光谱重建训练样本选择方法研究

目的研究光谱重建训练样本选择方法及训练样本集样本数量对光谱重建精度的影响。方法以光谱反射率已知的1687个矿物颜料样本为研究对象,搭建真实六通道宽带光谱成像系统并进行标定,利用宽带光谱成像系统对样本进行六通道数字响应值采集并进行校正,通过R-matrix光谱重建方法,对现有以及文中引入和提出的训练样本选择方法的光谱重建精度和计算效率进行比较。结果实验结果表明,分区最大化色域边界描述方法可以较好地应用于光谱成像过程中训练样本选择,其在保证光谱重建精度的同时大大提高了训练样本选择的效率,实验证明文中提出的基于色域最大化思想的训练样本方法,在面向复制的光谱成像流程中也具有一定的实用性。结论研究结果对于光谱成像过程中训练样本选择方法的选择具有一定指导作用。     

基于自适应投影增强的压缩感知CT图像重建

针对投影数据受噪声影响严重、被检测工件体积大获取投影数据不完全等导致重建质量差的问题,提出一种自适应反锐化掩模算法对某发动机模拟件投影数据均匀抽取60个角度投影进行预处理,根据其边缘信息设定阈值进行自适应去噪并对高频信息乘以自适应增益增强,采用TV最小化约束的代数重建(ART)算法重建.将改进算法与经典ART、经典反锐化掩模技术处理投影以及投影未处理的POCS-TVM算法进行对比分析,实验结果表明,该方法能够平滑噪声,提高对比度,进而提升不完全投影重建图像质量.重建同样大小的图像,相对于全角度投影迭代一次时间100 s, 60个角度投影迭代一次18.7 s,时间缩短近6倍,大量节省重建时间.     

基于FCM优化的平面阵列电容成像算法

为提高平面阵列电容成像系统的成像精度,提出了一种基于FCM数据优化的成像算法。根据平面阵列电极电容数据的特点,为减小电容测量误差对介电常数的影响,利用FCM算法对测量电容值的不断收敛以实现数据优化的作用,在减弱噪声的同时提高电容值数据的稳定性。在此基础上,对一种隔热材料胶层进行缺陷检测实验,使重建图像的相关系数得到了提高,减小了图像重建误差。实验结果表明:图像重建结果的优化算法可获得更加稳定、有效的电容数据,胶层缺陷图像重建精度具有较大提升。     

利用正三棱锥前向展开法追踪三维温度场声线路径

声波在温度梯度场中传播路径的弯曲效应是影响声学法温度场重建精度的一个重要因素,使用正三棱锥前向展开方法对三维温度梯度场中声波的真实传播路径进行研究,并在具体的温度场模型中对该声线追踪算法进行了仿真计算和分析,仿真结果表明,该方法能够很好地追踪三维温度场声波的真实传播路径.     

多权值电容归一化方法的研究

基于并联模型且考虑了不同介质满场分布时电容值对测量电容值的影响,提出了一种多权值电容归一化方法,所建模型不仅适用于两相流还适用于两相流以上的多相流。实验结果表明:在包含2种以上介质的多相流图像重建中,使用多权值模型的图像重建精度的均方差为0.0044,明显优于并联模型的0.0322,因此,多权值模型能够提高图像重建算法精度。分析重建图像发现,该模型还能提高物场中心处的灵敏度,使得物场中心处物体的成像误差减小,从而提高重建图像质量。     

强偏折场的光偏折层析重建技术

强偏折场的重建是光学层析技术测量复杂流场领域的难点问题.在研究现有重建算法及其适用性的基础上,结合光线追迹算法提出基于曲线路径反演的正则化修正重建技术.该技术克服了现有重建技术中光路直进的假设,基于几何光学原理设计了光线通过强偏折场的积分路径追踪算法,使用代数重建技术和正则化重建技术相结合的思想,通过偏折角投影数据的迭代修正实现非线性反演.使用该技术对强偏折场进行数值重建,证实了其在有限角投影数据条件下重建大梯度折射率分布的有效性,并显示了其在刻画场关键特性方面相对于现有算法的优势.     

