X射线掠入射显微成像诊断技术研究进展

高精密的X射线成像诊断是深入理解内爆过程，揭示点火尺度下未知物理问题的关键。基于掠入射反射的X射线显微镜，结合亚纳米级的超光滑球面或非球面反射镜，能够实现空间分辨优于5 μm的高分辨成像。介绍了国际惯性约束聚变领域的X射线显微成像技术发展及应用，重点展示了我国在高分辨X射线（KB）显微镜、多通道X射线KB显微镜以及大视场X射线KBA显微镜方向的进展，分析了下一阶段超高分辨X射线显微成像的研究计划。通过不断的技术创新，我国的X射线显微成像诊断能力已经达到国际先进水平。     

同轴X射线相位衬度计算机X射线断层摄影术研究

基于北京同步辐射装置(BSRF)开展了同轴X射线相位衬度计算机X射线断层摄影术(CT)研究.利用北京同步辐射的14 keV单色X射线作为光源,以高分辨能力的X射线胶片作为探测器,分别开展吸收衬度和同轴相位衬度成像的比较研究以及相位衬度计算机X射线断层摄影术研究.相位衬度计算机X射线断层摄影术重建采用Bronnikov提出的算法.结果显示,与传统的吸收衬度图像相比,相位衬度图像具有更好的衬度和更高的空间分辨力;实验获得人工样品和蝗虫的相位衬度计算机X射线断层摄影术重建图像.重建图像中可见样品的一些结构细节.实验结果表明,相位衬度X射线成像更适合于研究弱吸收或吸收差异很小的材料;利用北京同步辐射开展同轴X射线相位衬度计算机X射线断层摄影术研究是可行的.     

GEM探测器与X射线成像实验

使用2个GEM探测器系统,分别用作增益测量和位置分辨测量.用GEM探测器测量铜靶X射线能谱,能量分辨率为21.5%.8keV的X射线入射双层GEM探测器,实验测量x方向位置分辨为64μm,y方向位置分辨为68μm.将科研成果经过精炼、核心提取,为核与粒子物理学科的本科生开设GEM探测器和X射线成像实验,使学生对高能粒子探测技术、数据获取和处理等有整体的理解.     

基于超分辨率重建的图像增强算法研究

 为了提高图像分辨率,从软件的角度出发,对低分辨率图像序列重建高分辨率图像的原理和算法实现开展了研究,提出从多个低分辨率图像序列中获取更高分辨率图像的方法。通过采用基于光流场的金字塔分层结构实现由粗到精的图像配准,获取了亚像素的运动估计。在采用多帧低分辨率图像进行亚像素级配准后,提出采用动态自适应确定正则化参数的方法,构造了简单的正则化代价函数,建立了低分辨率图像与高分辨率图像之间关系的重建模型,仿真实验结果表明该超分辨率重建算法水平和垂直方向上分辨率各增加一倍,与其他算法相比清晰度更高,计算时间不到传统POCS算法的一半。     

基于双螺旋点扩散函数工程的多焦点图像扫描显微

在传统共聚焦显微技术的基础上,图像扫描显微技术使用面阵探测器来代替单点探测器,结合虚拟数字针孔并利用像素重定位和解卷积图像重构算法将传统宽场显微镜的分辨率提高一倍,实现了高信噪比的超分辨共焦成像.但是,由于采用逐点扫描的方式,三维成像速度相对较慢,限制了其在活体样品成像中的应用.为了进一步提高图像扫描显微术的成像速度,本文提出了一种基于双螺旋点扩散函数工程的多焦点图像扫描显微成像方法和系统.在照明光路中,利用高速数字微镜器件产生周期分布的聚焦点阵对样品进行并行激发和快速二维扫描;在探测光路中,利用双螺旋相位片将激发点荧光信号的强度分布转换为双螺旋的形式;最终,利用后期数字重聚焦处理,从单次样品扫描数据中重构出多个样品层的超分辨宽场图像.在此基础上,利用搭建的系统分别对纤维状肌动蛋白和海拉细胞线粒体进行成像实验,证明了该方法的超分辨能力和快速三维成像能力.     

图像复原式整形环形光横向超分辨共焦显微测量新方法

提出一种新的图像复原式整形环形光横向超分辨共焦显微测量法. 该方法首先利用二元光学器件，将高斯照明光束整形为环形光束，用于初步改善共焦显微镜的横向分辨力，然后利用基于最大似然估计法(maximum likelihood estimate, MLE)的单幅图像超分辨复原技术，重建测量图像的高频信息，来进一步改善共焦显微镜的横向分辨力. 实验表明,当λ=632.8nm，N.A. =0.85时，该方法能使共焦显微镜获得优于0.1μm的横向分辨力. 利用该方法建立的横向超分辨共焦显微系统除了具有显著的超分辨效果外 关键词： 超分辨 超分辨复原 最大似然估计 共焦成像     

