改进Canny算法在钢印数字特征提取中的应用

边缘检测是实现图像分割、特征提取和图像理解的基础.研究了传统Canny算子的优势与不足.在此基础上,提出了一种快速分块自适应Canny算法.方法首先按字符大小分割图像,然后在每一块上进行自适应边缘检测.自适应边缘检测是在平滑图像的同时得到高斯滤波尺度参数,然后采用Otsu方法的自适应阈值计算Canny算子的高、低门限值.实验结果表明,方法不需人工设定参数就能自动提取不同光照背景下的钢印数字边缘,而且能有效抑制噪声,与传统Canny算子相比,边缘连接程度最佳,噪声敏感程度较低,实时性较强.     

基于改进Canny算子的图像边缘检测方法

经典Canny图像边缘检测算法在面对复杂背景和椒盐噪声时会出现伪边缘或漏检等问题,影响后续图像分割,目标检测和识别.针对经典Canny算法高斯滤波和人工门限设置2个步骤进行优化改进,首先提出一种循环自适应滤波方法代替高斯滤波对图像进行平滑降噪,提升椒盐噪声抑制性能的同时较好的保留了图像中的细节信息,然后提出一种最小类内类间距准则的2-均值算法自动确定高低阈值门限,相对于人工门限设置方法具有更高的精确性和更强的适应性.基于标准图像库数据开展试验,结果表明所提方法可以明显提升经典Canny算法的椒盐噪声鲁棒性和复杂背景下的边缘检测性能.     

基于方向信息测度的图像边缘检测

边缘检测是图像处理中—个重要的研究课题．针对传统图像边缘检测算法对噪声敏感的问题,本文在分析图像像素灰度信息的基础上,建立了—个改进的确定方向信息测度的方法,并利用震动滤波对边缘检测图像进行增强,该方法在滤除噪声的同时,能有效地保留图像的基本目标信息,正确提取图像的边缘．     

一种去除图像中Cauchy噪声的滤波算法

数字图像在采集、传输等过程中会产生各种噪声,噪声特征不同,处理方法也不同,如何尽可能恢复被强噪声干扰的图像是一个有意义的研究课题,因为传统的滤波算法在强噪声情况下,难以得到理想的结果.该文在中值滤波的基础上,结合局部区域内像素聚类的思想,提出一种对图像边缘进行修正的滤波算法,对三幅被Cauchy噪声干扰的图像处理的结果表明,该算法弥补了中值滤波在细节处理方面的不足,和其它方法相比,在滤除强噪声并保护图像细节和边缘方面有明显提高,是一种有效的去除强噪声的滤波算法.     

基于小波变换的图像去噪方法的研究

小波变换能有效的去除高斯噪声,中值滤波能有效的去除脉冲噪声,两者结合可以更有效的去除高斯噪声和脉冲噪声的混合噪声.当医学图像去除混合噪声时,先进行中值滤波再进行小波去噪的方法优于先进行小波去噪后再进行中值滤波的方法,且去噪后图像视觉效果较好,而且图像均方误差(M SE)也较小.在图像去噪处理中这种方法具有实际应用价值.     

基于模糊中值滤波的椒盐噪声去除方法

研究基于模糊中值滤波的椒盐噪声去除方法。通过比较图像各像素点的灰度值,定义基于图像梯度信息的各点被判别为噪声点的模糊隶属函数。利用此模糊隶属函数对中值滤波方法进行加权,得到了一种加权中值滤波器,可实现边缘处椒盐噪声的有效滤除。讨论这种模糊加权方法与其它先进滤波方法的结合途径,指出了其推广应用价值。最后利用数值实验验证本文方法的有效性,结果表明,相比于自适应中值滤波方法,本文方法得到的滤波图像在峰值信噪比及结构相似度方面均有明显提高。     

一种带椒盐噪声的图像恢复快速方法

研究被椒盐噪声干扰的模糊图像恢复问题.提出了一种利用两步法来恢复图像的快速算法.第一步,用自适应中值滤波(adaptive median filter,AMF)识别被噪声干扰的图像的像素.第二步,基于无噪声的像素,对图像进行恢复.利用交替方向极小化,提出了一种带椒盐噪声的图像恢复快速方法.实验结果显示,该方法较其他现有的两步法恢复图像的效果更好.     

