X射线光场成像技术研究

X射线三维成像技术是目前国内外X射线成像研究领域的一个研究热点.但针对一些特殊成像目标,传统X射线计算层析(CT)成像模式易出现投影信息缺失等问题,影响CT重建的图像质量,使得CT成像的应用受到一定的限制.本文主要研究了基于光场成像理论的X射线三维立体成像技术.首先从同步辐射光源模型出发,对X射线光场成像进行建模;然后,基于光场成像数字重聚焦理论,对成像目标场在深度方向上进行切片重建.结果表明:该方法可以实现对成像目标任一视角下任一深度的内部切片重建,但是由于光学聚焦过程中的离焦现象,会引入较为严重的背景噪声.当对其原始数据进行滤波后,再进行X射线光场重聚焦,可以有效消除重建伪影,提高图像的重建质量.本研究既有算法理论意义,又可应用于工业、医疗等较复杂目标的快速检测,具有较大的应用价值.     

基于光场成像原理和混沌系统的多图像加密方法

针对现有光学加密方法对加密系统要求高、受器件性能限制、加密效率低、解密图像易失真的局限性,提出一种基于光场成像原理和混沌系统的多图像加密方法.该方法利用混沌系统随机生成光场成像系统的个数与系统参数,并在计算机中构造出相应的多个光场成像系统;将多幅待加密图像拼接后置于光场成像系统中依次计算得到光场图像,通过提取光场图像的多幅子孔径图像并进行拼接,实现多幅图像的快速加密.解密过程为加密过程的逆过程.该方法将计算成像的方式引入加密过程,使加密不受硬件条件的限制,易于实现.实验结果表明,提出的算法密钥复杂度低,易于传输;对噪声有较好的鲁棒性,密钥空间大,密钥敏感度高,安全性好;加密效率高,解密图像无损失.在需要大量图像进行安全传输的领域具有广泛的应用前景.     

强度涨落在热光鬼成像中的作用

热光鬼成像的图像质量在实际应用中具有重要作用. 通过理论分析和数值模拟, 发现光场的强度涨落程度会影响热光鬼成像的对比度, 基于此, 提出可以通过调节热光场的平均强度和强度波动的方差来提高成像对比度, 并且研究了这一方法对成像信噪比的影响. 将这种方法与另一种提高成像对比度的方法——高阶鬼成像进行了对比, 所得结果将有助于提高对热光鬼成像的理解. 关键词： 鬼成像 强度涨落 对比度 信噪比     

基于相机扫描的太赫兹光场成像预处理方法

太赫兹光场成像是一种太赫兹波段内的计算成像手段。在太赫兹相机扫描成像的基础上进行了图像预处理和光场计算。针对太赫兹相机受器件性能限制导致输出图像存在噪声大、分辨率低和视场小等问题,对基于相机扫描的太赫兹光场成像进行了预处理,通过控制二维平移台,采集到一系列存在特定视角差别的目标图像;采取高通滤波方式对图像进行处理,得到噪声小、锐化程度好的图像;在不降低图像分辨率的基础上利用Harris特征图像拼接算法计算得到较大视场的图像。通过上述方法,有效地提高了光场成像质量,为实现太赫兹光场成像三维重构奠定了基础。     

数字对焦光场成像清晰度评价方法研究

结合光场成像数字对焦空间域算法和频率域算法的区别,分别提出了适用的图像清晰度评价方式,并进行了仿真实验分析.空间域评价方法根据评价区域缩小图像重构数据量,频率域评价方法则将评价函数嵌入到图像重构过程中,改进后的方法相对传统方法提高了速度.     

数字对焦光场成像清晰度评价方法研究

结合光场成像数字对焦空间域算法和频率域算法的区别,分别提出了适用的图像清晰度评价方式,并进行了仿真实验分析.空间域评价方法根据评价区域缩小图像重构数据量,频率域评价方法则将评价函数嵌入到图像重构过程中,改进后的方法相对传统方法提高了速度.     

