应用神经网络方法数值重现离轴电子全息像—一种在实空间内高分辨率数值重现电子全息像的新方法

以实验测得的带电乳剂微粒产生的微电场的离轴电子全息图为例，首次应用神经网络－B－P网络方法成功地实现了在实空间对电子全息图的数值重现。同时提出一种新的神经网络训练方法－将含有不同噪声水平的全息图一起训练，增强了应用神经网络方法进行电子全息数值重现的抗噪声能力，从而提高数值重现的分辨率。     

电子全息高分辨率三维数值重现

对电子全息图进行计算机三维数值重现时,由于样品各层电子衍射的干扰,影响了纵向分辨率的提高。本文提出一种基于多能量电子全息图数值重现像叠加原理的新方法,可以提高电子全息数值重现纵向分辨率,从而实现真正的三维数值重现的含义。还通过计算机模拟研究的手段对该原理和方法的有效性进行了验证,得出了有价值的结果。     

电子全息高分辨率三维数值重现

对电子全息图进行计算机三维数值重现时，由于样品各层电子衍射的干扰，影响了纵向分辨率的提高。本提出一种基于多能量电子全息图数值重现像叠加原理的新方法，可以提高险息数值重现纵向分辨率，从而实现真正的三维数值重现的含义。还通过计算机模拟研究的手段对该原理和方法的有效性进行了验是出了有价值的结果。     

低能电子投射全息的计算机模拟和数值重现研究

点源发出的低能量电子波经过样品的散射后,带有物体原子结构信息的散射波(物波)和未被散射的电子波(参考波)在收集平面上相互干涉形成的电子全息图即为低能电子投射全息图.本文在合理的物理模型下,以石墨的晶体结构为试样,通过计算机模拟的方法,首次对低能电子投射全息进行了研究,讨论了一些关键参数对晶体结构电子全息的重现像和分辨率的影响,并且实现了晶体结构三维数值重现,讨论了三维重现的效果.     

俄歇电子全息的计算机模拟研究和数值重现

原子的芯能级电子吸收能量后被激发 ,经过退激发过程发射出的俄歇电子波在传播过程中被周围的原子散射 ,带有周围原子结构信息的散射波 (物波 )与未被散射的电子波 (参考波 )相干涉形成的衍射图即俄歇电子全息图 ,通过全息图的重现 ,可获得原子级分辨率的三维结构信息。本文在合理的物理模型下 ,以Cu单晶以及高温超导YBCO中的Y原子近邻的一些原子簇为计算实例 ,首次就受激原子周围单层近邻原子散射和多层原子散射产生的俄歇电子全息进行了计算机模拟和数值重现 ,获得了三维晶格结构参量 ,并讨论了不同原子序数的原子在全息图形成及重现中的作用。本工作有助于材料微结构的研究     

低能电子全息高分辨率三维数值重现研究--一种提高三维重现像分辨率的新方法

本文将一种基于多电子能量电子全息重现像加权叠加原理的方法,用于提高低能同轴电子全息的三维数值重现像分辨率,并以三层石墨晶体为样品进行了计算机模拟研究.结果表明,用该方法数值重现时可以有效地消除样品各层电子散射的干扰,并抑制数值重现过程中产生的虚假峰,从而改善了像质并明显地提高了三维重现分辨率.     

俄歇电子全息的计算机模拟研究和数值重现

原子的芯能级电子吸收能量后被激发,经过退激发过程发射出的俄歇电子波在传播过程中被周围的原子散射,带有周围原子结构信息的散射波（物波）与未被散射的电子波（参考滤）相干涉形成的衍射图即俄歇电子全息图,通过全息图的重现,可获得原子级分辨率的三维结构信息。本在合理的物理模型下,以Ｃｕ单晶以高温超志ＹＢＣＯ中的Ｙ原子近邻的一些原子簇为计算实例,首次就受激原子周围单这邻原子散射和多层原子散射产生的俄歇电子全     

压缩超级像素遗传算法重现离轴电子全息图

对重现离轴电子全息图的遗传算法进行了研究,讨论了参考波波矢和超级像素尺寸对重现结果的影响规律,并在此基础上对遗传算法进行了改进.遗传算法利用了全息图的全部信息因而得到较好的重现效果,与传统的滤波重现相比,该方法重现的振幅和相位误差分别下降了69％和64％.讨论结果表明,超级像素中必需包含一到两个周期的参考波以保证较好的重现效果,而参考波的取向对重现结果没有显著的影响；在垂直于参考波波矢方向上压缩超级像素的尺寸,可以进一步提高全息图的重现精度.本文提出了压缩超级像素遗传算法,与常规超级像素遗传算法相比,该方法重现的振幅和相位误差分别减小了15％和7％.     

