透射电镜原子像的数值分析

高分辨电子显微术（HRTEM）已广泛应用于材料结构的测定，且分辨率已达到原子尺度。特别是近年来球差校正电子显微术的发展大大增加了获取各种材料结构原子像的可行性。然而，球差校正的高分辨电子显微像并未像人们期望的那样以信息分辨极限的分辨率直接反映材料结构，它们仍需要配以计算像模拟或图像处理技术才可给出可靠的结构信息。     

基于衍射理论的高斯像面球差的计算与分析

为了利用波动光学的衍射理论分析处理球差,采用先计算像点的光强分布再进一步分析处理球差的方法,对球差进行了理论分析和模拟。从基于点源圆孔衍射的成像原理出发,经过适当推导得到高斯像面上仅有球差像点的光强分布的解析计算式,分析探讨了高斯像面上球差的衍射机理及其计算与校正。结果表明,这种方法能够更为精细地分析处理球差,有助于在光学设计中对球差进行像差平衡与校正、光学加工中对球差的检测与修正以及数码成像时对球差做电子校正等,有利于促进激光技术和微光学以及微光机电系统的发展。     

透射电子显微学的新进展ⅡZ衬度像、亚埃透射电子显微学、像差校正透射电子显微学

本文综述了原子分辨率的原子序数衬度成像与原位电子能量损失谱分析、亚埃透射电子显微学、像差校正透射电子显微学和材料的微观结构表征与原位性能测试的最新发展和应用。在配置球差校正器、单色器和高能量分辨率过滤器的FEGTEM／STEM中,用相位衬度像／Z衬度成像与原位电子能量损失谱分析方法,在亚埃的空间分辨率和亚电子伏特能量分辨率下,可以研究各种材料的原子尺度界面和缺陷的原子和电子结构、价态、成键和成分等。配置球差校正器后,可明显提高透射电镜的点分辨率,把点分辨率延伸到信息分辨率,同时显著减小村度离住。随意改变球差系数Cs和离焦值△f,像差校正透射电子显微镜可提供新的成像模式。把特殊的样品杆插入电镜后,可把扫描隧道显微镜(STM)或原子力显微镜(AFM)功能相结合,开展材料的显微结构表征与原位的性能测试,不仅能得到物质的与显微像、成分、衍射有关的信息,同时还可以测量电学、力学性能,也可以研究在外场(温度、应力、电和磁场)作用下材料微观结构演变及结构与性能间的关系。     

离轴反射光学系统像差特性研究

以轴对称光学系统的初级矢量波像差理论为基础,通过引入孔径缩放因子和孔径偏移矢量,获得了离轴反射光学系统初级像差特性。通过分析可知:离轴反射光学系统的初级像差依然由球差、彗差、像散组成。由于孔径缩放因子存在,离轴反射光学系统的波像差系数均有不同比例的减小,且轴对称光学系统的高级孔径像差会在对应离轴光学系统中引入较低阶孔径像差,例如轴对称光学系统未校正球差,对应的离轴光学系统除过球差外还将引入彗差、像散等。相比于轴对称光学系统的像差,由于孔径偏移矢量的引入,离轴反射光学系统的像差不再关于中心视场旋转对称,有可能在轴外视场产生像差零点。     

高分辨二次电子像中的成份衬度

高分辨二次电子像中的成份衬度徐军陈文雄张会珍（北京大学电镜实验室，北京１００８７１）在传统的扫描电子显微学的概念中，二次电子像中包含的是形貌衬度，而背散射电子像中包含的是成份衬度。但实际上二次电子的产额是和样品的成份有关的，不过样品表面极易玷污，样品...     

Cr2Ta中层错的Z衬度像研究

本文利用高角环形暗场成像技术获得六角Cr2Ta Laves相的重原子分辨的原子序数(Z)衬度像,阐述了在相近分辨率情况下Z衬度成像技术与普通高分辨像相比具有一定优势.从所得到Cr2Ta的层错的Z衬度像,直观地解释了层错的原子排列构型,为理解复杂结构中缺陷的原子构型提供了资料.     