基于平场光栅的稀疏约束鬼成像高光谱相机

基于稀疏约束的鬼成像光谱相机,能够通过单次曝光获得目标场景的三维空间光谱数据立方体。但是由于不同波长的散斑场在探测器的同一位置处,使得仪器的光谱分辨率和信噪比受到限制。为了解决上述问题,提出了利用平场光栅分光将不同波长的光场在探测面上错开一定距离的系统,实现了基于平场光栅的稀疏约束鬼成像高光谱相机。通过对系统成像过程的理论推导,得到了系统的关联函数,并通过实验和数值模拟验证了理论推导结果。在保证原先光谱相机优点的同时,基于稀疏约束的鬼成像高光谱相机可以分别调控光谱分辨率和空间分辨率,实现可控的信噪比。此外,还能够根据不同波长的光场特性来优化测量矩阵,从而提高图像恢复质量。     

基于邻域相似度的压缩感知鬼成像

为了提高压缩感知鬼成像的成像质量以及解决低采样率条件下成像失真度高的问题,提出一种基于邻域相似度的鬼成像(NSGI)方案。邻域相似度体现在图像像素间的关联性,携带关于物体结构的重要信息,在分析压缩鬼成像原理的基础上,利用邻域相似度来评价待探测目标。根据贪婪算法的原理,采用邻域相似度优化图像重构过程,并设置相关度阈值降低计算的复杂度。仿真和实验结果均表明,与传统方法相比,该方案可以在低采样率条件下获得高质量低失真度的图像,有利于推动鬼成像技术的实用化。     

压缩感知理论在图像处理领域的应用

针对传统的采样方法得到的图像数据量巨大,给图像信息的后续处理造成极大压力的问题,对压缩感知理论（Compressed Sensing,CS）进行了研究。压缩感知理论使采集很少一部分数据并且从这些少量数据中重构出更大量信息的想法变成可能,突破了奈奎-斯特采样定理的限制。综述了CS理论及关键技术问题,并着重介绍了CS理论在成像系统、图像融合、图像目标识别与跟踪等方面的应用与发展状况。文章指出CS理论开拓了信息处理的新思路,随着该理论的进一步完善,会有更广泛的应用领域。     

基于阈值分割的并行压缩鬼成像方法

为了提高鬼成像的速度,提出了基于阈值分割的并行压缩差分鬼成像方法。首先通过设置合理的阈值筛选出涨落较为明显的数据进行采样;然后再通过并行分块降低图像维度,提高整体运算速度;最后通过压缩感知重构算法重构图像。通过对图"BUAA"的半物理仿真以及透射物体"光"的实验结果表明,与普通的二阶赝热光关联成像相比,该方法能以更少的采样次数和运行时间重构图像,可提升成像信噪比。     

基于自适应阈值方法实现迭代降噪鬼成像

为了有效降低传统鬼成像中相关噪声对成像质量的影响,本文提出一种基于最佳阈值的迭代降噪鬼成像.首先在迭代降噪鬼成像的基础上,采用自适应阈值迭代法,在不需要目标先验信息的前提下,找到一个逼近传统鬼成像中相关噪声的阈值,根据得到的阈值构造噪声干扰项.为了每次迭代初值更接近原始目标的透射系数,对其进行二值化,降低重构图像背景噪声对迭代性能的影响.仿真以及实验结果表明,本文提出的方法与传统方法相比,视觉效果以及峰值信噪比值有明显提高.     

