三维锥束OSEM算法中子集序列的研究

有序子集算法的提出提高了迭代算法的图像重建速度,但是三维锥束OSEM算法中的子集个数和子集迭代的顺序都会影响图像重建的收敛速度和质量.文中提出了划分子集序列的方法,该方法通过应用距离加权方案改变子集的迭代顺序,使子集均匀分布,来减小相邻子集的相关性对收敛性的影响,并且采用每次迭代后逐渐减少子集个数,提高图像重建收敛速度...     

基于单张图像的三维人脸重建技术研究

本文研究提出的使用UV空间表示人脸几何信息的方法,能够从单张二维人脸图像中直接回归出人脸的形状,并能够明确形态上每个点的语言信息,大大提高了三维人脸重建的效率及效果。经过在多个测试集上的定性分析与定量分析可知,本研究提出的方法不仅能够实现更高效的人脸几何信息重建,而且结果也更加精确。     

基于梯度保真项的低剂量CT统计迭代重建算法

针对低剂量计算机断层扫描(Computed Tomography,CT)重建图像时容易出现明显条形伪影这一现象,提出一种基于梯度保真项的低剂量CT统计迭代重建算法。该算法克服了原始全变分(Total Variation,TV)模型在抑制条形伪影和噪声的同时引入阶梯效应的缺点,首先把梯度保真约束项和能够区分图像平滑区和细节区的边缘指示函数应用到TV模型中得到基于梯度保真项的自适应全变分模型,然后再把新模型与惩罚加权最小二乘(Penalized Weighted Least Square,PWLS)重建算法相结合,使用交替方向迭代法得到最终的图像。采用Shepp-Logan模型来验证算法的有效性,实验结果表明,该算法不仅可以有效地去除条形伪影,还可以较好地保护图像的边缘和细节信息。     

电阻抗成像的修正阻尼最小二乘重建算法

本文给出了一种新的电阻抗成像的图像重建算法,该算法是对Ｎｅｗｔｏｎ－Ｒａｐｈｓｏｎ类算法的改进。算法根据 目标函数的分析,将以前常用的,对正规方程组的求解。转化为 解一个条件数更好的超定方程组的最小二乘解。理论分析和数值试验都表明,这种新的重建算法较常用的Ｎｅｗｔｏｎ－Ｒａｐｈｓｏｎ类算法,不仅数值稳定性有较大的提高,而且计算量有所减少,同时这种算法也适用于其他最小二乘问题的求解。     

基于OSEM算法的X射线荧光CT重建算法研究

X射线荧光CT作为一种新颖的无损检测手段,能够有效获取样本内部物质的属性和分布,并以图像的形式直观展示。文章以XFCT图像重建为出发点,研究了基于OSEM的XFCT重建算法,讨论了重建质量、重建时间与子集划分个数之间的关系,并进行相关仿真和验证,获得良好的结果。     

基于全变差的加权最小二乘法PET图像重建

传统的加权最小二乘法、惩罚项加权最小二乘法虽然能够重建得到较好质量的图像,但在欠采样的条件下不能很好的拟制噪声.全变差作为正则项已广泛用于图像重建中,利用图像稀疏的先验知识能够在欠采样的条件下很好的重建图像.本文结合加权最小二乘法和全变差的优点,构造了基于全变差正则项的加权最小二乘法目标函数,运用交替求解的方法,将目标函数分解为求解二次优化和全变差正则化的优化问题,并分别用超松弛迭代方法和梯度下降法求解这两个优化问题.采用Zubal模型对该算法与传统算法进行仿真验证比较,并用相关系数、方差、信噪比等参数描述图像重建质量.结果表明在欠采样条件下,该算法能够更好的拟制噪声,重构效果比传统的有明显地提高.     

基于FPGA实现CT图像重建加速的设计

为满足CT图像重建系统的实时处理要求,提出了一种基于FPGA实现CT图像重建的加速方法。首先将滤波反投影算法进行并行性分解,然后采用并行流水线设计思想,给出了算法在FPGA上实现的总体结构,并且分别给出滤波、反投影、循环累加等主要功能单元的电路设计,最后在FPGA上实现了12条并行流水线重建系统。实验结果表明,与基于CPU的图像重建相比较,基于FPGA的重建系统重建出的图像绝对误差小于0.4%,加速比在100倍以上,且重建图像的规模越大,加速效果越明显。     

可编程图形硬件下的三维图像的并行重建算法

本文在研究过程中,从几何层面上研究了FDK算法特性,并对其进行了几何描述,并据此分析了一种运用可编程图形硬件加速图像构建方式,在该方式运用过程中,充分运用了图形处理器的高精度性特征、可编程性特征以及计算特性,该图形重建方式操作较为便捷,能够降低运用成本,与原有的传统算法相比,操作效率更高。     

