光学低通滤波器的光学传递函数分析

光电成像系统的抽样过程会引起图像频谱在频谱空间上的多次复制,如果抽样过程不满足系统的奈奎斯特条件,相邻的频谱项就会产生混叠.光学传递函数可以反映系统中光学成像,抽样和重建等各个过程对图像信号频谱的响应.通过对使用光学低通滤波器(OLPF)光电成像系统的光学传递函数的分析,表明光学低通滤波器可以有效地限制图像的频谱宽度,使抽样满足奈奎斯特条件,达到消除频谱混叠,改善重建图像质量的目的.     

光电成像系统中的光学传递函数

本文研究了光电成像系统中的光学成像、抽样和重建等各个过程对整个系统的光学传递函数的影响。分析了光电成像系统在积分抽样和图像运动情况下的光学传递函数，重建图像中的频谱混叠和假响应情况，由此给出光电成像系统获取图像的一些原则。     

CCD抽样对傅里叶变换轮廓术的影响

从傅里叶变换轮廓术(FTP)原理入手,分析了傅里叶变换过程中频谱的产生,给出了CCD像元信号强度及其经抽样后的变形条纹的表达式,得出了变形条纹的傅里叶频谱式,其频谱是原连续函数的频谱在频域内的无限重复,即"频谱岛"。频谱中除了基频外,还产生了二级、三级等的高级频谱。给出了抽样条件及满足抽样条件的方法:当抽样频率与光栅基频的比值m>4时可以恢复物体的面形,反之不能;减小抽样间隔可以使m>4。给出了实验结果:当m=2.0883时,物体面形没有恢复;当m=16.6667时,物体面形得到了恢复。实验结果证明了理论分析的正确性。     

光电探测器对光谱仪器精度的影响

通过优化光谱探测环节来提高光谱仪器的精度是改进或研制新型光谱仪的重要途径。为此,文章基于对光电成像系统中光学传递函数的研究,建立了光谱图像经探测器积分抽样后重建的数学模型,并在此基础上分析了光电探测器积分抽样特性参数对光谱线频谱的影响,讨论了光谱线半宽度与探测器积分区间宽度、灵敏度及抽样间隔的关系,提出了准确重建光谱线,提高光谱仪器波长精度和光度精度的探测器优化原则。     

高阶光学强度相干成像的相位恢复方法改进

为了提高强度相干方法对空间目标的成像质量,避免复杂的相位估计过程及其引入的额外误差,对高阶强度相干的相位恢复方法进行了改进.在对高阶强度相干相位测量信噪比定量讨论的基础上,选择三阶强度相干方法对远场目标测量过程进行仿真,针对相位解算过程中频谱模值传递噪声放大等问题,根据相位估计与相位测量结合的思路,提出了相位闭合正则项改进算法.通过室内强度相干成像实验,比较改进算法与最速下降法、先验信息迭代方法对相同测量结果的图像恢复效果.实验结果表明,改进算法得到的图像误差值低于原有方法,较为有效地提高了成像质量.     

CT扫描系统中成像系统响应的不一致性对重建图像质量的影响

在 CT扫描系统中 ,成像系统响应的不一致性将导致所获取的投影中存在趋势项 ,这将引起 CT重建图像质量的下降。分析了成像系统响应不一致性产生的原因及其对 CT重建图像的影响 ,提出了不一致性校正方法 ,并通过实验进行了验证。     

光电成像系统混淆效应的定量分析

光电成像系统由于抽样产生频谱混淆。用矢量分析方法,给出了正弦图像基元的混淆表达式。具体分析了以短形光栅为例的周期目标莫尔条纹式的混淆效应,以及非周期性目标噪 声形式的混淆效应。并分别作了计算机仿真实验。     

