MAXIMUM ENTROPY RESTORATION METHOD OF LINEARLY DEGRADED BINARY IMAGE

This paper investigates the maximum entropy restoration of blurred binary image.In concerning with the binary property of image,a new maximum entropy restoration methodwith binary constraint is proposed.The properties of existence and uniqueness of solution arediscussed.The problem of maximum of entropy with two constraints is solved and the corre-sponding algorithm is given.In this paper,the maximum bounded entropy principle is employedconcerning the prior knowledge of binary image,and the maximum bounded entropy restora-tion method with binary constraint is put forward.The proposes methods,Wiener filter(WF)restoration method and maximum entropy restoration method are compared.The experimen-tal results show that the maximum entropy restoration method and maximum bounded entropyrestoration method with binary constraint can improve the quality of restored image.     

Research of Malicious Code Attack Effect Based on Synthetic Entropy Method

To resolve the problem of assessing the effect of a network attack, this paper combines cross entropy with network character entropy method and proposes scheme to evaluate the malicious code attack effect. It captures the related indicators in real time and normalizes the data so as to evaluate it at the same level. It adopts cross entropy method to pretreat the indicators adaptively. We calculate the weight coefficient and exploit network character entropy method to evaluate the attack with accuracy according to the importance of the indicators in the evaluation system. Experimental results and corresponding comparisons reveal that the proposed method can quantitatively determine the exact effect of the malicious code attack.     

RESEARCH ON TWO-DIMENSIONAL LDA FOR FACE RECOGNITION

The letter presents an improved two-dimensional linear discriminant analysis method for feature extraction. Compared with the current two-dimensional methods for feature extraction, the improved two-dimensional linear discriminant analysis method makes full use of not only the row and the column direction information of face images but also the discriminant information among different classes. The method is evaluated using the Nanjing University of Science and Technology （NUST） 603 face database and the Aleix Martinez and Robert Benavente （AR） face database. Experimental results show that the method in the letter is feasible and effective.     

High Accuracy Velo city Measurement Based on Keystone Transform Using Entropy Minimization

Velocity measurement is a basic task of radars. The target velocity is usually estimated according to the Doppler frequency shift. While traditional Doppler methods are unsuitable for high-speed targets, since the serious range migration between adjacent echoes causes phase wrapping. The serious range migration also inter-feres the coherent integration to improve the accuracy of the velocity estimation. A velocity measurement method based on Keystone transform using entropy minimization is studied to solve this problem. This method applies Key-stone transform to the echo to calculate the ambiguity de-gree with the help of entropy minimization. The proposed algorithm estimates the ambiguity degree with no error at a wider range of SNR than the traditional method. The ambiguous Doppler frequency is obtained according to the slow time. Theoretical analyses and simulations show that this method has very high precision.     

SOLVING THE PROPAGATION OF ELECTROMAGNETIC WAVE IN A SIMPLE TWO—DIMENSI8ONAL INHOMOGENEOUS MEDIUM BASED ON SYMPLECTIC GEOMETRICAL THEORY

A new symplectic geometrical high-frequency approximation method for solving the propagation of electromagnetic wave in the two-dimensional inhomogeneous medium is used in this paper. The propagating caustic problem of electromagnetic wave is translated into non-caustic problem by the coordinate transform on the symplectic space. The high-frequency approximation solution that includes the caustic region is obtained with the method combining with the geometrical optics. The drawback that the solution in the caustic region can not be obtained with geometrical optics is overcome by this method. The results coincide well with that of finite element method.     

Time Delay Estimation Based on Entropy Estimation

Abstract--This paper presents a novel time delay estimation （TDE） method using the concept of entropy. The relative delay is estimated by minimizing the estimated joint entropy of multiple sensor output signals. When estimating the entropy, the information about the prior distribution of the source signal is not required. Instead, the Parzen window estimator is employed to estimate the density function of the source signal from multiple sensor output signals. Meanwhile, based on the Parzen window estimator, the Renyi＇s quadratic entropy （RQE） is incorporated to effectively and efficiently estimate the high-dimensional joint entropy of the multichannel outputs. Furthermore, a modified form of the joint entropy for embedding information about reverberation （multipath reflections） for speech signals is introduced to enhance the estimator＇s robustness against reverberation.     

CHARACTERISTIC IMPEDANCE OF SPECIFIC TRANSMISSION LINES WITH OFFSET INNER CONDUCTORS

The partial charge simulation method is presented to solve the characteristicimpedance of the transmission line of specific cross section with an offset inner conductor.Thismethod has a higher accuracy due to the accurate satisfaction of the boundary condition on theouter conductor.The combined method of the Gauss elimination and optimization is used tosolve the equation of charge simulation,and it is an effective method for increasing the accuracyand assuring the convergence.The Green's functions of five transmission lines(i.e,with circular,elliptic,rectangular,trough and slab conductor)are given.     

