轴向运动目标鬼成像点扩散函数质量评价方法

研究了鬼成像系统的点扩散函数(PSF),以轴向运动目标的鬼成像系统为例理论推导出其点扩散函数,并进行了数值模拟和计算机仿真实验.结果表明:目标在信号光路轴向的运动会导致成像质量的下降,速度越大成像质量越差,应用点扩散函数方法分析鬼成像系统的成像质量可以在不成像的情况下预先判断成像效果,相对于传统的质量评价方式有很大优势.     

相干光照明下无衍射系统的点扩散函数的测量

在分析相干光照明下无衍射成像系统理论的基础上,设计了无衍射光成像系统的点扩散函数的测量系统。利用菲涅耳衍射理论,推导出相干光照明下无衍射系统的点扩散函（PSF）的形式,并分析出点扩散函数的条纹疏密与锥镜的夹角和点光源的位置有关。实验和仿真结果表明,点扩散函数的条纹间距随锥镜夹角增大而变窄,随点光源距透镜距离的增大而变窄。     

湍流变化对多孔径光学系统成像特性的影响

为了研究大气湍流变化对多孔径光学系统成像质量的影响,针对Golay3结构建立了一个理论分析模型,推导了湍流影响下望远系统的点扩散函数（point spread function, PSF）的表达式。分别针对近似圆形和近似长条状分布的湍流结构对成像特性的影响进行了讨论,具体构建了两种湍流影响下的光场相位计算模型,并计算比较了两种湍流的调制传递函数（modulation transfer function, MTF）。结果表明,光路中存在湍流将导致系统成像质量下降。在近似圆形湍流中,湍流强度越小,系统MTF影响越小。不同强度湍流影响的MTF在归一化空间频率0.16、0.45和0.69处均下降0.05左右。在近似长条状湍流中,对流湍流风速越小,系统MTF影响越小。不同风速的湍流影响的MTF在空间频率0.16、0.42和0.69处分别下降0.25、0.09和0.05左右。比较两种湍流表明,近似长条状湍流对成像系统MTF影响更为明显。     

热光关联成像的均方收敛无偏估计方法

关联成像实验和理论证明了光可以用特殊的方式传递信息。文中采用半经典理论,按照赝热光场、光电探测和关联计算的成像过程,系统分析了关联成像的理论基础,并给出了系统的视场、空间分辨率和对比度。在此基础上,对传统关联成像的线性计算方法进行了改进,使之为物体透射函数的均方收敛无偏估计。相应的计算鬼成像仿真实验表明:在相同测量次数下,尤其是低于奈奎斯特采样频率时,该方法显著提高了鬼像的峰值性噪比并有效抑制了背景噪声。     

中国科学院上海光学精密机械研究所鬼成像理论研究的若干进展（特邀）

相比利用光场的一阶关联实现物空间与像空间一一对应的传统成像,鬼成像基于光场的二阶关联实现物空间与像空间的一一对应,从而获取物体图像信息。通过引入光场涨落调制和计算重构,鬼成像不仅可以具有更高的信息获取效率,而且提升了图像信息获取方式的灵活性,能够具备传统成像所不具备的成像能力。随着鬼成像在系统优化及技术应用方面的进一步发展,对鬼成像理论也提出了新的要求和挑战。文中分别从鬼成像的物理本质、图像信息获取理论及理论分辨率研究三方面介绍了中国科学院上海光学精密机械研究所近期在鬼成像理论上的若干研究工作,并对今后鬼成像的理论研究工作进行了展望。     

长曝光大气湍流退化图像点扩散函数估计

大气湍流能明显降低光学系统的成像质量,距离目标越远,曝光时间越长,受大气扰动越严重,图像越模糊。利用大气湍流退化点扩散函数可以对模糊图像进行复原,但实际自然条件下的点扩散函数往往难以准确获得。结合课题研究背景,针对长曝光大气湍流退化图像复原提出了近似等腰三角形模型,通过该模型能得到准确的大气湍流点扩散函数,并采用维纳滤波获得清晰复原图像。实验表明该方法能够对大视场、远距离条件下获得的长曝光大气湍流退化自然图像估计出准确的点扩散函数,复原图像拥有较好的视觉效果,通过计算灰度平均梯度值和拉普拉斯梯度模两个客观评价标准,进一步证实了该算法的有效性。     

嫦娥一号卫星CCD立体相机影像超分辨率重建算法

针对嫦娥一号卫星CCD立体相机空间分辨率不足的问题,运用最大后验概率估计法(MAP)实现了月表影像的超分辨率重建。介绍了嫦娥一号卫星CCD立体相机的成像模型,分析了图像获取过程中的主要影响因素,并建立了相应的超分辨率重建模型。基于该模型,首先采用误差-参数分析法估计嫦娥一号卫星CCD立体相机动态成像光学系统的点扩散函数(PSF);然后将估计的PSF应用到MAP算法所建立的目标函数中,采用共轭梯度法对目标函数进行最值求解;再通过VC软件平台编程实现了对单帧正视月表影像的超分辨率重建;最后从信息熵、清晰度和频谱等方面对重建图像进行评价,结果表明重建图像像质优良。     