通道调制型偏振成像系统的偏振参量重建

通道调制型偏振成像系统中,焦平面上获取的信息需要通过目标偏振参量的重建才能有效提取,因而重建是目标识别、材料分析、生物医疗等技术进一步应用的前提.为了实现在非理想情况下通道调制型偏振成像系统的偏振参量精确重建,需要解决成像系统中电荷耦合器件(CCD)采样频率与频谱位置偏移对重建的影响.本文首先详细分析了频谱不发生混叠的条件:CCD采样频率应至少为4倍基频;在偏振干涉频谱位置偏移时,使用最大频谱法确定各个偏振态的载波频率,通过频移、滤波和傅里叶变换获得目标的偏振重建二维图像;最后通过计算机模拟仿真与实验分析结合的方法验证该重建方案的可行性与有效性.模拟与实验结果表明:改进后的偏振重建法得到的偏振图像与原始输入图像的均方差在0.001以下,峰值信噪比有明显的提高,且结构相似度可达到0.9以上,表明该方法获得的二维偏振态重建图像精度高,与理论偏振解调法相比具有很大的优越性.该工作希望为后续偏振探测与分析进一步的研究提供参考.     

超声换能器带宽对光声成像的影响

研究了不同尺寸吸收体产生的光声压的频谱特性：对于厘米量级、毫米量级和几百个微米量级的吸收体,产生光声压频谱的主要范围分别约为20～300kHz、70kHz～2．5MHz和400kHz～20MHz;讨论了不同频率范围的光声信号对重建图像的影响,低频段的光声信号能反映物体的非边界区域,而高频段的光声信号能突出物体的细微结构,尤其是物体的边界特征。提出了不同尺寸的吸收体要选用或设计不同带宽范围的探测器进行检测的方法．当探测器的带宽范围与光声压频谱范围基本吻合时,损失的频率成份较少,重建的光声图像效果较好,这一结论在仿真和实验结果中都得到了证明。实验用的光源为YAG激光器,波长为532nm,重复频率为30Hz,脉宽为7ns,探测器为针状的PVDF膜水听器,接收面积的直径为1mm。     

基于双随机相位编码的彩色图像加密技术

为了实现仅用两个密钥对彩色图像进行加密,提出了一种基于光栅调制的彩色图像加密方法.该方法首先把彩色图像分成三基色分量:红,绿,兰.然后,把这三帧灰度图像分别用空间频率不同正弦振幅光栅调制,之后,再把调制结果进行叠加而形成一个实值目标图像,该目标图像包含了原始彩色图像的全部信息.对此目标图像进行双随机相位加密系统的加密,即实现了彩色图像的加密隐藏.由于正弦光栅的调制作用,R、G、B灰度图像的频谱在实值目标图像的频谱中分离开来,通过选取合适的滤波窗口,就可以对他们的频谱分别提取并予以重建,并最终实现重构原始彩色图像.本文给出了理论分析和计算机模拟,实验结果证实了该方法的可行性.     

像面数字全息的重建相位误差分析和改善

像面数字全息是数字全息技术中常用的测量和成像方式,它通常采用离散傅里叶变换和频率滤波的方法进行物光波的重建.本文讨论了这些算法对重建相位的影响.首先分析了频谱泄露对于相位误差的影响,结果表明当采样周期为整数时,重建相位误差很小,因此具有极高的相位重建精度;而当不满足整周期采样时,相位重建误差有了明显的增加.为了改善频谱泄露所引起的相位误差,采用Hanning函数对数字全息图进行了预处理,结果表明Hanning窗的加入能够有效地提高重建相位的准确程度.     

