基于OSV模型的迭代规则化医学图像去噪方法

提出一种基于Osher-Sole-Vese(OSV)模型的迭代规则化医学图像去噪方法.该方法使用迭代规则化方式,通过加入分解出的纹理和细节震动信号以修止原始OSV模型的全变分去噪结果,在一定程度上克服了医学图像去噪过程中对细节和纹理信息的过渡平滑.同时给出了迭代规则化医学图像去噪方法的实现步骤.实验结果表明,新方法能较好地保护医学图像的细节和纹理信息.     

基于OSV分解的图像多尺度特征表示

 本文给出一种新的图像多尺度表示算法.首先,应用OSV模型得到图像的单尺度分解;其次,针对上一步的信息亏损,引入不同的单调尺度参数,迭代OSV变分模型,从而为图像的不同特征提供一种非线性的分级自适应表达式.同时,本文也给出有关新算法的离散格式.数值实验表明,与已有的Nezzar算法相比,新算法的多尺度分解效果更佳.     

基于JRM的战场环境红外图像实时生成

为了满足新一代红外成像目标仿真系统的性能需求,生成逼真的战场环境红外图像供红外成像制导武器进行半实物仿真试验,研究了一种基于JRM的战场环境红外图像生成方法。首先利用3DSMAX建立目标的三维模型,并从导引头视场需求出发,结合目标地区的卫星影像数据与高程数据建立背景区域的三维模型;然后利用JRM的GenesisMC工具、SigSim工具和SenSim工具分别对物理材质特性及目标热源、场景红外特性、传感器特性进行建模;最后使用OSV工具实时渲染生成红外图像。实验结果表明,该方法可满足红外成像目标仿真系统的红外图像实时生成要求,具有灵活性强,效果良好等优点。     

一种基于角色的使用控制授权模型

综合基于角色的访问控制RBAC模型和使用控制UCON模型各自的优势,提出了一种基于角色的使用控制授权模型.该模型基于属性分配角色,通过授权规则、上下文信息约束和属性更新机制来实现动态授权并能有效降低授权管理的规模.对该模型的基本元素进行了形式化描述,并用动作时态逻辑TLA来分析该模型的动态性和安全性,最后分析了该模型的特点.     

基于有效路径统计网络的群组命令传输模型

Internet在动态环境及资源有限条件下,其尽力而为的服务模式在支持群组命令传输过程中,容易产生路径过期无效及路径竞争问题.对此,定义出有效路径统计网络,并提出基于有效路径统计网络的群组多约束多目标优化问题.针对该问题,提出基于有效路径统计网络的群组命令传输模型.为解决路径无效过期,该模型基于动态追逐解的思想提出基于动态环境下群体激励算法.为解决路径竞争,该模型分别从竞争选择策略以及避让选择策略两个角度分析了路径竞争问题.最后,本文分别证明模型的收敛性和有效性.实验分别从响应延迟率及传输成功率等方面,验证了该模型在支持群组命令传输的合理性.     

基于智能视觉的近海支持船靠泊作业安全监控技术

为了提高近海支持船(OSV)靠泊作业的安全性,需要进行靠泊作业的安全监控设计,提出基于智能视觉的近海支持船靠泊作业安全监控技术。对近海支持船靠泊作业进行三维图像采集,在计算机视觉下对采集的图像进行边缘轮廓特征提取,结合关键点的帧扫描技术进行靠泊作业的关键点判断和危险状态识别报警。进行视觉分析和监控,在三维视觉下进行近海支持船靠泊作业的过程重构,实现安全监控。仿真结果表明,采用该方法进行近海支持船靠泊作业的智能视觉监控,能准确进行危险报警和船舶靠泊位置的动态分析。     

铁路电视单频网系统测试和性能分析

介绍了中国铁路电视单频网系统的测试结果和性能分析,描述了系统测试环境和基础网络设备组成.由于这个系统设计基于简化信道模型的思想,因此之后先从理论上分析了信道简化模型所带来的优势.然后,现场测试结果验证了基于定向天线覆盖和分集接收机的网络设计的有效性.测试结果与理论分析相一致,证明了该系统很适合于中国铁路电视这一特殊应用...     