Correspondence normalized ghost imaging on compressive sensing

Ghost imaging(GI) offers great potential with respect to conventional imaging techniques. It is an open problem in GI systems that a long acquisition time is be required for reconstructing images with good visibility and signal-to-noise ratios(SNRs). In this paper, we propose a new scheme to get good performance with a shorter construction time. We call it correspondence normalized ghost imaging based on compressive sensing(CCNGI). In the scheme, we enhance the signal-to-noise performance by normalizing the reference beam intensity to eliminate the noise caused by laser power fluctuations, and reduce the reconstruction time by using both compressive sensing(CS) and time-correspondence imaging(CI) techniques. It is shown that the qualities of the images have been improved and the reconstruction time has been reduced using CCNGI scheme. For the two-grayscale "double-slit" image, the mean square error(MSE) by GI and the normalized GI(NGI) schemes with the measurement number of 5000 are 0.237 and 0.164, respectively, and that is 0.021by CCNGI scheme with 2500 measurements. For the eight-grayscale "lena" object, the peak signal-to-noise rates(PSNRs)are 10.506 and 13.098, respectively using GI and NGI schemes while the value turns to 16.198 using CCNGI scheme. The results also show that a high-fidelity GI reconstruction has been achieved using only 44% of the number of measurements corresponding to the Nyquist limit for the two-grayscale "double-slit" object. The qualities of the reconstructed images using CCNGI are almost the same as those from GI via sparsity constraints(GISC) with a shorter reconstruction time.     

Orthogonal-triangular decomposition ghost imaging

Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. However, there are still some obstacles for reconstructing images with high quality, especially in the case that the orthogonal measurement matrix is impossible to construct. In this paper, we propose a new scheme based on the orthogonal-triangular (QR) decomposition, named QR decomposition ghost imaging (QRGI) to reconstruct a better image with good quality. In the scheme, we can change the randomly non-orthogonal measurement matrix into orthonormal matrix by performing QR decomposition in two cases. (1) When the random measurement matrix is square, it can be firstly decomposed into an orthogonal matrix $\bm Q$ and an upper triangular matrix $\bm R$. Then let the off-diagonal values of $\bm R$ equal to 0.0, the diagonal elements of $\bm R$ equal to a constant $k$, where $k$ is the average of all values of the main diagonal, so the resulting measurement matrix can be obtained. (2) When the random measurement matrix is with full rank, we firstly compute its transpose, and followed with above QR operation. Finally, the image of the object can be reconstructed by correlating the new measurement matrix and corresponding bucket values. Both experimental and simulation results verify the feasibility of the proposed QRGI scheme. Moreover, the results also show that the proposed QRGI scheme could improve the imaging quality comparing to traditional GI (TGI) and differential GI (DGI). Besides, in comparison with the singular value decomposition ghost imaging (SVDGI), the imaging quality and the reconstruction time by using QRGI are similar to those by using SVDGI, while the computing time (the time consuming on the light patterns computation) is substantially shortened.     

基于lp范数的压缩感知图像重建算法研究

图像重建是光学成像、光声成像、声纳成像、核磁共振成像、 天体成像等物理成像领域中的关键技术之一. 近年来提出的压缩感知理论指出: 对稀疏或者可压缩信号进行少量非自适应线性投影,投影信号含有足够的信息, 从而能对信号进行高概率重建. 压缩感知已被应用于多种物理成像系统. 将罚函数法和修正Hesse阵序列二次规划方法相结合, 并采用了分块压缩感知思想, 提出一种基于l_p范数的压缩感知图像重建算法. 以cameraman, barbara和mandrill图像为例, 采用该算法进行图像重建. 首先, 在不同采样率下对图像重建. 即便采样率低至0.3时, 也能获得高达32.23dB的信噪比, 重建图像清晰可辨. 验证了该算法的正确性. 其次, 将该算法与正交匹配追踪算法进行对比, 在采样率达到0.5以上时, 能够获得高信噪比的重建图像, 成像时间也大为减少, 特别是采样率为0.7时, 成像时间减少88%. 最后, 与现有基于l_p 范数的压缩感知图像重建算法进行对比, 计算结果表明在成像质量有所提高的基础上, 成像时间大为缩短. 关键词： 图像重建 压缩感知 罚函数 修正Hesse阵序列二次规划     

