同位素扫描医学图象的三维重建

本文提出了一个图象处理系统,通过同位素扫描仪器获得的人体正、侧投影图象,实现人体器官的三维图象重建。文中介绍了改进的最小费用流算法实现图象重建的原理,以及实际处理中正、侧面图象配准等问题的处理方法。     

一种约束最小模方估计的图像迭代重建算法

较少投影重建问题一直是成像领域研究的重点，传统的卷积反投影(CBP)不能产生满意的重建图像——伪影严重、误差大，而迭代算法却具有一些较好的特性。本文以CT成像为基础，以最小模方为目标函数提出了一种约束迭代重建算法，本算法与无约束迭代算法相比，在花费相同时间的情况下，可以重建出误差更小，拟合度更高的图像。以扇束扫描投影数据重建的结果验证了本文的结论。     

采用高频能量的CT几何参数自标定方法

针对锥束CT系统中几何参数失配引起几何伪影的问题,提出了一种采用空域高频能量的几何伪影自校正算法。该算法以重建图像的空域高频能量为目标函数建立优化模型,通过单纯形法迭代求解使空域高频能量最大的几何参数最优解。利用投影图像的特性提取部分参数作为输入初值,减小算法搜索范围。并采用图形处理器（Graphic Processing Unit,GPU）对自校正过程中的图像重建并行加速,减少重建时间,提高校正速度。实验结果表明:该算法具有较高的求解精度,最大相对误差不超过5%,对重建图像中的几何伪影有较好的校正效果。同时,在不影响精度的情况下减少了迭代次数,算法执行效率提高了18.8%。     

神经网络在少数投影图像重建中的应用

本文提出了将两种神经网络模型进行套用的图像重建算法。算法将图像重建问题转化为神经网络优化计算,利用Hopfield神经网络（HNN）将各种优化准则转化为模型的能量函数,并应用Adaline网络调整各准则在能量函数中所占的比重,从而求取图像重建的最优解。将其应用于少数投影图像重建,体现出重建精度高、收敛快等特点,取得了令人满意的效果。     

基于非局部相似块低秩的压缩感知图像重建算法

传统的压缩感知重建算法利用信号在某个特征空间下的稀疏性构建目标优化函数,但没有充分考虑信号的局部特性和结构化属性,影响了算法的重建性能和算法的适应性.本文考虑图像的非局部自相似性(NonlocalSelf-Similarity,NLSS),提出一种基于图像相似块低秩的压缩感知图像重建算法,将图像恢复问题转化为聚合的相似块矩阵秩最小问题.算法以最小压缩感知重建误差为约束构建优化模型,并采用加权核范数最小化算法(Weighed Nuclear Norm Minimization,WNNM)求解低秩优化问题,很好地挖掘了图像自身的信息和结构化稀疏特征,保护了图像的结构和纹理细节.多个测试图像、不同采样率下的实验证明了算法的有效性,特别是在低采率下对于纹理较为丰富的图像,提出的算法图像重建质量较明显的优于最新的同类算法.     

基于Hopfield神经网络的DS—CDMA多用户检测

从新的角度研究ＤＳ－ＣＤＭＡ（直接序列码分多址）系统中的多用户检测，将多用户检测的优化问题映射为Ｈｏｐｆｉｅｌｄ神经网络（ＨＮＮ）“能量”函数的最小化问题，利用连续ＨＮＮ固有的快速下降特性，实现了坟对ＣＤＭＡ（码分多址）系统的多用户检测。与现有各种方案比较，具有运算量小、抗远近效应强、实时性好等优点。     

用逼近定理解决有限角图象重建问题

本文提出了有限角图象重建的新方法。Ｗｅｉｅｒｓｔｒａｓｓ三角多项式逼近定理用来估计缺失角外的投影数据，补全投影数据后再用常用的滤波反投影算法重建图象。文章给出并证明了投影值函数连续的一个充分条件，把该方法与已有的方法作了比较，说明了它的优良性能和应用前景。     

自适应精简点云改进预处理优化三维重建算法

针对初始点云离群点噪声大、冗余性高导致三维重建效率低、重建曲面表面粗糙等问题，提出一种自适应精简点云改进预处理算法。首先使用统计滤波消除离群点噪声，并在基于体素重心邻近特征点下采样中引入双曲正切函数，在保持点云特征不变的情况下精简点云数据；然后建立移动最小二乘法拟合函数，确定其二次基函数和高斯权函数，完成点云数据平滑优化；最后使用投影三角化算法完成点云曲面重建。实验结果表明，所提算法在有效去除离群点的同时，还能精简点云数据、提升曲面重建效率，且重建后的模型表面光滑、孔洞减少。     

自旋式综合孔径微波辐射计稀疏阵列布局优化设计

该文针对自旋式综合孔径微波辐射计非均匀采样问题,提出新的阵列优化目标函数与阵列优化算法。首先,针对Cornwell提出的基线距离乘积最大目标函数优化稀疏阵列会出现基线中心与边缘区域密集而过渡区域稀疏的问题,该文提出修正的电荷最小能量分布目标函数以及基于最小误差网格剖分的方法。针对标准的粒子群优化(PSO)算法历史最优个体位置更新速度慢,容易陷入局部极小值的缺点,提出具有量子体制的雁群粒子群优化算法。该算法具有速度快、收敛精度高的优点。数值分析结果表明利用该文引入的目标函数优化的基线比距离乘积最大目标均匀,并且基于最小误差网格剖分的方法对应的重构图像更精确。该方法为实际自旋式稀疏阵列的设计与应用提供依据。     

基于全变差的加权最小二乘法PET图像重建

传统的加权最小二乘法、惩罚项加权最小二乘法虽然能够重建得到较好质量的图像,但在欠采样的条件下不能很好的拟制噪声.全变差作为正则项已广泛用于图像重建中,利用图像稀疏的先验知识能够在欠采样的条件下很好的重建图像.本文结合加权最小二乘法和全变差的优点,构造了基于全变差正则项的加权最小二乘法目标函数,运用交替求解的方法,将目标函数分解为求解二次优化和全变差正则化的优化问题,并分别用超松弛迭代方法和梯度下降法求解这两个优化问题.采用Zubal模型对该算法与传统算法进行仿真验证比较,并用相关系数、方差、信噪比等参数描述图像重建质量.结果表明在欠采样条件下,该算法能够更好的拟制噪声,重构效果比传统的有明显地提高.     