A lossless compression algorithm of remote sensing image for space applications

A simple and adaptive lossless compression algorithm is proposed for remote sensing image compression, which includes integer wavelet transform and the Rice entropy coder. By analyzing the probability distribution of integer wavelet transform coefficients and the characteristics of Rice entropy coder, the divide and rule method is used for high-frequency sub-bands and low-frequency one. High-frequency sub-bands are coded by the Rice entropy coder, and low-frequency coefficients are predicted before coding. The role of predictor is to map the low-frequency coefficients into symbols suitable for the entropy coding. Experimental results show that the average Comprcssion Ratio （CR） of our approach is about two, which is close to that of JPEG 2000. The algorithm is simple and easy to be implemented in hardware. Moreover, it has the merits of adaptability, and independent data packet. So the algorithm can adapt to space lossless compression applications.     

Reconstruction of Tomographic Images from a Few Views by New Maximum Entropy Approach

A new algorithm for the reconstruction of tomographic images from a few views is presented.A variable metric method is used to solve the unconstrained optimization problem which resulted from the analysis by use of the maximum entropy formalism.The numerical simulation is used to study the reconstruction effect on the different asymmetric functions.The results show that the reconstruction accuracy is satisfactory.     

NEW RANGE ALIGNMENT ALGORITHM FOR ISAR BASED ON HIGH ORDER MOMENT

A new technique is proposed for range alignment in Inverse Synthetic Aperture Radar (ISAR). The basic idea is to perform range alignment using a maximum kurtosis (fourth-order central moment) criterion. After maximizing the kurtosis of the combined range profile of two adjacent echoes, the amount of range shift between them can be automatically tracked out. The combined range profile is constructed by a max operation, which only reserves the larger elements of the two echoes, and the echoes’ amplitudes are limited before they are combined. This algorithm has been used to process real ISAR data and the results demonstrate the effectiveness of the method. Compared with the correlation method and the minimum entropy method, the proposed algorithm obtains much better results in both examples in this paper. Its computation complexity has the same order of magnitude as the minimum entropy method.     

基于Tsallis交叉熵快速迭代的河流遥感图像分割

为了使河流遥感图像分割的精度和速度进一步提高,本文提出了一种基于二维Tsallis交叉熵快速迭代的河流遥感图像分割方法。鉴于现有的Tsallis交叉熵阈值法运算效率不够高,首先提出了一维Tsallis交叉熵阈值选取的快速迭代算法;然后导出了基于灰度级—邻域平均灰度级直方图的Tsallis交叉熵阈值选取公式,以进一步提高分割精度,并采用递推方式计算阈值选取准则函数中的中间变量,避免其重复运算,加快运算速度;最后,提出了二维Tsallis交叉熵阈值选取的快速迭代算法,推导出相应的公式,大大减少了运算量。大量实验结果表明,与近年来提出的4种阈值分割方法相比,本文方法在对河流遥感图像的分割效果及运行时间上均有明显优势,是河流检测与类型识别系统中可选择的一种快速有效的分割方法。      

分解的二维倒数交叉熵图像阈值选取

Shannon熵常用于表示信息平均不确定性,但因其定义基于对数函数故存在零点处无意义的缺陷,且二维交叉熵法中若能避免对数运算可使处理速度进一步提升。据此,本文提出了基于分解的二维倒数交叉熵图像阈值选取方法。首先定义了倒数交叉熵,依据分割前后图像之间的最小倒数交叉熵选取阈值;然后给出了二维倒数交叉熵定义及其阈值选取公式,提出了二维倒数交叉熵阈值选取的分解算法。通过求解两个一维倒数交叉熵的最佳阈值,再将其组合获得二维倒数交叉熵最佳阈值,由此将二维运算分解为两个一维运算,算法的计算复杂度从O(L4)降低到O(L)。大量实验结果表明,与基于粒子群优化(Particle Swarm Optimization, PSO)的二维最大Shannon熵法、基于粒子群优化的二维Shannon交叉熵法以及二维指数交叉熵法相比,本文方法的分割效果和运行速度均有优势。      

基于分解的二维指数交叉熵图像阈值分割

尽管用对数定义的Shannon熵是测度信息不确定性的有效方法,但存在无定义值和零值的问题,且现有的二维Shannon交叉熵法其运行速度仍有提升空间。为此,提出了一维和二维指数交叉熵阈值分割算法。首先给出了指数交叉熵的定义,并导出了一维指数交叉熵阈值选取方法;然后将其推广提出了基于分解的二维指数交叉熵阈值分割算法。通过分别求原像素灰度级图像和邻域平均灰度级图像的一维指数交叉熵最佳阈值,并将其组合求解二维指数交叉熵最佳阈值,从而将二维运算转换到两个一维空间上,大大缩小了搜索空间,使计算复杂度由O(L4)降为O(L)。实验结果表明,与最近提出的二维Shannon交叉熵法及二维Tsallis交叉熵法相比,所提出的方法能够得到更为优越的分割效果,且运行时间大幅减少。      