单色化X射线显微成像实验研究

围绕激光惯性约束聚变(ICF)内爆压缩阶段高空间分辨、高能谱分辨的诊断需求,提出了一种将KB显微镜和衍射晶体组合的大视场、单色化成像系统。在实验室条件下,利用Fe靶X射线光管,采用KB显微镜结合高定向热解石墨(HOPG)对网格进行背光成像,晶体选能后的成像结果表明,系统的视场能达到800μm,其中高分辨区域成像的分辨率为37μm。采用能谱探测器测试成像能谱,结果表明,系统的能量分辨率为28,验证了系统的单色性能。该系统兼顾了大视场、空间分辨和能量分辨,对内爆压缩阶段实验中热斑结构及混合效应的研究具有重要应用。     

基于读出条读出的二维位置灵敏气体电子倍增器的研制

为研发能够满足北京同步辐射(BSRF)实验要求的二维探测器,研制了基于读出条读出的二维位置灵敏X射线气体电子倍增器(GEM).实验过程中通过改进读出条的周期,并基于中科院高能所实验物理中心电子学组研制的多路电荷放大器系统与VME存储-ADC系统,对软X射线源的位置分辨率进行了精密测量且得到了优异的二维位置分辨:X方向约为84μm,Y方向约为75μm.研究了三级GEM压差之和对位置分辨的影响,并测试了探测器的位置准确度与线性.对基于阳极读出的GEM用于模拟读出测试系统得到了一定的理论认识,侧重研究了GEM探测器基于模拟读出的系统特性,给出了相关的分析与讨论.     

稀疏阈值的超分辨率图像重建

为了解决基于字典学习的超分辨重构算法耗时过长的问题,提出了基于稀疏阈值模型的图像超分辨率重建方法。首先,将联合字典理论与图像块稀疏阈值方法相结合,训练得到高、低分辨率过完备图像字典对。接着,通过稀疏阈值OMP算法对图像特征块进行稀疏表示。然后,通过高分辨率字典重构出初始的超分辨图像。最后,通过改进迭代反投影算法对初始的超分辨图像进行全局优化,从而进一步提高图像重构质量。实验结果表明,超分辨图像重构平均峰值信噪比(PSNR)为30.1 d B,平均结构自相似度(SSIM)为0.937 9,平均计算时间为10.2 s。有效提高了超分辨重构的速度,改善了重构高分辨图像的质量。     

基于双阈值Huber范数估计的图像正则化超分辨率算法

多帧图像超分辨率算法利用图像间的互补信息, 可以从一系列具有亚像素位移的低分辨率影像数据中重建出高分辨率图像. 在众多超分辨率算法中, 正则化方法以其求解病态问题的有效性而被广泛应用, 但在此类方法中, 最优估计算子的估计准确度对最后的重建结果有着较大的影响. 本文在现有正则化超分辨率重建算法的基础上, 提出了一种基于双阈值Huber范数的极大似然估计算子, 可以提高Huber范数对于阈值取值的容忍性和算子估计精度; 并给出了基于该算子的正则化超分辨率算法的迭代公式. 通过对仿真图像进行重建, 结果表明算法可有效地抑制各种噪声并保证重建效果; 同时将此算法应用于实际图像的超分辨率重建, 有效地提高了目标影像的空间分辨率. 关键词： 图像处理 图像重建 超分辨率 正则化     

基于多次成像的分辨率提高算法研究

在数字成像快速发展的今天,对CCD的分辨约有着越来越高的要求,文中提出了一种新的提高CCD成像分辨率的算法,利用多幅有相对位移的低分辨率图像,通过一定的计算,合成后得到分辨率较高的图像.由于算法与硬件无关,即独立于设备,所以无论CCD硬件发展到如何先进的水平,该算法都有其应用价值的.     

Super-resolution image reconstruction based on Tukey data fusion and bilateral-total-variation regularization

Images of high-resolution are desired and often required in most photoelectronic imaging applications, and corresponding image reconstruction algorithm has became the frontier topics. On the basis of stochastic theory, a novel super-resolution image reconstruction algorithm based on Tukey norm data fusion and bilateral total variation regularization is proposed in this paper. The Tukey norm is employed for fusing the data of low-resolution frames and removing outliers in the data, and then aiming at the sickness of super-resolution reconstruction, the bilateral total variation regularization as a priori knowledge about the solution is incorporated to remove the artifacts from the final answer and improve the convergence rate. Simulated and real experiment results show that the proposed algorithm can improve the image resolution greatly and it is immune to noise and errors in motion and blur estimation.     