基于视觉传达技术的三维图像重建系统研究

三维图像重建是当前图像处理领域的研究热点,为了解决传统重建系统存在重建效率、精度均低的问题,以提高三维图像重建效果为目标,设计了基于视觉传达技术的三维图像重建系统。首先分析了三维图像重建的系统功能框架,然后对系统的主要子模块:图像解析模块、图像预处理模块、三维可视化模块进行了详细设计,最后在相同环境与传统三维图像重建系统进行了仿真对比实验。实验结果表明,基于视觉传达技术的三维图像重建精度高,而且提高了三维图像重建效率,三维图像重建整体效果要明显优于传统三维图像重建系统,具有更高的实际应用价值。     

基于双目视觉的三维场景图像表面重建算法

在三维场景图像中,像素色度不均匀会影响图像的视觉表现效果,维持像素色度的均匀分布是提升图像视觉表现效果的有效方法。为满足实际应用需求,大幅提升三维场景图像的视觉表现效果,提出一种基于双目视觉的三维场景图像表面重建算法。首先,确定视觉相机的内参、外参标记结果,通过极线校正双目图像的处理方式,完成基于双目视觉的三维场景图像标定;然后,求解像素代价聚合条件,根据像素节点的三维视差推导图像表面的重建表达式,完成基于双目视觉的三维场景图像表面重建算法的设计。实验结果表明,双目视觉技术作用下,三维场景图像的像素色度呈现出较为均匀的分布状态,符合提升图像视觉表现效果的实际应用需求。     

Accelerated iterative transmission CT reconstruction using an ordered subsets convex algorithm

Iterative maximum likelihood (ML) transmission computed tomography algorithms have distinct advantages over Fourier-based reconstruction, but unfortunately require increased computation time. The convex algorithm [1] is a relatively fast iterative ML algorithm but it is nevertheless too slow for many applications. Therefore, an acceleration of this algorithm by using ordered subsets of projections is proposed [ordered subsets convex algorithm (OSC)]. OSC applies the convex algorithm sequentially to subsets of projections. OSC was compared with the convex algorithm using simulated and physical thorax phantom data. Reconstructions were performed for OSC using eight and 16 subsets (eight and four projections/subset, respectively). Global errors, image noise, contrast recovery, and likelihood increase were calculated. Results show that OSC is faster than the convex algorithm, the amount of acceleration being approximately proportional to the number of subsets in OSC, and it causes only a slight increase of noise and global errors in the reconstructions. Images and image profiles of the reconstructions were in good agreement. In conclusion, OSC and the convex algorithm result in similar image quality but OSC is more than an order of magnitude faster.     

基于六视角自标定算法的三维图像重建

针对其他标定方法在三维图像重建中的缺点,提出自标定算法。首先建立摄像机标定模型,通过透视投影在成像平面上生成对应像点的齐次坐标;接着自标定数学模型利用单应性矩阵,使一张图像与另一张图像上存在对应点;最后在三维重建中为了判断像素点是否落在摄像机定位模块坐标内,采用投影面约束方程。实验仿真从6个角度前、后、左、右、上、下获取空间曲面信息,具有较高的重建精度。     

Accelerated image reconstruction using ordered subsets of projection data

The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks). An iteration of ordered subsets EM is defined as a single pass through all the subsets, in each subset using the current estimate to initialize application of EM with that data subset. This approach is similar in concept to block-Kaczmarz methods introduced by Eggermont et al. (1981) for iterative reconstruction. Simultaneous iterative reconstruction (SIRT) and multiplicative algebraic reconstruction (MART) techniques are well known special cases. Ordered subsets EM (OS-EM) provides a restoration imposing a natural positivity condition and with close links to the EM algorithm. OS-EM is applicable in both single photon (SPECT) and positron emission tomography (PET). In simulation studies in SPECT, the OS-EM algorithm provides an order-of-magnitude acceleration over EM, with restoration quality maintained.     

基于分割先验图像多能CT重建算法

固定电压CT成像系统动态范围有限,照射复杂工件时产生的投影极易出现欠曝光、过曝光现象,从而导致有效投影的缺失。为解决投影不完备问题研究了基于分割先验图像多能CT重建算法。该方法首先采用ART-TV算法对最低能量的投影进行重建,并对重建结果进行分割,截取与高能重建图像相似部分做为先验图像,以先验图像约束的压缩感知算法对邻近高能投影重建,重建结果再次进行分割后截取先验图像,重复上述步骤直至最高能量完成重建。仿真实验表明:和传统的多能重建方法相比,研究的方法在完整重建复杂结构工件基础上有效地减少伪影,提高图像像素的稳定性,在主观视觉感受和客观评价参数均有一定的进步。     

Globally convergent image reconstruction for emission tomography using relaxed ordered subsets algorithms