基于lp范数的ECT图像重建算法研究

针对在ECT图像重建过程中,基于lp-范数的非凸压缩感知算法常存在计算量较大以及现有的近端映射算法受一些特定的p值限制而导致成像分辨率较低的问题,利用改进的插值函数替换lp-范数xpp,通过调整参数使得改进的函数无限逼近lp-范数xpp,同时引入阈值表示理论,并在此基础上提出新的自适应阈值迭代算法对新模型进行求解。实验结果表明,改进后的自适应lp-范数重构算法相对于Landwebr算法、迭代重加权最小二乘法具有更强的适应性,更高的图像分辨率,更快的成像速度。     

基于维纳估计的光谱反射率重建优化算法研究

目的研究光谱反射率重建算法,解决各种物体颜色的光谱反射率重建精度问题。方法通过高精度多光谱成像系统获取实验样本的系统响应值,分光光度计获取样本的光谱反射率,采用Wiener估计法、自适应维纳估计法和提出的优化维纳估计法,对待测样本实验数据进行光谱重建,并评价重建结果。结果在3种光谱重建算法仿真实验中,提出算法的均方根误差平均值为0.0355,平均CIE1976色差为1.4349,优于其他2种算法。结论在光谱重建算法的研究中,基于优化的维纳估计算法可以有效提高光谱的重建精度,可应用于实际的多光谱成像复制中。     

基于主动位移成像的图像超分辨率重建

超分辨重建算法是一种将低分辨率图像恢复为高分辨率图像的算法,被广泛用于医学、遥感、军事安防以及人脸识别等领域。在黑夜、远场场景下构建数据集比较困难,基于深度学习的超分辨重建算法应用受到阻碍。而微扫描成像技术扫描模式固定,对器件到位精度要求高。针对这两个问题,我们提出一种基于主动位移成像的图像超分辨率重建算法。具体地,在控制相机随机移动的同时记录采样时刻位移,通过解算、映射选图、精确匹配图像序列并获取多帧图像间的亚像素信息,然后对估计图像进行迭代和更新,最后重建获得高分辨率图像。实验结果表明,本算法在PSNR、SSIM和平均梯度三个指标上都优于最近提出的基于POCS的图像超分辨率重建算法MFPOCS,与基于CNN的方法 ACNet相比具有竞争力。值得提出的是,本算法无需固定的扫描模式,降低了微扫描技术对器件实时到位精度的要求,同时,本算法可以保证重建初始帧的优良选取,有效规避了POCS算法的固有缺点。     

基于原始对偶分裂方法求解一类约束可分离凸优化问题及其应用

本文研究一类具有代表性的约束可分离凸优化模型,其目标函数中的数据误差项满足可微性条件,许多图像恢复和图像重建等问题都可以归结为该模型的求解.为克服现有求解该模型方法的不足,文中首先借助指示函数,将原模型转化为无约束的凸优化模型;然后基于原始对偶分裂方法思想,提出一种新的迭代算法,该算法具有结构简单和参数选取容易的特点,同时证明所提算法的收敛性.最后,为验证算法的有效性,我们将其应用于CT图像重建问题,数值实验结果表明所提出的算法在重建时间和重建图像质量上优于现有的其他算法.     

