基于自校验的单像素成像系统动态干扰去除方法

单像素成像系统通过对目标场景的多次调制,获取相应的单像素测量值,并由此重构图像.在这一过程中,如果其他物体侵入成像场景,会严重影响测量值的准确性,降低重构图像质量.由于侵入物体的反射率,形态均具有一定的随机性,因此从桶探测器信号的角度很难有效分离出受干扰信号.针对这一问题,基于哈达玛矩阵的特征,提出了一套自校验方法,即利用桶探测器自身测量值进行正确性校验,筛选出未受干扰的桶探测器信号,显著提升了重构图像的质量.该方法适用于一般性成像场景,且不需要引入额外的调制图像辅助校验,有力推动了单像素成像技术的实用化进程.     

基于锁相光子计数测量的单像素空间频率域成像系统

基于锁相光子计数技术,提出一种多波长并行检测的单像素空间频率域成像(SFDI)系统。以一个数字微镜器(DMD)为待测多波长漫反射光的调制光源,以另一个DMD为漫反射光采集、编码与会聚设备,使得会聚后的漫反射光通过锁相光子计数技术实现不同波长的解调分离,同时降低了系统成本。引入压缩感知图像恢复理论,有效缩短了多次空间编码引起的单像素成像时间。实验结果表明,所提SFDI系统只需要进行像素总数20%左右的编码,即可准确重构多波长下仿体表面漫反射光图像。     

基于Hadamard矩阵优化排序的快速单像素成像

为提升单像素成像速度,提出了基于Hadamard矩阵优化排序的压缩采样解决方案.利用数值仿真和室外实验对提出的5种排序方法进行了对比分析.研究结果表明:按Haar小波变换系数绝对值排序时单像素成像效果最优,排序对应到Walsh序后可利用快速变换重建图像,速度达300帧/秒@64×64像素;最优排序下,采样率25%仍可重建图像,采样速度可提升4倍.针对排序方法与成像信噪比关系,从关联成像角度给出了其物理解释:测量基矩阵元邻域数值相等的区域面积等效于光场二阶相干面积,当光场二阶相干面积随测量基由大到小排序时成像效果最优.本文研究成果可用于提升单像素成像速度,具有实用价值.     

基于压缩感知的后调制远距离三维成像研究

基于压缩感知(CS)理论,提出使用高功率纳秒脉冲激光器照射远距离目标物体,通过望远系统将目标物体成像到数字微镜阵列(DMD)上,利用DMD加载的调制图像对目标物体的像进行调制(后调制),采用光电倍增管(PMT)作为单像素探测器收集调制后的光强,通过压缩感知计算,完成对远距离目标物体的三维图像重建。将此系统应用于远距离三维成像,通过搭建实验系统,分别对距离为230m和4.5km左右的目标物体完成了绝对距离的测量,实现了64pixel×64pixel的三维成像。同时也证实利用压缩感知进行远距离图像恢复,随着采样率的提高,图像恢复的质量和对比度都有一定程度的提高;目标物体图像越稀疏,重构图像所需的采样次数越少。     

基于纯相位调制的散射介质传输矩阵测量与光波聚焦

提出了基于纯相位调制的传输矩阵通用测量方法,搭建了传输矩阵测量的实验装置。分别利用单位矩阵调制和哈达玛矩阵调制方式进行了传输矩阵测量,并实现了穿透散射介质的光波聚焦。实验结果表明,基于单位矩阵测得的传输矩阵,其聚焦点光强为背景光强的19倍;而基于哈达玛矩阵测得的传输矩阵,其聚焦点光强为背景光强的16倍。     

基于元素图像阵列采样的集成成像重构方法

高质量的集成成像系统需要采集和存储大量图像数据。提出了一种新的计算机重构方法,可以减少集成成像系统显示端在计算机重构时所需要使用的数据量。传统的计算机重构方法要利用每个元素图像,而此方法对CCD相机采集的元素图像阵列进行周期采样,仅利用被采样的元素图像即可重构出多视点图像。这种方法充分利用集成成像中邻近的元素图像中具有匹配像素的特点,将未被采样的元素图像信息由邻近的被采样的元素图像中的匹配像素替代,从而减少了重构过程中使用的数据量,这给不同性能的集成成像显示端带来很大的灵活性,降低了对存储空间和传输带宽的要求。     

基于交叉验证的超微弱光子图像去噪处理

超微弱光子图像的很多有用部分被噪音所淹没,为了提高光子图像质量,提取更多有用信息,将一种基于交叉验证理论的小波滤波器引入到超微弱光子计数成像采集系统中,计算机模拟及实验结果显示:这种处理方法能滤除较多噪音、很好地保留图像信息并显著提高图像信噪比.     

