基于光强相减与灰度值变换改善全息图再现质量

通过对数字全息图光强信息的分析,介绍了光强相减法的原理,为了进一步改善再现像的质量,提出了将光强相减和灰度值变换相结合提高再现像质量的方法.实验对比了全息图的直接再现像、用光强相减法处理后的再现像、对全息图的灰度值进行变换后用光强相减法处理的再现像.实验表明,文中提出的方法有效地削弱了中央零级像的影响,提高了全息图的再现质量.     

有限大小光栅的衍射分析

用稳相积分法分析了点光源经光栅光阑的衍射,得知加光阑对光栅的衍射虚像点的位置没有影响。将全息图置于有限大小平面透射光栅的衍射区域,并且满足全息图成消色散再现像条件时,白光中所有波长的色光都参与再现,形成白光消色散再现像。在有限大小平面透射光栅的衍射面上白光区域以外的区域将形成色光区域分布,将全息图置于该区域时,全息图再现像将呈现彩色。     

利用扭曲向列型液晶光阀实现对数字全息再现像质的改善

提出一种提高全息图像的清晰度的新方法.即原始图像经过微分运算和计算全息的变换后,得到它的边缘全息图,然后对该边缘全息图的对比度进行调节,再通过液晶光阀的调制作用,得到这幅调整后的全息图的再现像.这样得到的全息图的再现像比原来边缘全息图更为清晰.文中推导了黑色和白色的灰度调节函数,同时借助实验数据利用Matlab推导了光强与灰度的近似公式,并利用这些公式对图形进行了改善,实现了更为清晰的全息再现演示.     

朋友

在几十载的岁月里,我交了好多朋友。每当夜深人静,我经常像过电影一样想起这些老友,一幕一幕地再现这些老友们对我的教益。     

浅谈色彩构成的教学新尝试

一、传统教学阻碍了学生的思维发展 传统的教学是从临摹到写生,用具像写生的方法进行专业的模仿性教学,注重再现客观事物,而忽略了创造性的训练.     

基于计算机技术的激光全息防伪

为了改善计算机技术制作全息图的质量和效率,在研究激光全息照相理论的基础上,结合拉普拉斯滤波技术和计算机模拟再现像技术,提出了一种新的计算机技术来制作全息图案的方法,主要包括抽样、离散傅里叶变换、黄氏编码、绘图以及计算机模拟再现全息图五个步骤,这种方法能使图案的仿制难度加大,增强防伪力度.整个过程采用MATLAB编程实现,程序简单,易处理.并制作了一个全息图,验证了该方法的方便快捷性,且能有效地提高全息图案的像质.     

对小学数学复习课的几点认识

“复习课最难上。”这是许多数学教师经常发出的感叹。复习课既不像新授课那样有“新鲜感”,又不像练习课那样有“成功感”,而是担负着查漏补缺、系统整理以及巩固发展的重任。下面就“小数除法的复习”谈谈自己对复习课的几点认识。一、再现知识,查漏补缺数学的各个单元都有其相应的知识点,随着时间的推移,学生容易遗忘,这时对旧知识必须进行回顾和再现。当然知识的再现既要有重点,也要注重一定的方式,复习的重点     

雨后

《雨后》这幅画显示了列维坦用抒情笔调再现大自然的才华。他把向来质感厚重的油画颜料用得像水彩画般纯净透明,把伏尔加河岸边经过雨水洗礼后的小城描绘得像天堂般圣洁且不失凝重。伏尔加的河面上一片片水洼在闪着白光,一团团雨云像低垂的烟雾,飘向远万。从轮船的烟囱里冒出的蒸气低悬在水面上。河岸边,那潮湿的驳船一片漆黑。     

辽宁庄河市第六高中高一年级考场佳作展评

[考试时间]2006年10月9日[考试题目]人生是一条浪花飞溅的长河,时光的流逝淡化了过去的记忆,但总有一些人和事在你的心灵深处留下不可磨灭的痕迹,就像照相的底片,随时可以再现。     

试析文学翻译中的对立统一原则

翻译不可能像数学中的一加一等于二那样,与原文百分之百等值;文学翻译更不能仅靠直译和意译。在文学翻译中要坚持贯彻对立统一的原则,而且要最大限度地忠实于原著,再现原著。     

基于组稀疏残差去噪的磁共振图像重构

为了克服磁共振图像重构精度低的问题,方便医生诊断与治疗,提出一种将组稀疏残差去噪和近似消息传递相结合的磁共振图像重构算法.在基于迭代软阈值的去噪近似消息传递(D-AMP)重构算法中,滤波的去噪算法将使用基于组稀疏残差约束(GSRC)的图像去噪实现.实验结果表明,基于组稀疏残差去噪的磁共振图像重建算法可有效缓解重建图像局...     

多帧图像超分辨率重建算法

多帧图像超分辨率重建技术就是将一些变形、模糊、降采样的低分辨率图像进行融合,估计出一幅高分辨率图像,其步骤主要分为运动估计、插值处理、图像重建.本文采用Vandewalle配准方法将处理过的低分辨率图像序列映射到一幅高分辨率网格上,然后进行插值,最后结合小波变换和迭代方法进行图像重建,并采用小波阈值去噪方法进行去噪处理.实验结果表明：本算法能较好地提高图像的峰值信噪比,是图像重建的一种有效方法.     