基于压缩感知的差分关联成像方案研究

关联成像可提供一种运用常规手段难以获得清晰图像的方法, 能够解决一些常规成像技术不易解决的问题, 是近些年来量子光学领域的前沿和热点之一.本文提出一种基于压缩感知的差分关联成像方案(简称, 差分压缩关联成像方案), 将高斯分布的热光源强度分布作为压缩感知的测量矩阵, 差分物体信息作为被成像物体信息, 利用差分关联成像方案的高成像信噪比和压缩感知技术的低采样次数, 通过正交匹配追踪算法, 高质量地恢复出物体信息. 并以二灰度“双缝”、“NUPT”, 多灰度Lena图和Boats图为例, 数值仿真差分压缩关联成像过程; 同时将本方案350次测量的结果与差分关联成像方案30000次测量的结果进行对比, 研究结果表明针对不同的被成像物体(二灰度“双缝”、“NUPT”, 以及多灰度Lena图和Boats图), 10次成像的均方误差平均值分别降低了97.7%, 93.9%, 92.5%和71.4%; 与压缩鬼成像方案相比, 同样测量次数条件下均方误差值对于二灰度双缝和多灰度Lena图、Boats图等目标物 体分别有50.4%, 72.9%和66.8%的降低. 差分压缩关联成像方案极大地提高了成像信噪比, 降低了成像时间. 关键词： 关联成像 差分 压缩感知 均方误差     

Single-shot and lensless complex-amplitude imaging with incoherent light based on machine learning

We present a single-shot incoherent light imaging method for simultaneously observing both amplitude and phase without any imaging optics, based on machine learning. In the proposed method, an object with a complex-amplitude field is illuminated with incoherent light and is captured by an image sensor with or without a coded aperture. The complex-amplitude field of the object is reconstructed from a single captured image using a state-of-the-art deep convolutional neural network, which is trained with a large number of input and output pairs. In experimental demonstrations, the proposed method was verified with a handwritten character database, and the effect of a coded aperture printed on an overhead projector film in the reconstruction was examined. Our method has advantages over conventional wavefront sensing techniques using incoherent light, namely simplification of the optical hardware and improved measurement speed. This study shows the importance and practical impact of machine learning techniques in various fields of optical sensing.     

Computational ghost imaging with deep compressed sensing

Computational ghost imaging (CGI) provides an elegant framework for indirect imaging, but its application has been restricted by low imaging performance. Herein, we propose a novel approach that significantly improves the imaging performance of CGI. In this scheme, we optimize the conventional CGI data processing algorithm by using a novel compressed sensing (CS) algorithm based on a deep convolution generative adversarial network (DCGAN). CS is used to process the data output by a conventional CGI device. The processed data are trained by a DCGAN to reconstruct the image. Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning. Moreover, the background noise can be eliminated well by this method.     

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     

Efficient implementation of x-ray ghost imaging based on a modified compressive sensing algorithm

Towards efficient implementation of x-ray ghost imaging (XGI), efficient data acquisition and fast image reconstruction together with high image quality are preferred. In view of radiation dose resulted from the incident x-rays, fewer measurements with sufficient signal-to-noise ratio (SNR) are always anticipated. Available methods based on linear and compressive sensing algorithms cannot meet all the requirements simultaneously. In this paper, a method based on a modified compressive sensing algorithm with conjugate gradient descent method (CGDGI) is developed to solve the problems encountered in available XGI methods. Simulation and experiments demonstrate the practicability of CGDGI-based method for the efficient implementation of XGI. The image reconstruction time of sub-second implicates that the proposed method has the potential for real-time XGI.     

双树小波变换与小波树稀疏联合的低场CS-MRI算法

压缩感知理论常用在磁共振快速成像上,仅采样少量的K空间数据即可重建出高质量的磁共振图像.压缩感知磁共振成像技术的原理是将磁共振图像重建问题建模成一个包含数据保真项、稀疏先验项和全变分项的线性组合最小化问题,显著减少磁共振扫描时间.稀疏表示是压缩感知理论的一个关键假设,重建结果很大程度上依赖于稀疏变换.本文将双树复小波变换和小波树稀疏联合作为压缩感知磁共振成像中的稀疏变换,提出了基于双树小波变换和小波树稀疏的压缩感知低场磁共振图像重建算法.实验表明,本文所提算法可以在某些磁共振图像客观评价指标中表现出一定的优势.