Infrared super-resolution imaging method based on retina micro-motion

With the wide application of infrared focal plane arrays (IRFPA), military, aerospace, public security and other applications have higher and higher requirements on the spatial resolution of infrared images. However, traditional super-resolution imaging methods have increasingly unable to meet this requirement in technology. In this paper, we adopt the achievement that the human retina micro-motion is the important reason why the human has the hyperacuity ability. Based on the achievement, we bring forward an infrared super-resolution imaging method based on retina micro-motion. In the method, we use the piezoelectric ceramic equipment to control the infrared detector moving variably within a plane parallel to the focal plane. The motion direction is toward each other into a direction of 90°. In the four directions of the movement, we get four sub-images and generate a high spatial resolution infrared image by image interpolation method. In the process of the shifting movement of the detector, we set the threshold of the detector response and record the response time difference when adjacent pixel responses are up to the threshold. By the method, we get the object’s edges, enhance them in the high resolution infrared image and get the super-resolution infrared image. The experimental results show that our proposed super-resolution imaging methods can improve the spatial resolution of the infrared image effectively. The method will offer a new idea for the super-resolution reconstruction of infrared images.     

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.     

基于流形学习的人脸图像超分辨率技术研究

超分辨率技术是由低分辨率图像复原出高分辨率图像的技术。针对人脸图像进行基于学习的超分辨率技术研究,将流形学习算法融入到超分辨率算法中,并且将其用于人脸图像的超分辨率复原。对流形学习应用于基于学习的超分辨率原理进行了介绍。为了使得人脸图像取得更好的复原效果,对特征提取模板进行改进,使得新的特征提取模板考虑更多的像素之间的相关性,并更好地抑制噪声的影响,保留了更多的特征信息。加入了新的特征(即拉普拉斯特征)。该特征突出的边缘细节,保持了人脸图像鲜明的轮廓和清晰的边缘信息。实验结果表明,算法复原出的人脸图像更接近于真实图像,具有更高的峰值信噪比。     

Manifold-preserving single-image super-resolution based on collaborative representation support

Nowadays, super-resolution is becoming more and more important in most optical imaging systems and image processing applications due to current resolution limit of charged couple device (CCD) and complementary metal-oxide semiconductor (CMOS). In this paper, we proposed a novel single-image super-resolution algorithm, which combines collaborative representation into manifold-preserving approach. The main contributions of our work can be summarized into two points. First, supporting bases which are used to calculate the mapping relationship between low-resolution (LR) images and high-resolution (HR) images are obtained by applying collaborative representation on the neighborhoods of the dictionary atoms, where the neighborhoods are clustered from the whole training sample pools. Second, to achieve a balance between execution speed and reconstruction quality, a global solution for our framework is constructed by transferring the online calculating process to offline. We demonstrate better results on commonly used datasets, showing both better visual performance and higher index values compared to other methods.     

基于局部约束群稀疏的红外图像超分辨率重建

针对红外图像分辨率低、视觉质量差等问题,提出基于局部约束群稀疏模型的红外图像超分辨率重建方法.考虑到红外图像的纹理自相似性和原子系数的群结构稀疏性,首先建立了基于局部约束的群稀疏表示模型.然后,在假定低分辨率图像空间和高分辨率图像空间具有相似流形的前提下,联合局部约束群稀疏表示模型和K-SVD(K奇异值分解)方法,训练得到高低分辨率图像对应的群结构字典对.最后,通过高分辨字典和对应的红外图像群稀疏表示系数重建得到高分辨率的红外图像.实验结果表明,本文方法具有更好的超分辨率效果,无论是在客观评价指标还是主观视觉效果方面都有明显的提高.     

基于压缩感知超分辨鬼成像

分辨率是成像系统的一个重要参数, 获得高分辨率图像一直是鬼成像系统的一个目标. 本文提出了以成像系统点扩散函数作为先验知识, 基于稀疏测量的超分辨压缩感知鬼成像重建模型. 搭建了一套计算鬼成像实验装置, 用于验证该模型对于提高鬼成像系统分辨率的有效性, 并与传统的鬼成像计算模型进行了对比. 实验表明, 利用该模型可突破成像系统衍射极限分辨率的限制, 得到超分辨鬼成像. 关键词： 鬼成像 压缩感知 超分辨 稀疏测量     

Texton-based super-resolution for achieving high spatiotemporal resolution in hybrid camera system

Many super-resolution methods have been proposed to enhance the spatial resolution of images by using iteration and multiple input images. In a previous paper, we proposed the example-based super-resolution method to enhance an image through pixel-based texton substitution to reduce the computational cost. In this method, however, we only considered the enhancement of a texture image. In this study, we modified this texton substitution method for a hybrid camera to reduce the required bandwidth of a high-resolution video camera. We applied our algorithm to pairs of high- and low-spatiotemporal-resolution videos, which were synthesized to simulate a hybrid camera. The result showed that the fine detail of the low-resolution video can be reproduced compared with bicubic interpolation and the required bandwidth could be reduced to about 1/5 in a video camera. It was also shown that the peak signal-to-noise ratios (PSNRs) of the images improved by about 6 dB in a trained frame and by 1.0–1.5 dB in a test frame, as determined by comparison with the processed image using bicubic interpolation, and the average PSNRs were higher than those obtained by the well-known Freeman’s patch-based super-resolution method. Compared with that of the Freeman’s patch-based super-resolution method, the computational time of our method was reduced to almost 1/10.