High resolution dynamic cardiac MRI using partial separability of spatiotemporal signals with a novel sampling scheme

The partial separability (PS) of spatiotemporal signals has been exploited to accelerate dynamic cardiac MRI by sampling two datasets (training and imaging datasets) without breath-holding or ECG triggering. According to the theory of partially separable functions, the wider the range of spatial frequency components covered by the training dataset, the more accurate the temporal constraint imposed by the PS model. Therefore, it is necessary to develop a new sampling scheme for the PS model in order to cover a wider range of spatial frequency components. In this paper, we propose the use of radial sampling trajectories for collecting the training dataset and Cartesian sampling trajectories for collecting the imaging dataset. In vivo high resolution cardiac MRI experiments demonstrate that the proposed data sampling scheme can significantly improve the image quality. The image quality using the PS model with the proposed sampling scheme is comparable to that of a commercial method using retrospective cardiac gating and breath-holding. The success of this study demonstrates great potential for high-quality, high resolution dynamic cardiac MRI without ECG gating or breath-holding through use of the PS model and the novel data sampling scheme.     

Image registration guided,sparsity constrained reconstructions for dynamic MRI

It is generally a challenging task to reconstruct dynamic magnetic resonance (MR) images with high spatial and high temporal resolutions, especially with highly incomplete k-space sampling. In this work, a novel method that combines a non-rigid image registration technique with sparsity-constrained image reconstruction is introduced. Employing a multi-resolution free-form deformation technique with B-spline interpolations, the non-rigid image registration accurately models the complex deformations of the physiological dynamics, and provides artifact-suppressed high spatial-resolution predictions. Based on these prediction images, the sparsity-constrained data fidelity-enforced image reconstruction further improves the reconstruction accuracy. When compared with the k-t FOCUSS with motion estimation/motion compensation (MEMC) technique on volunteer scans, the proposed method consistently outperforms in both the spatial and the temporal accuracy with variously accelerated k-space sampling. High fidelity reconstructions for dynamic systolic phases with reduction factor of 10 and cardiac perfusion series with reduction factor of 3 are presented.     

Incorporating prior knowledge via volumetric deep residual network to optimize the reconstruction of sparsely sampled MRI

For sparse sampling that accelerates magnetic resonance (MR) image acquisition, non-linear reconstruction algorithms have been developed, which incorporated patient specific a prior information. More generic a prior information could be acquired via deep learning and utilized for image reconstruction. In this study, we developed a volumetric hierarchical deep residual convolutional neural network, referred to as T-Net, to provide a data-driven end-to-end mapping from sparsely sampled MR images to fully sampled MR images, where cartilage MR images were acquired using an Ultra-short TE sequence and retrospectively undersampled using pseudo-random Cartesian and radial acquisition schemes. The network had a hierarchical architecture that promoted the sparsity of feature maps and increased the receptive field, which were valuable for signal synthesis and artifact suppression. Relatively dense local connections and global shortcuts were established to facilitate residual learning and compensate for details lost in hierarchical processing. Additionally, volumetric processing was adopted to fully exploit spatial continuity in three-dimensional space. Data consistency was further enforced. The network was trained with 336 three-dimensional images (each consisting of 32 slices) and tested by 24 images. The incorporation of a priori information acquired via deep learning facilitated high acceleration factors (as high as 8) while maintaining high image fidelity (quantitatively evaluated using the structural similarity index measurement). The proposed T-Net had an improved performance as compared to several state-of-the-art networks.     

采样间隔和投影数对SR-XFMT重构质量的影响

同步辐射微束X射线荧光CT(SR-XFMT)是一种能无损、高空间分辨和高灵敏地探测样品内部元素含量和分布的新技术. 对SR-XFMT实验进行了计算机模拟, 讨论了采样间隔和投影数对重构质量的影响, 分析发现采样间隔对重构质量的影响比投影数的影响要大.     

Infrared super-resolution imaging based on compressed sensing

The theoretical basis of traditional infrared super-resolution imaging method is Nyquist sampling theorem. The reconstruction premise is that the relative positions of the infrared objects in the low-resolution image sequences should keep fixed and the image restoration means is the inverse operation of ill-posed issues without fixed rules. The super-resolution reconstruction ability of the infrared image, algorithm’s application area and stability of reconstruction algorithm are limited. To this end, we proposed super-resolution reconstruction method based on compressed sensing in this paper. In the method, we selected Toeplitz matrix as the measurement matrix and realized it by phase mask method. We researched complementary matching pursuit algorithm and selected it as the recovery algorithm. In order to adapt to the moving target and decrease imaging time, we take use of area infrared focal plane array to acquire multiple measurements at one time. Theoretically, the method breaks though Nyquist sampling theorem and can greatly improve the spatial resolution of the infrared image. The last image contrast and experiment data indicate that our method is effective in improving resolution of infrared images and is superior than some traditional super-resolution imaging method. The compressed sensing super-resolution method is expected to have a wide application prospect.     