基于稀疏激光点云数据和单帧图像融合的三维重构算法

由于激光雷达获取的深度数据非常稀疏,为了能够将深度数据与图像数据重构出稠密三维深度图,本文提出了基于稀疏激光点云数据和单帧图像融合的三维重构算法。该方法首先使用点直方图特征有效地选择对应于目标的点数据并消除体素中的非相似点;然后,使用高斯过程回归对局部深度数据建模,并通过插值获得三维深度数据,本文算法获得的三维深度点更接近基准值,并保持了目标的局部形状特征;最后,利用马尔科夫随机场对图像灰度数据和三维插值点进行融合来构建三维深度图。仿真实验结果表明:相比现有基于激光雷达数据和单目图像数据的三维重建算法,本文提出的算法将大大提升算法的鲁棒性与重构的准确度,可辅助用于复杂的城市场景中车辆的无人驾驶。     

光谱反射率重建技术的研究进展

目的综述目前已有光谱反射率重建技术的研究进展,为光谱反射率重建技术的适用范围更广和精度更高提供科学的研究基础。方法介绍目前已有光谱反射率重建技术的相关方法,并探讨相关方法的优缺点和适用的应用场景,提出相关方法的改进以及未来发展的趋势。结果直接重建法的重建精度相对较低,插值重建只适用于小样本的数据集,基于人工智能技术虽然精度高,在实际应用中也受到局限,组合重建法将各种重建方法按照一定的方式组合起来,并加入相关的修正项进行光谱重建,解决了其他3类方法存在的缺陷,保证了光谱反射率重建精度,解决了真正实际应用需要。结论通过组合的形式解决了在重建光谱反射率过程中遇到的噪声、智能化等问题,通过组合的方法可以解决各自单一方法缺陷,进行互补。     

Multicriteria maximum likelihood neural network approach to positron emission tomography

The emerging technology of positron emission image reconstruction is introduced in this paper as a multicriteria optimization problem. We show how selected families of objective functions may be used to reconstruct positron emission images. We develop a novel neural network approach to positron emission imaging problems. We also studied the most frequently used image reconstruction methods, namely, maximum likelihood under the framework of single performance criterion optimization. Finally, we introduced some of the results obtained by various reconstruction algorithms using computer‐generated noisy projection data from a chest phantom and real positron emission tomography (PET) scanner data. Comparison of the reconstructed images indicated that the multicriteria optimization method gave the best in error, smoothness (suppression of noise), gray value resolution, and ghost‐free images. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol 11, 361–364, 2000     

Research on Multi-View Image Reconstruction Technology Based on Auto-Encoding Learning

Traditional three-dimensional (3D) image reconstruction method, which highly dependent on the environment and has poor reconstruction effect, is easy to lead to mismatch and poor real-time performance. The accuracy of feature extraction from multiple images affects the reliability and real-time performance of 3D reconstruction technology. To solve the problem, a multi-view image 3D reconstruction algorithm based on self-encoding convolutional neural network is proposed in this paper. The algorithm first extracts the feature information of multiple two-dimensional (2D) images based on scale and rotation invariance parameters of Scale-invariant feature transform (SIFT) operator. Secondly, self-encoding learning neural network is introduced into the feature refinement process to take full advantage of its feature extraction ability. Then, Fish-Net is used to replace the U-Net structure inside the self-encoding network to improve gradient propagation between U-Net structures, and Generative Adversarial Networks (GAN) loss function is used to replace mean square error (MSE) to better express image features, discarding useless features to obtain effective image features. Finally, an incremental structure from motion (SFM) algorithm is performed to calculate rotation matrix and translation vector of the camera, and the feature points are triangulated to obtain a sparse spatial point cloud, and meshlab software is used to display the results. Simulation experiments show that compared with the traditional method, the image feature extraction method proposed in this paper can significantly improve the rendering effect of 3D point cloud, with an accuracy rate of 92.5% and a reconstruction complete rate of 83.6%.     

Three dimension reconstruction through measure-based image selection

Three dimension (3D) reconstruction is one of the research focus of computer vision and widely applied in various fields. The main steps of 3D reconstruction include image acquisition, feature point extraction and matching, camera calibration and production of dense 3D scene models. Generally, not all the input images are useful for camera calibration because some images contain similar and redundant visual information. These images can even reduce the calibration accuracy. In this paper, we propose an effective image selection method to improve the accuracy of camera calibration. Then a new 3D reconstruction algorithm is proposed by adding the image selection step to 3D reconstruction. The image selection method uses structure-from-motion algorithm to estimate the position and attitude of each camera, first. Then the contributed value to 3D reconstruction of each image is calculated. Finally, images are selected according to the contributed value of each image and their effects on the contributed values of other images. Experimental results show that our image selection algorithm can improve the accuracy of camera calibration and the 3D reconstruction algorithm proposed in this paper can get better dense 3D models than the normal algorithm without image selection.     