Sequential and parallel image restoration: neural networkimplementations

Sequential and parallel image restoration algorithms and their implementations on neural networks are proposed. For images degraded by linear blur and contaminated by additive white Gaussian noise, maximum a posteriori (MAP) estimation and regularization theory lead to the same high dimension convex optimization problem. The commonly adopted strategy (in using neural networks for image restoration) is to map the objective function of the optimization problem into the energy of a predefined network, taking advantage of its energy minimization properties. Departing from this approach, we propose neural implementations of iterative minimization algorithms which are first proved to converge. The developed schemes are based on modified Hopfield (1985) networks of graded elements, with both sequential and parallel updating schedules. An algorithm supported on a fully standard Hopfield network (binary elements and zero autoconnections) is also considered. Robustness with respect to finite numerical precision is studied, and examples with real images are presented.     

Multiobjective neural network for image reconstruction

The authors propose a multiobjective neural network model and algorithm for image reconstruction from projections. This model combines the Hopfield model and multiobjective decision making approach. A weighted sum optimisation based neural network algorithm is developed. The dynamic process of the net is based on minimisation of a weighted sum energy function and Euler's iteration and this algorithm is applied to image reconstruction from computer-generated noisy projections and Siemens Somaton DR scanner data, respectively. Reconstructions based on this method are shown to be superior to those based on conventional iterative reconstruction algorithms such as MART (multiplicate algebraic reconstruction technique) and convolution from the point of view of accuracy of reconstruction. Computer simulation using the multiobjective method shows a significant improvement in image quality and convergence behaviour over conventional algorithms     

Regularized image reconstruction using SVD and a neural networkmethod for matrix inversion

Two methods of matrix inversion are compared for use in an image reconstruction algorithm. The first is based on energy minimization using a Hopfield neural network. This is compared with the inverse obtained using singular value decomposition (SVD). It is shown for a practical example that the neural network provides a more useful and robust matrix inverse     

能量极小化的一种启发式遗传算法

Chakradhar et.al(1988,1990)将组合电路表示为Hopfield神经网络,将测试生成问题转化为一个组合优化问题。本文在传统遗传算法的基础上,结合电路的拓扑信息,提出了一种用于组合电路神经网络模型能量极小化的启发式遗传算法。     

反馈电压偏置型Hopfield 网络电域盲检测相干光通信信号

为解决无线分集相干光接收机的自适应盲检测问题,提出了一种新的离散时间连续状态的网络输出反馈偏置型的复Hopfield 神经网络用以解决多值QAM 信号的盲检测问题。反馈电压偏置的引入即不脱离传统Hopfield 模型,又能有效满足多值信号检测时所需的搜索空间变大的特殊要求。全文完成多值信号盲检测的优化问题构造和能量函数的映射,给出能量函数的证明、分析和它的约束条件,给出适用该问题的激活函数的基本特征,正确盲检测信号的权矩阵的配置方法。最后,通过详细的仿真结果展示和与其他算法性能对比进一步验证算法的有效性和优越性并指出算法所存在的问题和下一步的研究方向。     

Super-resolution target identification from remotely sensed imagesusing a Hopfield neural network

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded     

The application of competitive Hopfield neural network to medical image segmentation

In this paper, a parallel and unsupervised approach using the competitive Hopfield neural network (CHNN) is proposed for medical image segmentation. It is a kind of Hopfield network which incorporates the winner-takes-all (WTA) learning mechanism. The image segmentation is conceptually formulated as a problem of pixel clustering based upon the global information of the gray level distribution. Thus, the energy function for minimization is defined as the mean of the squared distance measures of the gray levels within each class. The proposed network avoids the onerous procedure of determining values for the weighting factors in the energy function. In addition, its training scheme enables the network to learn rapidly and effectively. For an image of n gray levels and c interesting objects, the proposed CHNN would consist of n by c neurons and be independent of the image size. In both simulation studies and practical medical image segmentation, the CHNN method shows promising results in comparison with two well-known methods: the hard and the fuzzy c-means (FCM) methods.     

一种基于神经网络的多层通孔最小化方法

在多层布线的线段－相交图模型基础上，利用Ｈｏｐｆｉｅｌｄ人工神经网络理论，通过反通孔数目这个优化目标与Ｈｏｐｆｉｅｌ网络能量函烽相联系的方法来解决多层布线通孔最小化问题。算法考虑了许多来自实际的约束。     

广义Hopfield网络求解TSP

本文提出了用广义Ｈｏｐｆｉｅｌｄ网络求解ＴＳＰ的改进算法，较之用Ｈｏｐｆｉｅｌｄ网络求解ＴＳＰ的传统算法，新算法改进之处主要有两点，一、引入了辅助单元（本文称之为快单元）从而可以更加灵活构造能量函数。二、采用新的单元输入输出函数，并调整单元的自反馈和阈值，从而实现能量补偿，抵消能量误差，模拟结果表明，新算法优于传统的Ｈｏｐｆｉｅｌｄ网络算法。