小波尺度函数计算的广义高斯积分法及其应用

对于小波尺度函数变换的分解系数的积分运算建立了以尺度函数为权的广义高斯积分方法的运算格式．借助于样条函数,证明了其广义高斯积分随小波分解水平（resolutionlevel）指标的上升而收敛．在此基础上给出了以小波尺度函数变换重构或逼近任一函数的显式解析式,并对具有函数算子、微分或积分算子的运算给出了变换规则．这对于求解复杂非线性方程（组）是一种强有力的工具．最后给出了用该文方法求解非线性二点边值问题的算例．     

一种结合小波模极大值法和Canny算子的目标提取方法

针对基于小波变换的目标提取中忽略低频子图像的一些重要信息的问题.提出了一种基于小波变换的模极大值法和Canny算子的目标提取方法.在小波域中,通过求解局部小波系数模型的极大值点提取(检测)高频边缘,利用Canny算子提取(检测)低频边缘.然后根据融合规则对两个子图像边缘进行融合.实验结果表明,该方法不仅能有效地增强图像边缘,而且能准确地定位图像边缘.     

一种改进的四阶偏微分方程图像去噪模型

针对四阶偏微分方程图像去噪模型对图像平滑区域处理造成不平整现象,以及无法去除椒盐噪声的问题.首先对含噪图像进行高斯滤波,然后通过修改扩散系数得到一个改进的四阶偏微分方程图像去噪模型.MATLAB仿真结果表明:新模型与原四阶偏微分方程去噪模型相比,其去噪图像不仅视觉效果好;而且峰值信噪比也高;另外,新模型还能有效去除椒盐噪声.     

ESL-SELO: A robust image denoising algorithm with penalty

Robust image recovery methods have been attracted more and more attention in recent decades for its good property of tolerating system errors or measuring noise. In this paper, we propose a new robust method (ESL-SELO) to recover nosing image, which combine exponential loss function and seamless-L0 (SELO) penalty function to guarantee both accuracy and robustness of the estimator. Theoretical result showed that our method has a local optimal solution and good asymptotic properties. Finally, we compare our method with other methods in simulation which shows better robustness and takes much less time.     

Complex diffusion filtering of three dimensional turbulent flows

The linear and nonlinear complex diffusion filtering methods are proposed to extract the organized coherent part as well as the random incoherent part from forced and decaying turbulent flows. An attempt to examine the robustness of the two methods in filtering the turbulent flow field without the transformation into the frequency domain is carried out. The velocity fields of the forced and decaying cases are decomposed into coherent and incoherent parts in the spatial domain. The complex diffusion process can be obtained by combining the linear diffusion equation and the free particle Schrodinger equation. The imaginary parts in the two methods serve as a robust edge-detector with increasing confidence in time. The numerical implementations of the 3D linear and nonlinear complex diffusion partial differential equations are done using the finite difference method. The flatness, skewness and spectrum of the extracted fields are also studied for each filtering method. Results show that the two filtering methods can identify the coherent fields and preserve the features of the turbulent fields. Comparisons to the wavelet and Fourier decompositions are also considered.     

Noise reduction method for vibration signals 2D time‐frequency distribution using anisotropic diffusion equation

Threshold noise reduction methods of vibration signals have been widely researched and used. However, these methods are less efficient in such situation, including requiring a time‐consuming and subjective to manual editing because different degree of noise signal requires selecting different characterization for filtering. In this paper, an efficient denoising method based on PDE for mechanical vibration signals time‐frequency distribution is investigated, in which, a one‐dimensional vibration signal is transformed into 2D time‐frequency domain by using Gabor transform. This enables (i) simultaneously utilize both time and frequency characteristic for effectively multiple dimension signal denosing and (ii) isotropic and anisotropic characteristics to be imposed by employing PDE, which explicitly fit with the local structure of time‐frequency signal. This paper analyzes the basic methods of isotropic and anisotropic diffusion filtering, investigates the anisotropic diffusion method based on local feature structure of 2D information, and conducts a set of comparative tests. Experiments show that this proposed method has a better performance of denoising than that of thresholding. At the same time, it is more handy than that of other methods, such as independent component analysis. Finally, problems and ways of improving the PDE‐based filter method are analyzed in this paper. Copyright © 2014 John Wiley & Sons, Ltd.     

基于相空间重构的降噪技术研究

探讨了基于相空间重构的局部线性映射算法在非线性时间序列降噪技术中的应用,并给出了算法中主要参数的选取方法.实验结果表明,该算法的降噪效果明显优于传统的线性信号滤波技术.并且针对多数实测数据的原始动态模型未知的特点,提出通过计算降噪前后时序信号的关联维数作为评判降噪效果的工具,克服了已有方法中无法计算该类时序信号降噪水平的缺点.     