基于非凸低秩优化的压缩感知植株图像重构

目标植株图像压缩重构对于图像的高效传输及存储意义重大,同时为后期植株生长状态检测及病虫害识别奠定了基础。传统图像压缩感知方法大多是针对信号在某个特征空间的稀疏性进行的,并没有考虑信号的局部特征与结构化特性,存在重构效率不高、重构精度较低等问题。针对以上情况,提出一种基于非凸低秩优化的压缩感知植株图像重构算法。首先通过KinectV2.0采集植株图像深度数据并进行预处理,结合K-means与Mean-shift聚类算法提取目标植株有效区域,再考虑图像的非局部自相似性,采用加权[lp]范数最小化算法（WSNM）求解低秩优化问题,较好地保留了图像结构细节,最后采用Dog-leg最小二乘算法取代最快下降法进行迭代优化。试验结果证明,该算法在不同采样率下的植株图像重构质量优于其它同类算法,尤其在低采样率下重构效果更为突出。     

平面型场景图像的层次化三维度量重建

主要对平面型场景图像的三维度量重建方法进行详细的研究阐释，提出了层次化的平面型场景图像三维度量重建方法。该方法不必对摄像机定标，首先根据平行线来计算图像平面的消失线得到仿射重建，然后在仿射平面上利用直线之间的已知相对长度、已知角度和相等未知角来计算圆点得到度量重建。     

A stable image reconstruction algorithm for ECT

INTRODUCTION Electrical capacitance tomography (ECT) is based on measuring the variation in capacitance be- tween pairs of electrodes surrounding a subject, and reconstructing the distribution of dielectric properties in the sensing area using the measured data. For im- age reconstruction with ECT, the linear back-pro- jection (LBP) algorithm is the most popular (Xie et al., 1992). However, it can only produce qualitative im- ages. In many cases, it is necessary to provide quantitativ…     

3D Reconstruction technique for tomographic PIV

Tomographic particle image velocimetry(Tomo-PIV) is a state-of-the-art experimental technique based on a method of optical tomography to achieve the three-dimensional(3D) reconstruction for threedimensional three-component(3D-3C) flow velocity measurements. 3D reconstruction for Tomo-PIV is carried out herein. Meanwhile, a 3D simplified tomographic reconstruction model reduced from a 3D volume light intensity field with 2D projection images into a 2D Tomo-slice plane with 1D projecting lines, i.e., simplifying this 3D reconstruction into a problem of 2D Tomo-slice plane reconstruction, is applied thereafter. Two kinds of the most well-known algebraic reconstruction techniques, algebraic reconstruction technique(ART) and multiple algebraic reconstruction technique(MART), are compared as well. The principles of the two reconstruction algorithms are discussed in detail, which has been performed by a series of simulation images, yielding the corresponding reconstruction images that show different features between the ART and MART algorithm, and then their advantages and disadvantages are discussed. Further discussions are made for the standard particle image reconstruction when the background noise of the pre-initial particle image has been removed. Results show that the particle image reconstruction has been greatly improved. The MART algorithm is much better than the ART. Furthermore, the computational analyses of two parameters(the particle density and the number of cameras), are performed to study their effects on the reconstruction. Lastly, the 3D volume particle field is reconstructed by using the improved algorithm based on the simplified 3D tomographic reconstruction model, which proves that the algorithm simplification is feasible and it can be applied to the reconstruction of 3D volume particle field in a Tomo-PIV system.     

基于全变差的超分辨率图像重建

根据低分辨率图像生成的矩阵模型,应用广义逆矩阵理论,得到高分辨率图像所在的解空间,把最小总体变差作为目标函数,应用最速下降方法求解,得到重建的高分辨率图像。实验结果表明：该方法能够提高重建图像质量,具有较好的鲁棒性。     

基于小波方法的超分辨率图像重建

提出了一种基于小波变换理论的超分辨率重建算法,即利用小波变换得到图像的高频和低频子带,结合非线性外推技术对高频子带进行处理,在增加高频子带信息量的同时进行迭代改进,并采用小波阈值方法进行去噪处理.实验结果表明：该算法能够克服以往插值算法的不足,如高频损失、细节模糊等,能很好地提高图像的峰值信噪比,是图像重建的一种有效方法.     

Sample based 3D face reconstruction from a single frontal image by adaptive locally linear embedding

In this paper, we propose a highly automatic approach for 3D photorealistic face reconstruction from a single frontal image. The key point of our work is the implementation of adaptive manifold learning approach. Beforehand, an active appearance model (AAM) is trained for automatic feature extraction and adaptive locally linear embedding (ALLE) algorithm is utilized to reduce the dimensionality of the 3D database. Then, given an input frontal face image, the corresponding weights between 3D samples and the image are synthesized adaptively according to the AAM selected facial features. Finally, geometry reconstruction is achieved by linear weighted combination of adaptively selected samples. Radial basis function (RBF) is adopted to map facial texture from the frontal image to the reconstructed face geometry. The texture of invisible regions between the face and the ears is interpolated by sampling from the frontal image. This approach has several advantages: (1) Only a single frontal face image is needed for highly automatic face reconstruction; (2) Compared with former works, our reconstruction approach provides higher accuracy; (3) Constraint based RBF texture mapping provides natural appearance for reconstructed face.     

Computed tomography using level set method and algebraic reconstruction technique

In this paper, a novel reconstruction technique based on level set method and algebraic reconstruction technique is proposed for multiphase flow computed tomography (CT) system. The curvature-driven noise reduction method is inserted into the conventional iteration procedure of algebraic reconstruction technique to improve the image quality and convergence speed with limited projection data. By evolving the image as a set of iso-intensity contours after each updation, the sufficient number of iterations for acceptable results is reduced by 80%-90%, while the image quality is enhanced obviously. Quantitative evaluation of image quality is given by using both relative image error and correlation coefficient. The resultant images can be utilized to detect flow regimes for monitoring industrial multiphase flow. Laboratory results demonstrate the feasibility of the proposed method. Phantoms of four typical flow regimes can be reconstructed from few-view projection data efficiently, and the corresponding image errors and correlation coefficients are acceptable for the cases tested in this paper.