蝙蝠优化的二维Tsallis熵多阈值SAR图像分割

针对智能优化SAR图像分割算法存在计算量大、易陷入局部最优、分割精度不够等问题,融合蝙蝠算法和二维Tsallis熵多阈值,提出了一种蝙蝠优化的二维Tsallis熵多阈值SAR图像分割算法。算法利用立方映射均匀化初始蝙蝠种群,引入Levy飞行特征加强算法跳出局部最优能力,使用Powell局部搜索加快算法收敛等3方面改进蝙蝠算法;同时将二维Tsallis熵单阈值分割方法扩展到多阈值分割,建立基于多阈值的选取方法,并结合改进的蝙蝠算法,将二维Tsallis熵多阈值应用于SAR图像分割中。仿真结果表明,与其他智能优化分割算法相比,本分割算法在边缘处理和分割精度上都有明显优势。     

边缘引导和轮廓约束下的跨域香农熵最大化导向的自动阈值选取方法

为了处理诸如高斯、伽马、极值、瑞利、均匀或贝塔等基本灰度分布情形下的阈值选取难题,本文提出了一种跨域香农熵最大化导向的自动阈值选取方法.该方法利用不变的引导边缘图像和变化的约束轮廓图像共同构造出一系列持续变化的一维灰度直方图,并采用香农熵作为熵计算模型,从而得以跨越图像中若干局部区域去计算跨域香农熵,并以最大跨域香农熵对应的阈值作为最终阈值.在40幅合成图像和50幅真实世界图像上的实验结果表明,该方法虽然在计算效率方面不优于Masi熵阈值方法、Tsallis熵阈值方法、局部香农熵阈值方法和迭代三类阈值方法,但在分割适应性方面有显著增强,且在误分割率方面有显著下降.     

基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法

为了能在统一框架内处理无模态、单模态、双模态或者多模态直方图情形下的自动阈值选取问题,该文提出一种基于多尺度多方向Gabor变换的Tsallis熵阈值分割方法(MGTE)。该方法先通过Gabor变换得到多尺度乘积图像,然后利用内外轮廓图像从多尺度乘积图像中重构1维直方图,并在重构1维直方图上采用Tsallis熵计算模型来选取4个方向Tsallis熵取最大值时对应的阈值,最后对4个方向的阈值进行加权求和作为最终分割阈值。将提出的方法和5个分割方法在4幅合成图像和40幅真实世界图像上进行了实验。结果表明提出的方法虽然计算效率不占优势,但它的分割适应性和分割精度有明显的提高。     

二维直方图θ-划分Tsallis熵阈值分割算法

鉴于常用二维直方图区域直分法存在错分,最近提出的斜分法不具普遍性,而Tsallis熵与传统的Shannon熵相比,具有普适性且更为有效,本文提出了适用面更广的基于二维直方图θ-划分和最大Tsallis熵的图像阈值分割算法。首先给出了二维直方图θ-划分方法,采用四条平行斜线及一条法线与灰度级轴成θ角的直线划分二维直方图区域,按灰度级和邻域平均灰度级的加权和进行阈值分割,斜分法可视为该方法中θ=45o的特例;然后导出了二维直方图θ-划分最大Tsallis熵阈值选取公式及其快速递推算法;最后给出了θ取不同值时的分割结果及运行时间,θ取较小值时,边界形状准确性较高,θ取较大值时,抗噪性较强,应用时可根据实际图像特点及需求合理选取θ的值。与常规二维直方图直分最大Tsallis熵法相比,本文提出的方法所得分割结果更为准确,抵抗噪声更为稳健,且所需运行时间及存储空间也大为减少。      

基于改进麻雀算法的最大2维熵分割方法

为了提高最大2维熵分割的性能,提出了基于改进麻雀算法的最大2维熵分割方法,可减小运算量并且缩短计算时间.首先,融合反向学习策略和自适应t分布变异,引入精英粒子,以扩大算法搜索范围,增加算法后期局部搜索能力;其次,使用萤火虫机制,对最优解进行扰动变异,进一步增加种群多样性;最后,采用提出的改进麻雀算法寻找图像最大2维熵,...     

基于改进C-V模型的B超图像分割

图像分割是医学超声图像学中的难题之一.针对传统的水平集图像分割法速度较慢,提出了一种基于改进Chan-Vese(C-V)模型和加强中值滤波的B超图像分割方法,并与传统的C-V模型分割方法进行比较,最后用最大香农熵等方法评价分割结果.实验结果表明:改进的C-V模型B超图像分割法具有较好的分割性能,并且耗时方面明显优于传统C-V模型分割方法,可以将其应用于医学超声图像的分割中,能够到达实时性的要求.     

基于遗传算法的最大熵阈值的图像分割

图像阈值分割技术在图像分析和图像识别中具有重要的意义.最大熵方法具有很多优点,但同时也存在弱点:需要大量的运算时间,特别是在计算多阈值时.因此需要引入优化算法.文中将遗传算法用于最大熵阈值的图像分割方法中,分别对一维及二维阈值分割的情况进行讨论,并提出了一种基于改进型遗传算法的最大熵阈值图像分割方法. 通过对几幅经典图像的分割结果对比,表明了基于遗传算法的最大熵阈值的图像分割方法可以有效地提高最大熵图像分割的计算速度,提高图像处理的实时性.