一种基于本体的信息检索模型

为解决传统的基于关键词的信息检索只能从语法上分析关键词、进行关键词的机械匹配,导致检索缺乏语义的问题,提出了一个基于本体的语义网检索模型,并以此为基础构建一个系统原型.实践结果表明,该检索模型能够在一定程度上改善检索效果.     

密码协议的SPIN建模和验证

为了将模型检测这种强有力的系统验证技术应用于网络协议的安全分析,形式化建模仍然是目前的关键问题和难点所在.本文提出了一种基于高级过程描述语言的建模方法.根据入侵者角色和攻击目标的不同,从入侵者的角度分析协议的运行模式,为每个主体建立过程模型,用模型检测工具进行分析验证.对BAN-Yahalom协议的SPIN分析验证了这种方法的可行性.该方法具有一定的通用性,对其它网络协议的分析有很好的参考价值.     

基于数据挖掘的WLAN告警分析

从告警数据库大量的告警数据中提取有用的信息是WLAN故障管理需要解决的主要问题之一.文中基于WLAN告警特点,提出了一种对告警数据库进行知识发现的数据挖掘模型,该模型结合了告警过滤和相关性分析,具有高效、易于理解和操作的优点.     

Decorrelating the Structure and Texture Components of a Variational Decomposition Model

The observation has been made by Aujol and Gilboa that the cartoon and texture components of the decomposition of an image should not be correlated, as they are generated from independent processes. They use this observation in order to choose an optimal fidelity parameter lambda for the decomposition process. However, this determination can be quite inefficient since a wide range of parameters lambda must be searched through before an estimated optimal parameter can be found. In the present paper, we take a different approach, in which the cartoon and texture components are explicitly decorrelated by adding a decorrelation term to the energy functional of the decomposition model of Osher, Sole, and Vese (the OSV model). Decomposition results of improved quality over those from the OSV model are obtained, as quantified by a series of new decomposition quality measures, with cartoon and texture information better separated into their respective components. A new derivation of the OSV model is developed which maintains the texture subcomponents g₁ and g₂ so that discrimination results similar to those from other decomposition models (e.g., from the model of Vese and Osher and Improved Edge Segregation) may be obtained. This derivation is extended to the proposed model, for which discrimination results are obtained in a substantially smaller number of iterations.     

Image selective restoration using multi-scale variational decomposition

In this paper we propose a multi-scale variational decomposition model for image selective restoration. Firstly, we introduce a single-parameter (BV, G, L²) variational decomposition functional and theoretically analyze the relationship between the parameter and the scale of image features. And then, by replacing the fixed scale parameter with a varying sequence in the single-parameter decomposition functional, we obtain the multi-scale variational decomposition which can decompose the input image into a series of image slices of different scales. Furthermore, we show some properties and prove the convergence of the multi-scale decomposition. Finally, we introduce an alternating and iterative method based on Chambolle’s projection algorithm to numerically solve the multi-scale variational decomposition model. Experiments are conducted on both synthetic and real images to demonstrate the effectiveness of the proposed multi-scale variational decomposition. In addition, we use the multi-scale variational decomposition to achieve image selective restoration, and compare it with several state-of-the-art models in denoising application. The numerical results show that our model has better performance in terms of PSNR and SSIM indexes.     

