一种双层散射介质中间目标单像素成像系统研究

在对散射介质散射特性进行分析的基础上,提出了一种对散射介质中物体进行单像素成像的方法,基于数字微镜阵列和单点探测器,设计并搭建了相应的单像素成像系统。通过对数字微镜阵列加载一系列具有不同相位的条纹图案,采集对应的光强信息,结合图像恢复算法,实现了对位于散射介质中的物体成像。为了去除噪声,分析了噪声的来源,设计了滤波器对所拍摄的物体图像进行滤波处理,图像质量得到了显著提高。与传统散射介质成像系统相比,该系统结构简单,无需复杂的标定过程,在多个领域具有应用前景。     

定量中子数字成像散射校正的蒙特卡罗模拟

 中子数字成像过程中,散射中子可降低像质致使提取样品的定量信息变得困难。针对该情况,分析了中子微光成像系统图像散射降质的原理,采用点扩展函数的叠加来表征散射中子引起图像降质的过程,借助于蒙特卡罗方法（MC）对点扩展函数进行模拟和建模,将点扩展函数描述为样品厚度以及样品到探测器距离为参量的解析函数,研究了点扩展函数的计算方法。研究结果表明,借助于MC方法构建系统的点扩展函数之解析函数,并利用该函数对中子图像进行散射校正,是一种行之有效的定量中子数字成像散射校正方法。     

基于重投影的多项式拟合校正射束硬化

在X射线工业CT(ICT)中,射束硬化会导致重建的图像出现伪影,甚至产生变形。为了消除这种影响,提出了一种基于重投影的多项式拟合校正射束硬化的方法。该方法对原始CT图像进行阈值分割二值化,将物体目标区域的像素值设为1;重投影此二值图像以获取X射线贯穿物体的长度集合;利用多项式拟合此长度集合与多色投影间的关系来建立射束硬化校正模型,用该模型对多色投影进行校正。与传统的多项式拟合校正方法相比,该方法不需要楔状模体(用于测量不同厚度下的衰减值,以此来建立射束硬化校正模型)。研究表明,该方法能有效地抑制射束硬化的影响。     

广角镜头桶形畸变的二元二次多项式修正法

为了修正广角镜头的桶形畸变,提出了一种基于二元二次多项式的修正方法,阐述了其图像的像素坐标校正和像素灰度恢复方法。该方法首先用模板对广角镜头进行对准相对标定,然后用二元二次多项式拟合畸变图像与非畸变图像像素坐标之间的规律,最后把该方法应用于某广角镜头拍摄的图像,并采用最小二乘法对校正效果进行评价。结果表明:在不考虑视觉系统具体参数的情况下,该方法能够有效地对广角镜头桶形畸变进行修正。     

基于主动光照的深海图像增强算法

为了改善主动光在水下传播过程中由散射与吸收效应导致的深海图像对比度低下以及颜色失真问题,提出一种水下图像增强算法.不同于传统方法利用最亮点的强度值作为背景光,提出基于物体与背景光非相关性的背景光估计方法,有效避免了前景处的亮像素或白色物体像素对背景光的误判,同时确保了去散射的精确性,提高水下图像的对比度;针对人造光源的颜色增益和光路衰减导致的图像色偏等问题,在去散射图像上选取离光源最近的灰色像素,利用其对光源的敏感性,将光照强度分离出来.最终,通过估计并去除光源本身的颜色增益,同时补偿光在传播过程中的损失,实现图像的颜色校正.实验结果表明,所提算法可以有效去除水下图像的散射效应,较好地恢复图像色彩,进而获得较优的增强图像.相比于其他算法,增强后的图像信息熵和水下图像质量评价指标值较高,说明该算法能显著提升水下图像的质量,同时保留图像有用信息.     

X射线能量谱间接测量方法

X光机的X射线能量谱在CT图像硬化校正、散射校正和定量CT成像等技术中扮演着重要角色。提出了一种通过由质量衰减系数已知的材料构成的楔型模体的扫描数据来恢复X射线能量谱的方法。经GEANT4模拟数据及实采数据验证,该方法能够较好地恢复X光机的X射线能量谱。通过仿真实验,分析了在几个常用电压下模体材质和厚度对恢复X射线能量谱的影响。     

基于偏振信息的遥感图像大气散射校正

大气散射严重影响了航空遥感图像的质量。为了提高航空遥感图像的识别能力,提出一种基于偏振信息的航空遥感图像大气散射校正方法。大气散射具有显著的偏振特性,而对于垂直探测的地物辐射信号偏振度非常低,该方法正是利用大气散射偏振特性与地物目标偏振特性的差别,从图像中提取地物目标辐射信息,从而提高遥感图像的质量。通过机载多波段偏振CCD相机获取航空偏振遥感图像数据,并采用一组443 nm波段的航空偏振图像数据进行图像大气散射校正实验,实验结果表明该方法能有效地进行航空遥感图像的大气散射校正,从而提高了航空遥感图像的识别能力。     

