对ITU—T．G．168的有关规范、带有频偏估计和相位补偿的单音信号翻转检测方案。针对信号幅度有较大的动态范围,判决采用了自适应门限。信号滤波和参数提取由DPLL(数字锁相环)实现,起到了良好的效果。在TMS320C6415平台上的仿真表明,此算法在高斯白噪声信道下具有良好的检测性能和较低的计算量。