阻燃聚酯纤维的现状和发展

从阻燃化方法和阻燃剂的类别等方面综述了阻燃聚酯纤维折现状,通过分析各种阻燃技术的优缺点,研究了阻燃聚酯的发展趋势,指出了磷系阻燃聚酯纤维存在的问题和研究的方向。     

Three-dimensional measurement of a gas flow temperature field using far-infrared band CT techniques

Emission spectral tomography (EST) can be utilized to reconstruct three-dimensional (3D) physical parameter distributions of gas flow fields. Mostly, the radiant energy of the visual and near-infrared bands is received by a video camera in EST, so it is difficult to examine a low/medium-temperature gas flow field by normal EST. However, the far-infrared radiant energy of a low/medium-temperature gas flow field is strong enough to be received by a far-infrared detector. Based on EST, a far-infrared band computed tomography (FICT) approach is proposed that focuses on far-infrared spectral emission and band emission tomography. Both low- and medium-temperature blackbody furnaces were adopted to calibrate the relation between infrared thermal image intensity and radiant exitance. The 3D temperature reconstruction of an alcohol blow lamp was carried out. According to the results of multiple measurements, the relative error of the FICT approach is less than 20%. The experimental results prove the feasibility of the FICT approach.     

基于虚拟仪器的火焰尺寸测量系统设计

针对可燃物燃烧过程中难以实时测量火焰尺寸的实际难题,本文基于虚拟仪器平台开发实现了一种实时测量火焰尺寸的非接触测试系统。该系统采用USB1080P高清摄像头实时采集火焰图像,对火焰图像进行目标区域划定及图像处理、边缘点检测。基于圆点基准图建立像素距离与实际尺寸关系,实时获取火焰的真实尺寸。实验结果表明,该系统具有快速实现对火焰尺寸的测量、图形化显示、数据存储,实时观察火焰尺寸变化及WEB发布等功能。     

Optimum multiscale decomposition in NSCT-based single image super resolution

Goal of image super resolution (SR) is to enhance the size of an image without upsetting the information it carries. The information around the edges may get disturbed due to image SR process. The proposed novel approach conserved the information around edges using Non-sub sample contourlet transform (NSCT)-based learning technique. The NSCT constitutes multiscale decomposition as well as multidirectional decomposition of an image. The main contribution of the paper is to optimally select multiscale decomposition level that offers minimum learning error. The smoothness of soft edges is to be potted by soft edge smoothness prior. The learning process leads to appearance of unwanted outliers which are sustained by using a robust error norm. The cost function consisting of a global constraint term and soft edge smoothness prior is optimised using Iterative Back Projection approach. The proposed method is capable to reconstruct a high-resolution image with minimum edge artefacts.     

基于l_(2,p)-范数的ECT图像重建算法

针对电容层析成像系统图像重建过程中Tiknonov正则化解过度光滑引起的重建图像细节信息丢失问题,引入l_(2,p)(0p≤1)的混合范数作为正则化算法的数据项和正则化项。混合范数l_(2,p)利用了欧氏范数l_2的光滑性和分数范数l_p(0p≤1)的稀疏性,不仅比范数L_(2,1)具有更好的联合稀疏性,对噪声的抗干扰性也更强,进而针对l_(2,p)矩阵范数的非凸、非Lipschitz连续问题提出一种新的电容层析成像图像重建模型。实验结果表明,基于矩阵混合范数l_(2,p)极小化优化模型的正则化算法相比牛顿迭代、奇异值分解、共轭梯度算法具有更强的适应性,更高的图像分辨率及更好的成像质量。