Ergodicity and Accuracy of Optimal Particle Filters for Bayesian Data Assimilation

Data assimilation refers to the methodology of combining dynamical models and observed data with the objective of improving state estimation. Most data assimilation algorithms are viewed as approximations of the Bayesian posterior (filtering distribution) on the signal given the observations. Some of these approximations are controlled, such as particle filters which may be refined to produce the true filtering distribution in the large particle number limit, and some are uncontrolled, such as ensemble Kalman filter methods which do not recover the true filtering distribution in the large ensemble limit. Other data assimilation algorithms, such as cycled 3DVAR methods, may be thought of as controlled estimators of the state, in the small observational noise scenario, but are also uncontrolled in general in relation to the true filtering distribution. For particle filters and ensemble Kalman filters it is of practical importance to understand how and why data assimilation methods can be effective when used with a fixed small number of particles, since for many large-scale applications it is not practical to deploy algorithms close to the large particle limit asymptotic. In this paper, the authors address this question for particle filters and, in particular, study their accuracy (in the small noise limit) and ergodicity (for noisy signal and observation) without appealing to the large particle number limit. The authors first overview the accuracy and minorization properties for the true filtering distribution, working in the setting of conditional Gaussianity for the dynamics-observation model. They then show that these properties are inherited by optimal particle filters for any fixed number of particles, and use the minorization to establish ergodicity of the filters. For completeness we also prove large particle number consistency results for the optimal particle filters, by writing the update equations for the underlying distributions as recursions. In addition to looking at the optimal particle filter with standard resampling, they derive all the above results for (what they term) the Gaussianized optimal particle filter and show that the theoretical properties are favorable for this method, when compared to the standard optimal particle filter.     

Towards a Mathematical Theory of Super‐resolution

This paper develops a mathematical theory of super‐resolution. Broadly speaking, super‐resolution is the problem of recovering the fine details of an object—the high end of its spectrum—from coarse scale information only—from samples at the low end of the spectrum. Suppose we have many point sources at unknown locations in [0,1] and with unknown complex‐valued amplitudes. We only observe Fourier samples of this object up to a frequency cutoff f_c. We show that one can super‐resolve these point sources with infinite precision—i.e., recover the exact locations and amplitudes—by solving a simple convex optimization problem, which can essentially be reformulated as a semidefinite program. This holds provided that the distance between sources is at least 2/f_c. This result extends to higher dimensions and other models. In one dimension, for instance, it is possible to recover a piecewise smooth function by resolving the discontinuity points with infinite precision as well. We also show that the theory and methods are robust to noise. In particular, in the discrete setting we develop some theoretical results explaining how the accuracy of the super‐resolved signal is expected to degrade when both the noise level and the super‐resolution factor vary. © 2014 Wiley Periodicals, Inc.     

The effect of noise reduction in measuring the linear and nonlinear dependency of financial markets

The daily closing prices of several stock market indices are examined to analyse whether noise reduction matters in measuring dependencies of the financial series. We consider the effect of noise reduction on the linear and nonlinear measure of dependencies. We also use singular spectrum analysis as a powerful method for filtering financial series. We compare the results with those obtained by ARMA and GARCH models as linear and nonlinear methods for filtering the series. We also examine the findings on an artificial data set namely the Hénon map.     

Radon odd radial harmonic Fourier moments in detecting cloned forgery image

Over the past decades, due to the progress of computing abilities and easy access to powerful photo editing software, more digital photos and images are created commonplace. Unfortunately, this technology progress may also bring with a big risk of the information security. Image region cloning is a popular and simple manner to create realistic forgery images. Most relevant researches have been carried out, but the methods based on those researches are only able to detect some simple duplicated successfully. So we present a new Radon Odd Radial Harmonic Fourier Moments (RORHFMs) method. Compared with other relevant methods, this method is more robust to resist post-processed operations, such as anti-translation, anti-rotation, anti-scaling, anti-mirror operations and resisted Gaussian noise contamination. We also introduce an auxiliary circle template to slide and detect the suspicious image in order to locate the cloned region. The invariant moment features of image are extracted and analyzed by our method. Each feature of similar region is arranged orderly by Lexicographic sorting for detecting. Pearson Correlation Coefficient is applied to calculate and classify the statistical data. Then, the statistical data is searched, analyzed. At last, the original coordinates of cloned and pasted region are detected and denoted. Extensive experiments verified better robustness of RORHFMs method than other relevant methods in detecting cloned forgery.