偏振X射线荧光分析尾矿薄层样品的散射校正方法研究

了解尾矿浆中的重金属元素含量能为矿物浮选提供决策依据,不仅可以提高矿物的利用率,还可减少环境污染。X射线荧光光谱法是一种常用的重金属元素分析技术,对于地质类样品的分析,康普顿散射内标法是一种常用的定量方法。但对于薄层沉积样品,其康普顿散射峰强度会受到支撑滤膜的散射影响。由于样品紧密附着在支撑滤膜上,难以直接获得来自样品本身的康普顿散射强度,不利于直接应用康普顿散射峰强度进行定量分析。以尾矿薄层样品为分析对象,研究了不同聚丙烯滤膜厚度对康普顿散射峰强度的影响,并对薄层样品的康普顿散射强度进行了校正。实验结果表明,在0.34～3.06 mm厚度范围内,康普顿散射峰强度随聚丙烯滤膜厚度的增加线性增加,通过建立探测器获得的总康普顿散射强度与滤膜厚度的线性关系,计算出样品的净康普顿散射峰强度。为验证该修正方法的可靠性,利用蒙特卡洛方法模拟研究了无滤膜的尾矿样品和带有不同厚度滤膜的尾矿样品,结果显示经滤膜厚度影响修正后的净康普顿散射峰强度与无滤膜样品康普顿散射峰强度基本一致,相对偏差为0.41%。同时通过实验和模拟计算了0.34 mm厚聚丙烯滤膜时修正后的净康普顿散射峰强度占总康普顿散射峰的比例,分别为91.31%和89.91%,二者基本一致。最后,利用了上述基于滤膜厚度康普顿散射影响的校正方法,建立了基于康普顿散射内标法的定量校准曲线,对两种尾矿浆中的Cu,Zn和Pb元素的定量分析结果显示,未经滤膜厚度修正的康普顿内标校正相比校正前,部分元素定量结果与ICP-OES结果相比,其相对偏差反而增加3.18%～9.00%。而经滤膜厚度修正的康普顿内标方法的定量结果与ICP-OES结果的相对误差在1.14%～11.15%之间,相比于校正之前,相对偏差减少了0.30%～8.97%。     

一种目标级的遥感图像变化检测算法

传统的像素级变化检测方法对图像的配准准确度要求较高,因而在实际运用中受到很多限制.在人造目标检测的基础上,提出了一种目标级的基于局部配准误差补偿的变化检测方法.根据遥感图像中人造目标与自然目标的纹理差异,对图像中的人造目标进行检测和分割,再对分割图像采用提出的算法进行变化检测.实验表明,与传统的像素级变化检测方法相比,本算法具有较高的检测准确度,对配准准确度的要求也有所放宽,并且可以简化变化检测前的辐射校正工作和变化检测后的像素分类的工作.     

X射线荧光光谱测定地质岩石样品中的痕量元素铷,锶和锆——二次基体校正法和两步背景校正法的应用

本文提出了一种用X射线荧光光谱测定地质岩石样品中的痕量元素铷,锶和锆的方法。为了能获得基体校正和背景校正的最佳结果,采用了二次基体校正法(用康普顿散射线和理论α系数)和两步背景校正法。实验结果令人满意。     

Image feature based automatic correction of low-frequency spatial intensity variations in MR images

An automatic method of compensating for low-frequency variations in magnetic resonance images is presented. Small variations within a tissue type are modelled and a correction function is generated. The methods is based completely on image features and does not need a phantom or user interaction to generate the compensation function. This image correction simplifies digital image analysis and may enhance clinical evaluation. As a result, the correction technique reduces inhomogeneity and improves contrast. Our results show that the radiofrequency response variation of coils can be reduced. The segmentation process, even with a simple threshold method, produces more reliable results when corrected images are used. The presented method is most useful for images acquired in the sagital and coronal planes with circular local coils, or using surface coils, e.g., spine coils.     

A dual image approach for bias field correction in magnetic resonance imaging

In this paper, we propose a dual image approach to correcting intensity inhomogeneities for MR images acquired using surface coils. Previous methods are usually not satisfactory due to restricted application domains, considerable human interactions, or some undesirable artifacts. The proposed algorithm provides nice correction results for a variety of surface-coil MR images. It is accomplished by using an additional body-coil MR image of a smaller size captured at the same position as that of the surface-coil image to facilitate the estimation of the bias field function. The correction algorithm consists of aligning the surface-coil image with the body-coil image and fitting a spline surface from a sparse set of data points for the associated bias field function. Experiments on some real images show satisfactory correction results by using the proposed algorithm.     

基于灰度和空间联合信息最小化的磁共振图像偏差场纠正

在磁共振图像（MR）的偏差场纠正中,针对灰度信息最小化方法没有考虑空间信息问题,提出联合信息最小化方法,该方法把图像的灰度信息和空间信息结合起来.空间信息采用灰度导数信息.被偏差场破坏的图像灰度及其导数值的联合信息（联合熵）大于对应的没被偏差场破坏的图像联合熵.联合熵是通过计算灰度及其导数值的联合概率分布得到.仿真脑部MR数据和临床脑部MR数据的试验结果都表明,灰度及其二阶导数联合信息最小化方法纠正效果良好,大大减少了脑白质和脑灰质的灰度交叠.     