We present two types of globally convergent relaxed ordered subsets (OS) algorithms for penalized-likelihood image reconstruction in emission tomography: modified block sequential regularized expectation-maximization (BSREM) and relaxed OS separable paraboloidal surrogates (OS-SPS). The global convergence proof of the existing BSREM (De Pierro and Yamagishi, 2001) required a few a posteriori assumptions. By modifying the scaling functions of BSREM, we are able to prove the convergence of the modified BSREM under realistic assumptions. Our modification also makes stepsize selection more convenient. In addition, we introduce relaxation into the OS-SPS algorithm (Erdo?an and Fessler, 1999) that otherwise would converge to a limit cycle. We prove the global convergence of diagonally scaled incremental gradient methods of which the relaxed OS-SPS is a special case; main results of the proofs are from (Nedi? and Bertsekas, 2001) and (Correa and Lemaréchal, 1993). Simulation results showed that both new algorithms achieve global convergence yet retain the fast initial convergence speed of conventional unrelaxed ordered subsets algorithms.     

一种基于加速Dijkstra算法的图像分割技术

利用最短路径搜索算法中的Dijkstra算法进行图像分割。提出一种加速Dijkstra算法减小经典Dijkstra算法的运算量,以加快其运行速度。提出基于加速Dijkstra算法的Live-Wire图像分割方法勾画出一幅图像中感兴趣目标的轮廓并采用边界填充分割该目标。实验结果表明该算法能正确地进行图像分割,抗噪声性能好,与手工分割法相比交互次数较少,与原Live-Wire分割算法相比运行时间较短。     

基于多媒体技术的三维人物图像动态重构

针对基于自标定的三维人物图像动态重构方法不能准确获取人物运动形态的三维位置,重构结果存在较高的偏差的问题,提出基于多媒体技术的三维人物图像动态重构方法,采用单目图像重构算法完成人体三维姿态重构。对多媒体技术中单摄像机拍摄的单目人体运动图像进行重构时,首先基于人体树状模型,采用基于部件检测器的二维肢体检测算法完成人体二维肢体检测;再采用基于关节点图像坐标的三维姿态重构算法,依据人体二维姿态检测结果,通过预测关节点反投影误差的退火粒子滤波算法完成人体三维姿态的跟踪,实现三维人体图像动态重构。实验结果说明,所提方法可准确实现三维人物图像动态重构,具有较高的重构精度和效率。     

A splitting-based iterative algorithm for accelerated statistical X-ray CT reconstruction

Statistical image reconstruction using penalized weighted least-squares (PWLS) criteria can improve image-quality in X-ray computed tomography (CT). However, the huge dynamic range of the statistical weights leads to a highly shift-variant inverse problem making it difficult to precondition and accelerate existing iterative algorithms that attack the statistical model directly. We propose to alleviate the problem by using a variable-splitting scheme that separates the shift-variant and ("nearly") invariant components of the statistical data model and also decouples the regularization term. This leads to an equivalent constrained problem that we tackle using the classical method-of-multipliers framework with alternating minimization. The specific form of our splitting yields an alternating direction method of multipliers (ADMM) algorithm with an inner-step involving a "nearly" shift-invariant linear system that is suitable for FFT-based preconditioning using cone-type filters. The proposed method can efficiently handle a variety of convex regularization criteria including smooth edge-preserving regularizers and nonsmooth sparsity-promoting ones based on the l(1)-norm and total variation. Numerical experiments with synthetic and real in vivo human data illustrate that cone-filter preconditioners accelerate the proposed ADMM resulting in fast convergence of ADMM compared to conventional (nonlinear conjugate gradient, ordered subsets) and state-of-the-art (MFISTA, split-Bregman) algorithms that are applicable for CT.     

旅客人身时频调和型毫米波三维重建

在毫米波人身安检成像中,二维平面承载的有限信息量使得安检系统对隐匿危暴/爆品的检出概率不高,亟待获取人体三维全聚焦重建结果,且以往仅时域或频域三维重建算法均难以实现精度与效率的平衡。为此,基于“维度分解-算法调和”策略提出时频调和型三维重建算法（Time-Frequency Coordination 3D Reconstruction,TFC-3DR）。该算法融合频域算法的效率与时域算法的精度优势,利用距离徙动频域成像算法（Range Migration Algorithm,RMA）的方位平移不变性,在距离-高度截面统一地校正距离徙动,以保证运算效率,而后将时域后向投影（Back Projection,BP）算法应用于距离-角度维截面,通过将脉冲压缩数据与成像空间分辨单元逐点相干积累,以保证成像精度,由此加快运算效率的同时又保证重建精度。由于BP算法的引入,阵列天线角度维扫描轨迹灵活可变,有效降低了人体侧面盲区面积,避免漏检、误检。仿真和实测人体毫米波数据实验,验证了算法的有效性与实用性,同时分析重建效果与运算复杂度,验证了该文所提算法相比三维BP重建算法的优越性。