基于剪切波的变分图像放大方法

针对图像放大的Chambolle 变分模型会出现阶梯效应的现象, 文中提出了一种基于Shearlet光滑分解空间的变分模型。利用有界变差空间和Shearlet分解空间的关系,特别是Shearlet分解空间的半范与加权Shearlet系数之间的等价关系,将所求的变分问题转化为基于Shearlet域的变分问题,其解归结于简单的Shearlet阈值。实验仿真表明,该方法放大后的图像有效地消除了阶梯块效应,保持了更多的细节,具有更高的峰值信噪比。     

Unsupervised learning of multi-task deep variational model

We propose a general deep variational model (reduced version, full version as well as the extension) via a comprehensive fusion approach in this paper. It is able to realize various image tasks in a completely unsupervised way without learning from samples. Technically, it can properly incorporate the CNN based deep image prior (DIP) architecture into the classic variational image processing models. The minimization problem solving strategy is transformed from iteratively minimizing the sub-problem for each variable to automatically minimizing the loss function by learning the generator network parameters. The proposed deep variational (DV) model contributes to the high order image edition and applications such as image restoration, inpainting, decomposition and texture segmentation. Experiments conducted have demonstrated significant advantages of the proposed deep variational model in comparison with several powerful techniques including variational methods and deep learning approaches.     

变分框架下的多尺度图像恢复与重建

变分图像分解,通过极小化能量泛函将图像分解为不同的特征分量,可以被应用到图像的恢复和重建.提出了变分框架下的多尺度图像恢复和重建的思想.基于这种思想,首先提出了一个单参数的（BV,G,E）三元变分分解模型,并且理论分析了参数与不同特征分量的尺度的关系.然后将此模型的参数选为一个二进制序列,得到多尺度的（BV,G,E）变分分解.该多尺度变分分解可以将图像分解为一序列图像结构、纹理和噪声.证明了此多尺度分解的收敛性并且基于对偶理论和交替迭代算法给出了其数值求解方法.最后将提出的多尺度的（BV,G,E）变分分解应用到图像恢复和重建,实验结果证实了理论分析的正确性,显示了将此模型进行图像多尺度恢复和重建的有效性,和与一些其他分解模型相比较的优越性.     

基于修正小波阈值的图像变分分解

Daubechies等人(2004)首先提出了图像的变分分解和小波软阈值之间的联系。小波软阈值会对图像边缘造成过度光滑,使重构图像在边缘附近产生吉布斯震荡现象,为克服该问题,本文用具有更高正则性的分段n次多项式小波阈值和指数阈值做图像分解,得到图像分解的变分泛函的近似最小值。当n越大时,图像分解的变分问题的近似最小值越逼近精确最小值。这样得到了图像的变分分解和修正小波阈值之间的联系。实验结果表明该模型用于图像分解的有效性。     

Image decomposition using adaptive second-order total generalized variation

This paper proposes a new model for image decomposition which separates an image into a cartoon, consisting only of geometric objects, and an oscillatory component, consisting of textures or noise. The proposed model is given in a variational formulation with adaptive second-order total generalized variation (TGV). The adaptive behavior preserves the key features such as object boundaries and textures while avoiding staircasing effect. To speed up the computation, the split Bregman method is used to solve the proposed model. Experimental results and comparisons demonstrate the proposed model is more effective for image decomposition than the methods of the state-of-the-art image decomposition models.     

Variational Image Restoration and Decomposition in Shearlet Smoothness Spaces

We present the shearlet-based variational model for image restoration and decomposition.The new model can be seen as generalizations of Daubechies-Teschke's model.By using regularization term in shearlets smoothness spaces,and writing the problem in a shearlet framework,we obtain elegant shearlet shrinkage schemes.Furthermore,the model allows us to incorporate general bounded linear blur operators into the problem.The experiments on denoising,deblurring and decomposition of images show that our algorithm is very efficient.     

A primal-dual gradient method for image decomposition based on (<Emphasis Type="Italic">BV</Emphasis>, <Emphasis Type="Italic">H</Emphasis><Superscript>−1</Superscript>)

The main aim of this paper is to accelerate the image decomposition model based on (BV, H ⁻¹). It is solved with a particularly effective primal-dual gradient descent algorithm. The algorithm works on the primal-dual formulation and exploits the information of the primal and dual variables simultaneously. It converges significantly faster than some popular existing methods in numerical experiments. This approach is to some extent related to projection type methods for solving variational inequalities.