Imaging with an array of adaptive subapertures

An imaging system composed of an array of adaptive optics subapertures referred to as a conformal imaging system is considered. A conformal image of an object viewed through atmospheric turbulence is obtained using the following sequential steps: adaptive compensation of phase distortions through optimization of image quality metrics at each subaperture, measurements of the phase and intensity distributions corresponding to the compensated subaperture images, digital combining and processing of the obtained data, computation of a conformal image using arbitrary phase shifts between subapertures, and correction of these phase shifts through conformal image quality optimization using the stochastic parallel gradient descent algorithm. Numerical simulation results of a dual-star conformal image through atmospheric turbulence are presented.     

低能X射线工业CT图像杯状伪影校正

为了去除X射线工业CT图像中的杯状伪影,提高CT图像的识别能力和量化分析精度,提出一种基于分度投影和权函数的射束硬化校正方法。首先分析得出杯状伪影主要是由X射线连续谱穿过被测物体过程中出现的射束硬化所导致。然后扫描阶梯模型,采集不同厚度下的投影数据并求出线衰减系数,通过拟合曲线,得到硬化模型函数和权函数校正模型函数,并确定权函数。接着,扫描被测圆柱形工件,采集不同分度下的投影数据。最后,针对每一个分度投影数据,采用权函数与当前分度投影数据乘积的方法进行硬化校正。对含有杯状伪影的实际CT图像进行了校正实验,结果表明,与多项式拟合法相比,该方法校正后的灰度图像没有放大噪声,且信噪比提高3.29%,有效地消除了杯状伪影,同时较好地保留了图像边界细节。     

基于Facet模型的闪光图像边缘检测

针对闪光图像的特点,对经典边缘检测算子进行了分析,发现存在检测出的边缘粗和对噪声的干扰比较敏感的缺点,提出了一种基于Facet模型的爆轰实验图像边缘检测算法。该方法吸取了其它同类方法的优点,引进了正交基简化了运算,用离散正交多项式对图像每个像素的邻域作最佳曲面拟合,在此基础上使用整体梯度算子寻求二阶方向导数的零交叉点,最终提取边缘点。结果表明:该方法检测出的边缘细,并且光滑连续,对图像噪声干扰有较强的抑制能力,对于孤立的边缘能够给出较精确的定位。     

X射线法测量的ICF靶丸参数的图像分析

 在ICF靶丸参数测量中，采用接触X射线显微辐射照相法获得靶核的X射线吸收底片图像，将该底片放置于显微镜下并用CCD获得了数字化图像。基于该数字化图像信息，编写了一套完整的计算机算法来计算靶参数。采用辐向平均法标定出靶中心，采用图像强度函数对半径的二阶微分来确定出靶层分界位置，计算精度约为0.2pixels。     

Subpixel image registration with reduced bias

A backward linear digital image correlation algorithm was introduced to obtain subpixel image registration without noise-induced bias for an image set consisting of a noise-free reference image and a number of noisy current images. Furthermore, a correction procedure using additional reference images (generated by offsetting the original image to displacement increments of either half-pixels or even quarter-pixels) was proposed to reduce subpixel approximation bias in the analysis. Numerical results of six sets of synthetic images showed that the proposed algorithm was effective in improving the accuracy of subpixel image registration.     

Edge linking and image segmentation by combining optical and digital methods

We present a hybrid method for segmentation of intensity images, which combines an optical contouring technique and digital algorithms for linking edge points or image segmentation. In a first stage, the digital image to be processed is displayed in a twisted-nematic liquid-crystal display (LCD), which is placed between a polarizer–analyzer pair at 45 deg (instead of 90 deg as occurs in standard LCDs). It is not difficult to demonstrate that the proposed setup produces a resultant image with very pronounced dark contours at middle intensity. After the optical preprocessing, two different digital algorithms are applied: an edge linking algorithm (modified chain code) and a simple thresholding technique for image segmentation. The proposed procedure works well with monochromatic and color images. The method could be useful as a robust technique for segmentation of large images in real-time, which presents potential applications in medical and biological imaging.     

An energy minimization method for MS lesion segmentation from T1-w and FLAIR images

In this paper, we extend the multiplicative intrinsic component optimization (MICO) algorithm to multichannel MR image segmentation, with focus on segmentation of multiple sclerosis (MS) lesions. The MICO algorithm was originally proposed by Li et al. in Ref. [1] for normal brain tissue segmentation and intensity inhomogeneity correction of a single channel MR image, which exhibits desirable advantages over other methods for MR image segmentation and intensity inhomogeneity correction in terms of segmentation accuracy and robustness. In this paper, we extend the MICO algorithm to multi-channel MR image segmentation and enable the segmentation of MS lesions. We assign different weights for different channels to control the impact of each channel. The weighted channels allow the enhancement of the impact of the FLAIR image on the segmentation of MS lesions by assigning a larger weight to the FLAIR image channel than the other channels. With the inherent mechanism of estimation of the bias field, our method is able to deal with the intensity inhomogeneity in the input multi-channel MR images. In the application of our method, we only use T1-w and FLAIR images as the input two channel MR images. Experimental results show promising result of our method.