用空间光调制器实现全息再现像的实时重构

针对目前实现大视角数字全息图实时动态再现的难点,提出了一种基于液晶空间光调制器用分时再现原理实现大视角数字全息图实时动态显示的新方法.分析了大视角全息图的计算原理和利用空间光调制器进行分时再现的技术原理,并提出了空间多屏拼接的方法.利用经改造的液晶背投影光学引擎系统设计了实验系统,对计算得到的全息图阵列进行了相应的处理以符合原投影系统RGB信号的传榆,处理后的全息图阵列由计算机控制以60Hz的频率输出到光学引擎.实验结果表明,可以观察到大视角全息图的动态再现像.     

用空间光调制器实现全息再现像的实时重构

针对目前实现大视角数字全息图实时动态再现的难点,提出了一种基于液晶空间光调制器用分时再现原理实现大视角数字全息图实时动态显示的新方法。分析了大视角全息图的计算原理和利用空间光调制器进行分时再现的技术原理,并提出了空间多屏拼接的方法。利用经改造的液晶背投影光学引擎系统设计了实验系统,对计算得到的全息图阵列进行了相应的处理以符合原投影系统RGB信号的传输,处理后的全息图阵列由计算机控制以60Hz的频率输出到光学引擎。实验结果表明,可以观察到大视角全息图的动态再现像。     

Quantum-implementable selective reconstruction of high-resolution images

This paper, written for interdisciplinary audience, presents computational image reconstruction implementable by quantum optics. The input-triggered selection of a high-resolution image among many stored ones, and its reconstruction if the input is occluded or noisy, has been successfully simulated. The original algorithm, based on the Hopfield associative neural net, was transformed in order to enable its quantum-wave implementation based on holography. The main limitations of the classical Hopfield net are much reduced with the simulated new quantum-optical implementation.     

冷却塔内两相流流态辨识方法研究

提出了一种基于模糊聚类分析和神经网络辨识冷却塔内两相流流态的新方法。借助聚类分析方法所得的结果和获得的隶属度,利用神经网络进行流态辨识。结果表明:这种方法具有较高的精度和运行速度,为实现冷却塔实时控制提供了一种有效的手段。     

喷水室内两相流流态辩识方法研究

提出了一种基于模糊聚类分析和神经网络辨识喷水室内两相流流态的新方法.借助聚类分析方法所得的结果和获得的隶属度,利用神经网络进行流态辨识.结果表明:这种方法具有较高的精度和运行速度,为实现喷水室实时控制提供了一种有效的手段.     

Neural network approach to image reconstruction from projections

In this article, we formulate the image reconstruction problem in terms of a multicriteria optimization-based neural network model, and study its performance. The value of neural network as a computational device is to be shown. We also demonstrate the efficiency of our implementation of neural net processing and compare its performance with respect to the convolution-backprojection reconstruction techniques based on computer-generated noisy projections. The method presented here has the particular ability to solve ill-posed reconstruction problems. The algorithm described was implemented on a serial PC/Intel 586 microcomputer, but was cast in a form which is ideally suited for parallel processing. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 381–387, 1998     

Fast numerical reconstruction technique for high-resolution hybrid holographic microscopy

A numerical reconstruction method believed to be new is proposed for hybrid holographic microscopy in which the hologram of a microscopic object is recorded by an image sensor and is then reconstructed by a computer. Because the Fresnel-Kirchhoff integral must be used for numerical reconstruction to achieve high resolution, we propose an approximation technique for reducing the calculation time. This approximation technique is suitable for microscopic application. The numerical reconstruction of 1-mum-sized objects was demonstrated with a He-Ne laser (lambda = 0.6328 mum).     

Reconstruction of scalar time-delay system models

A new method is proposed for the reconstruction of scalar time-delay system models. The method is simple, fast, and applicable to the analysis of noisy data. The performance of the proposed method is illustrated by application to the time series obtained in a numerical experiment (with a possible noise component) and in a real radiophysical system.     

Neural-network-based method of calibration and measurandreconstruction for a high-pressure measuring system

The problem of applying the neural networks for static calibration of measuring systems and for measurand reconstruction is addressed. A multilayered neural network based method for the static calibration of this system is proposed. The functioning of the calibrated measuring system is based on three fiber-optic transducers whose static characteristics are nonmonotonic and significantly influenced by temperature. The applicability of the proposed calibration method is demonstrated in the case under consideration using synthetic and real data. The neural network is designed and implemented in a general purpose microcontroller. In comparison with the spline-based method of calibration, for the same reference data, the proposed method allows obtention of a better quality of calibration and, most important, when calibrated, the multilayered neural network does not require the measurement of temperature for pressure reconstruction     

Cone-beam reconstruction by backprojection and filtering

A new analytical method for tomographic image reconstruction from cone-beam projections acquired on the source orbits lying on a cylinder is presented. By application of a weighted cone-beam backprojection, the reconstruction problem is reduced to an image-restoration problem characterized by a shift-variant point-spread function that is given analytically. Assuming that the source is relatively far from the imaged object, a formula for an approximate shift-invariant inverse filter is derived; the filter is presented in the Fourier domain. Results of numerical experiments with circular and helical orbits are considered.