数字全息周期像的产生机理及在抑制零级衍射上的应用

在数字全息成像中, 利用CCD的RGB模式采样全息图时, 全息重构像会出现特定的周期性分布. 本文从理论和实验上详细研究了这种周期像产生的机理、分布特性和应用. 研究结果显示, 由于CCD的光谱滤镜会使全息图的RGB三个单色采样阵列出现部分像素信号的缺失, 因此, 需要通过特定的demosaicing数学算法对缺失的像素信号进行重建以形成完整的单色采样阵列, 这是数字全息再现像周期分布产生的根源. 而基于demosaicing算法的采样阵列重建会在全息图频谱中引入调制函数, 导致物体再现像和零级衍射斑的周期分布差异. 本文揭示了全息图的RGB采样、demosaicing算法与全息重构像周期性之间的内在关联. 最后, 讨论了结合空间移位和图像形态学技术, 利用重构像的周期性抑制零级衍射斑的应用. 所有理论与实验研究结果完全一致. 关键词： 数字全息 图像周期性 零级斑抑制     

A fast implementation for computing spatial impulse response of ultrasonic transducers

The spatial impulse response(SIR) method is often used as the 'gold standard5 in simulation of transient acoustic wave fields due to its high accuracy in the linear domain.However, a high sampling frequency is often required in order to achieve the high accuracy. As a result, a large amount of data has to be processed. In this paper a fast approach for computing spatial impulse response is proposed to reduce the computation burden. The proposed approach is developed by employing the relationship of SIRs at observed points and SIRs of the projection points on the transducer surface. Two critical parameters used in the proposed approach, the calculation sampling frequency and the interpolation sampling frequency, are then analyzed.Results show that for a 2.25 MHz rectangular transducer with the size of 5 mmxlO mm,a calculation sampling frequency of 1000 MHz and an interpolation sampling frequency of500 MHz can achieve superior performance while improving the computation efficiency 18 times than the direct solving.     

数字全息显微系统结构参量对再现像质的影响

为了获取高质量的数字全息显微再现像,分析了同轴相移数字全息显微系统结构参量对再现像质的影响.首先经过计算得出理想成像时,像空间物光波频率完全由物体面形结构频率和系统放大倍率决定.然后基于显微成像时物光波的所有频率分量都应该被有效记录的分析,得出在同轴相移系统中放大倍率必须使物光波的空间频率缩小到满足采样要求|并得出在相同的放大倍率下记录距离会影响有效记录的物体尺寸,记录器件离开像平面的距离越小,有效记录的物体尺寸越大,在像平面上时有效记录的物体尺寸最大.计算机仿真和光学实验都证明了上述结论的正确性.     

Magnetic resonance image enhancement using highly sparse input

Lately, the Magnetic Resonance scans have struggled with its own inherent limitations, such as spatial resolution as well as long examination times. In this paper, a novel, rapid compressively-sensed magnetic resonance high resolution image resolution algorithm is presented. This technique addresses these two key issues by employing a highly-sparse sampling scheme and super-resolution reconstruction (SRR) method. Due to highly challenging requirements for the accuracy of diagnostic images registration, the presented technique exploits image priors, deblurring, parallel imaging, and a discrete dense displacement sampling for the deformable human body and motion analysis. The clinical trials as well as phantom based studied have been conducted. It has been proven that the proposed algorithm is able to enhance image spatial resolution, reduce motion artefacts and scan times.