高分辨电子显微像的解卷处理程序:DEC

高分辨电子显微像因离焦量和晶体厚度等因素的影响，未必能正确反映晶体结构。为此，李方华等建立并发展了高分辨像的解卷处理技术，将单张高分辨像恢复为结构像，并提高像的分辨率。DEC是为高分辨像解卷处理编写的配套计算机程序，将此程序应用于Si0.75Ge0.24晶体中缺陷的实验高分辨像，     

高分辨二次电子像中的成分衬度

从理论上阐明了获得高分辨二次电子成分衬度像的原理、条件和方法，并作了细致的实验验证，空间分辨率为３．３ｎｍ（Ｖ０＝５ｋＶ），原子序数分辨率为０．０３Ｚ。实验研究样品为分子束外延ＡｌＧａＡｓ量子阱激光器，其分层结构的最小层宽为７ｎｍ，测得的层宽和透射电镜测量结果相吻合，由于透射电镜制样的复杂和困难，高分辨扫描电镜二次电子成分衬度像观测方法具有重要的理论意义和实用价值     

场发射高分辨电子显微像的复原

本文讨论了弱相位物体近似的实际应用范围，在此基础上对场发射高分辨电子显微像作了解卷处理。     

高分辨衍衬像的成像原理

高分辨衍衬像技术的分辨率比普通衍衬像技术高约几倍到一个数量级,它对材料中的缺陷的研究具有重大意义。本文主要介绍两种重要的高分辨行衬像技术,即弱束暗场像和高阶明场像技术的基本原理和成像条件。     

Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data

It has been shown that employing multiple atlas images improves segmentation accuracy in atlas-based medical image segmentation. Each atlas image is registered to the target image independently and the calculated transformation is applied to the segmentation of the atlas image to obtain a segmented version of the target image. Several independent candidate segmentations result from the process, which must be somehow combined into a single final segmentation. Majority voting is the generally used rule to fuse the segmentations, but more sophisticated methods have also been proposed. In this paper, we show that the use of global weights to ponderate candidate segmentations has a major limitation. As a means to improve segmentation accuracy, we propose the generalized local weighting voting method. Namely, the fusion weights adapt voxel-by-voxel according to a local estimation of segmentation performance. Using digital phantoms and MR images of the human brain, we demonstrate that the performance of each combination technique depends on the gray level contrast characteristics of the segmented region, and that no fusion method yields better results than the others for all the regions. In particular, we show that local combination strategies outperform global methods in segmenting high-contrast structures, while global techniques are less sensitive to noise when contrast between neighboring structures is low. We conclude that, in order to achieve the highest overall segmentation accuracy, the best combination method for each particular structure must be selected.      

A fast and efficient color image enhancement method based on fuzzy-logic and histogram

A new fuzzy logic and histogram based algorithm for enhancing low contrast color images has been proposed here. The method is computationally fast compared to conventional and other advanced enhancement techniques. It is based on two important parameters M and K, where M is the average intensity value of the image, calculated from the histogram and K is the contrast intensification parameter. The given RGB image is converted into HSV color space to preserve the chromatic information contained in the original image. To enhance the image, only the V component is stretched under the control of the parameters M and K. The proposed method has been compared with conventional contrast enhancement techniques as well as with advanced algorithms. All the above techniques were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality, Tenengrad, CII and the computational time. The inter comparison of different techniques was carried out on different low contrast color images. Based on the performance analysis, we advocate that our proposed Fuzzy Logic method is well suited for contrast enhancement of low contrast color images.     

基于HSV空间的红外偏振图像改进融合算法

研究了偏振产生的原理和基于标准HSV颜色空间的偏振融合,并根据红外偏振图像对比度较强、强度较弱的特点改进了HSV融合算法,利用原始偏振图像的灰度均值和极值进行图像强度修正,计算融合权重,进行偏振图像融合,增强红外偏振融合图像的强度和对比度。对室内以及室外人造目标进行实验验证,改进的HSV融合算法较红外原图灰度均值最高提升216%,对比度最高提升1384%;较标准HSV融合图像的灰度均值最高提升1363%,对比度最高提升292%。为使用红外偏振融合图像进行目标识别打下了很好的基础。     

基于Mean Shift和插值图像修复算法的CT图像金属伪影消除方法

近年来CT成像技术在临床医学中广泛应用,但当病人体内含有金属移植物时,由于射线硬化等原因很可能在金属物体周围产生亮暗伪影,降低图像质量,影响诊断的准确性.为了消除CT图像中的金属伪影,本文提出基于Mean Shift和插值图像修复的算法,基本流程为用自适应Mean Shift算法预处理CT图像,平滑噪声和轻度伪影,用简化的Mean Shift算法快速精确分割金属物体,由修复组织信息的插值图像生成先验图像,用先验图像的投影数据替换原投影数据得到校正后的CT图像.经过对比实验,文中算法在去除金属伪影的同时,能够保护原有CT图像的组织结构,取得了更好的处理效果.     