光子计数成像系统的自适应累积时间扫描方法

得益于单光子探测器的超高灵敏度,光子计数成像系统近年来成为极弱光探测成像领域的研究热点.基于单点扫描的光子计数成像系统,以光子累积的方式获取大量返回光子事件后重建目标图像.然而,单像素探测时间固定导致的光子事件累积冗余或累积不足的问题,限制了系统的成像效率.本文提出了一种基于自动选取单像素最佳累积时间的时间自适应扫描方法,并分别进行了单点测距和扫描成像实验.结果表明,本文提出的方法在重建质量接近的深度图像(64×88)时的数据总获取时间相比单像素固定累积时间的扫描方法降低了一个数量级,极大地提高了扫描数据获取的效率,为光子计数成像系统快速成像提供了新思路.     

哈达玛变换光谱成像仪光谱响应非均匀性修正

哈达玛变换光学是近些年发展起来的一门新技术,已经被广泛应用于目标设别、微弱信号检测等领域。基于DMD的哈达玛变换光谱成像仪是一种新型的色散型光谱成像仪。介绍了哈达玛变换成像原理,在自行研制的哈达玛变换光谱成像仪基础上,从光谱定标的角度,研究了哈达玛变换光谱成像仪获得的编码图像的光谱响应非均匀性。针对哈达玛变换光谱成像仪获得的光谱图像中叠加非均匀性噪声及光谱混叠影响光谱复原精度的问题,首次在该光谱成像仪上利用辐射度相对光谱修正和绝对光谱修正算法,对光谱响应非均匀性进行修正。仿真计算和实验结果表明,对于哈达玛变换光谱仪采集的七个波段的光谱图像,修正后的光谱曲线与辐射度计获得的谱线精度非常接近,使复原光谱的误差在2.4%～4.2%的范围,满足实验室和工程应用的技术要求。     

数字投影三维数字成像的并行DSP实现

提出一种基于数字白光投影的三维数字成像嵌入式DSP处理系统.该系统根据时序变频条纹投影原理,利用TMS320DM642定点DSP和FPGA硬件电路以及多线程软件技术实现了条纹编码、编码图像采集和条纹分析的流水线处理过程.为了提高相位解调算法的速度,使用软件流水对其进行了优化.对于512×512像素图像,五步相移时的条纹图处理速度可达到39.7 fps,能满足快速条纹图处理的要求.测量实验表明,该系统可以实现快速、准确的三维轮廓测量.     

Imaging a periodic moving/state-changed object with Hadamard-based computational ghost imaging

We propose a method for imaging a periodic moving/state-changed object based on computational ghost imaging with Hadamard speckle patterns and a slow bucket detector, named as PO-HCGI. In the scheme, speckle patterns are produced from a part of each row of a Hadamard matrix. Then, in each cycle, multiple speckle patterns are projected onto the periodic moving/state-changed object, and a bucket detector with a slow sampling rate records the total intensities reflected from the object as one measurement. With a series of measurements, the frames of the moving/state-changed object can be obtained directly by the second-order correlation function based on the Hadamard matrix and the corresponding bucket detector measurement results. The experimental and simulation results demonstrate the validity of the PO-HCGI. To the best of our knowledge, PO-HCGI is the first scheme that can image a fast periodic moving/state-changed object by computational ghost imaging with a slow bucket detector.     

Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information

In this Letter, we propose a novel three-dimensional(3D) color microscopy for microorganisms under photonstarved conditions using photon counting integral imaging and Bayesian estimation with adaptive priori information. In photon counting integral imaging, 3D images can be visualized using maximum likelihood estimation(MLE). However, since MLE does not consider a priori information of objects, the visual quality of 3D images may not be accurate. In addition, the only grayscale image can be reconstructed. Therefore, to enhance the visual quality of 3D images, we propose photon counting microscopy using maximum a posteriori with adaptive priori information. In addition, we consider a wavelength of each basic color channel to reconstruct 3D color images. To verify our proposed method, we carry out optical experiments.     

光子计数成像研究

从APD阵列的响应特性出发,利用随机点的统计学描述,建立了光子计数成像的一维点过程数学模型。通过对光子事件发生时间统计特性的描述,分析了光子密度、门控时间、雪崩光电二极管恢复时间等因素对光子计数成像质量的影响。并采用蒙特卡罗方法,对近红外波段不同光子密度条件下多种计数间隔的成像质量进行了仿真,给出了仿真结果。     

Improved matrix inversion in image plane parallel MRI

A new 3D parallel magnetic resonance imaging (MRI) method named Generalized Unaliasing Incorporating Support constraint and sensitivity Encoding (GUISE) is presented. GUISE allows direct image recovery from arbitrary Cartesian k-space trajectories. However, periodic k-space sampling patterns are considered for reconstruction efficiency. Image recovery methods such as 2D SENSE (SENSitivity Encoding) and 2D CAIPIRINHA (Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration) are special instances of GUISE where specific restrictions are placed on the k-space sampling patterns used. It is shown that the sampling pattern has large impacts on the image reconstruction error due to noise. An efficient sampling pattern design method that incorporates prior knowledge of object support and coil sensitivity profile is proposed. It requires no experimental trials and could be used in clinical imaging. Comparison of the proposed sampling pattern design method with 2D SENSE and 2D CAIPIRINHA are made based on both simulation and experiment results. It is seen that this new adaptive sampling pattern design method results in a lower noise level in reconstructions due to better exploitation of the coil sensitivity variation and object support constraint. In addition, elimination of the non-object region from reconstruction potentially allows an acceleration factor higher than the number of receiver coils used.     

Three-dimensional photon counting integral imaging using moving array lens technique

In this Letter, we present three-dimensional (3D) photon counting integral imaging using the moving array-lens technique (MALT) to improve the visualization of a reconstructed 3D scene. In 3D scene reconstruction of photon counting integral imaging, various techniques such as maximum likelihood estimation may be used. However, the visual quality depends on the number of scene photons or detector pixels activated by photons. We show that MALT may improve the viewing resolution of integral imaging for reconstructed 3D scene under photon-starved conditions.     

High speed ghost imaging based on a heuristic algorithm and deep learning

We report an overlapping sampling scheme to accelerate computational ghost imaging for imaging moving targets,based on reordering a set of Hadamard modulation matrices by means of a heuristic algorithm. The new condensed overlapped matrices are then designed to shorten and optimize encoding of the overlapped patterns, which are shown to be much superior to the random matrices. In addition, we apply deep learning to image the target, and use the signal acquired by the bucket detector and corresponding real image to train the neural network. Detailed comparisons show that our new method can improve the imaging speed by as much as an order of magnitude, and improve the image quality as well.     

3D passive photon counting automatic target recognition using advanced correlation filters

In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.     

基于压缩感知与扩展小波树的自适应压缩成像

为了在现有的采样条件下,通过新的压缩采样方式获得计算量小且质量更好的图像,提出了基于压缩感知与扩展小波树的自适应压缩成像方法。首先将图像投影到分区控制的DMD上,获得图像在低分辨率下的测量值,并通过压缩感知重构算法重构出低分辨图像,接着利用扩展小波树预测重要小波位置,通过DMD在小波域采样获取图像的细节信息,最后由小波逆变换恢复高分辨率图像。将该方法与最小化全变分算法（TVAL3）和近来提出的基于扩展小波树的自适应成像算法（EWT-ACS）效果进行对比,实验结果表明,以boat图像为例,在压缩感知采样率为0.75,整体采样率为10%的无噪声条件下,该方法相较于TVAL3、EWT-ACS算法信噪比提高了4.63 dB和2.87 dB,在附加噪声条件下成像效果也较好。该方法能极大地降低压缩感知重建算法的运行时间,同时减少采样次数,具有较好的抗噪性。