Image restoration in X-ray microscopy: PSF determination andbiological applications

We show that digital image processing methods can be applied to enhance the quality of X-ray microscopic images. One application of X-ray microscopy is imaging of biological specimens in their natural aqueous environment. Since X radiation can introduce structural changes in these objects when observing them at room temperature, it is sometimes necessary to take images with short exposure time. The image quality can thus be reduced due to low signal-to-noise ratio (SNR). Digital image processing methods can be applied to reduce image degradation caused by noise. Another example of a digital image processing method we applied to X-ray microscopy images is contrast enhancement of structures near the resolution limit of the microscope. Structures of 20 nm in size, which show weak contrast in the original image, become more clearly visible after restoration. Since image restoration methods are based on the knowledge of the optical transfer function (OTF) or the point spread function (PSF), a handy method for quick PSF determination is presented. An iterative image-restoration method was applied to pictures obtained with the Gottingen transmission X-ray microscope at BESSY. Results of image quality enhancement by this method are shown for images of polytene chromosomes of Chironomus Thummi larvae, test-structures and in situ hybridization on Xist RNA using biotinilated DNA probe     

Knowledge-based interpretation of MR brain images

The authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5+/-0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.     

基于人眼视觉的红外图像增强算法研究

红外图像具有低对比度、噪声大、动态范围大以及视觉效果差等特点。传统的图像增强算法具有各自的局限性,处理后的视觉效果并不理想。为了改善红外图像的视觉效果,增强图像中目标的识别力,研究了基于人眼视觉特性的Retinex算法,结合红外图像的成像机制,将适用于可见光图像的Retinex增强算法应用于红外图像,取得了良好的视觉效果。在此基础上,对算法进行自适应改进,提出了AMSR算法。采用几种传统的图像增强算法和基于Retinex的算法对具有典型特性的红外图像进行增强处理,并计算客观评价指标。通过对实验结果的主观评价和客观指标分析,验证了AMSR增强算法对红外图像具有适用性和优越性。本文的研究工作对红外目标探测和识别具有重要的意义。     

基于Tetrolet变换的近红外与彩色可见光图像融合算法研究

针对近红外与彩色可见光图像融合后出现的对比度降低、细节丢失、颜色失真等问题,提出一种基于Tetrolet变换和自适应脉冲耦合神经网络PCNN(PCNN-Pulse Coupled Neural Network)的近红外与彩色可见光图像融合的新算法。首先将彩色可见光源图像转换到各个分量相对独立的HSI空间(HSI-Hue Saturation Intensity),将其亮度分量与近红外图像进行Tetrolet分解,对分解后得到的低频系数,提出一种从给定不完备数据集中寻找潜在分布最大似然估计的期望最大算法融合规则;对分解后得到的高频系数,采用一种Sobel算子自动调节阈值的自适应PCNN模型作为融合规则;处理后的高低频图像经Tetrolet逆变换作为融合后的亮度图像,提出一种饱和度分量自适应拉伸方法来解决图像饱和度下降的问题。处理后的各个分量反向映射到RGB空间,完成融合。将本文算法与多种高效融合算法进行对比分析,实验表明,本方法取得的图像,细节清晰,色彩对比度得到提升,在图像饱和度、颜色恢复性能、结构相似性和对比度等客观评价指标上均具有明显的优势。     

一种结合人眼对比度感知特性的彩色图像压缩算法

为了使图像压缩后的效果更加符合人眼感知特性,提出了一种结合人眼对比度敏感视觉特性的图像压缩算法。算法首先结合视觉特性和图像变换域频谱系数特征,提出一种图像的角频率的计算方法,并依据计算的角频率提出一种人眼觉察图像最小误差阈值的计算方法;然后以此阈值作为量化步长,提出一种图像变换域频谱系数的量化方法;最后采用霍夫曼编码算法进行编解码,实现图像的压缩。并对三幅彩色图像进行了仿真实验,结果表明:与JPEG技术相比,三幅彩色图和各分量图的平均压缩比、PSNR和SSIM依次提高了10.4807%、6.9879%和2.6494%。表明提出的结合人眼视觉特性的图像压缩算法是一种较好的、有实用价值的压缩算法。     

数字遥感图像清晰度评价研究

清晰度作为衡量数字图像质量的重要指标,现有的评价方法仍不够成熟。经对当前主要的图像清晰度评价方法分析研究,提出一种基于边缘对比度的遥感图像清晰度评价方法,并进行了实验数据分析。结果表明,该评价方法客观、高效,既能够对不同模糊程度的同一图像内容进行清晰度评价,也能在一定程度上对不同内容的遥感图像进行评价,具有很高的实用性。