光学合成孔径成像原理及图像复原技术

根据光学合成孔径系统成像原理,利用傅立叶光学理论得出了合成孔径系统的点扩散函数（PSF）和光学传递函数（OTF）,从这两个参数分析了合成孔径成像系统的性能;给出了使用维纳滤波算法进行图像复原的方法。分析和实验结果表明,用光学合成孔径系统和图像复原技术完全可以达到单个大孔径的成像观测效果。     

波前编码—图像处理一种新方法

数字化图像处理不久将成为提高光学系统性能的方法。美国科拉拉多州的CDM光学公司研制一种特殊的图像轮廓清晰板(specially contoured clear plate)插入任意标准光学系统的光瞳面以改变系统的点扩散函数(PSF)。此透明板使PSF强度分布保持不变,甚至光学系统像差较严重时,也可得到此结果,因此,可以简化图像处理要求。点扩散函数是一个简单的点目标图像,通常在有像差的情况下,如散焦或像散出现时,点扩散函数变化很大。一个扩展目标的图像就会造成无数个点扩散函数,如果对视场     

劈裂纳米矩形的Fano共振效应及对不同偏振角入射激光的衰减作用

为了探讨光场的高阶关联对于轴向运动目标鬼成像 成像质量的影响,将热光高阶鬼成像相关理论应用于轴向运动目标鬼成像的研究中。首先, 建立了理想热光高阶强度涨落的轴向运动目标鬼成像模型,给出轴向运动目标高阶鬼成像的 高阶关联函数;接着,设置不同仿真参数对建立的数学模型进行了数值模拟,同时给出了相 关仿 真图像;最后,采用峰值信噪比(PSNR)质量评价方法 比较仿真图像的成像质量,并对仿真结果进行了分析。结果表明,对于轴向运动目标,随着 参考光束的阶数增加,光场的高阶关联将会极大的增加背景 涨落的噪声,导致图像重建质量急剧变差;而信号光束的阶数增加对成像质量影响较小,但 是成像质量会 随着信号光束的增加而增加,最低阶的鬼成像得到的鬼像质量最好。     

On the accuracy of PSF representation in image restoration

Point spread function (PSF) models derived from physical optics provide a more accurate representation of real blurs than simpler models based on geometrical optics. However, the physical PSF models do not always result in a significantly better restoration, due to the coarse sampling of the recording device and insufficiently high signal-to-noise ratio (SNR) levels. Low recording resolutions result in aliasing errors in the PSF and suboptimal restorations. A high-resolution representation of the PSF where aliasing errors are minimized is used to obtain improved restorations. The SNR is the parameter which ultimately limits the restoration quality and determines the need for an accurate PSF model. As a rule of thumb, the geometrical PSF can be used in place of the physical PSF without significant loss in restoration quality when the SNR is less than 30 dB.     

Image restoration in computed tomography: estimation of the spatially variant point spread function

An indirect method for determining the point-spread-function (PSF) in computed tomography (CT) is described. Unlike experimental techniques in which a resolution phantom is scanned to obtain the system PSF, the approach estimates the parameters of a model which describes the two-dimensional X-ray beam profile at each point as a convolution of the appropriately scaled focal spot intensity and detector sensitivity distributions. The model was validated by experimental measurement of the focal spot intensity distribution. Using known X-ray beam profiles, the PSF of a CT scanner can be derived by simulating the data collection process and applying conventional image reconstruction techniques. Visual comparison of directly measured and computed PSFs reveals an asymmetry resulting from misregistration of the phantom wires and the image grid.     

单帧振动模糊图像的快速恢复算法

振动退化图像的恢复算法面临的主要问题是在比较宽松的约束条件下对模糊图像的快速恢复。在对国内外的振动模糊图像恢复算法进行充分研究的前提下,提出一种快速有效的算法。首先应用伪白化叠加的方法得到与点扩展函数对应的调制传递函数,然后根据最小相位的理论,解线性方程组得到点扩展函数。在较少的约束条件下避免了传统算法循环迭代计算复杂、费时等缺点,实验结果表明,恢复效果较好,抗噪声能力强。     

Blind deblurring of spiral CT images

To discriminate fine anatomical features in the inner ear, it has been desirable that spiral computed tomography (CT) may perform beyond their current resolution limits with the aid of digital image processing techniques. In this paper, we develop a blind deblurring approach to enhance image resolution retrospectively without complete knowledge of the underlying point spread function (PSF). An oblique CT image can be approximated as the convolution of an isotropic Gaussian PSF and the actual cross section. Practically, the parameter of the PSF is often unavailable. Hence, estimation of the parameter for the underlying PSF is crucially important for blind image deblurring. Based on the iterative deblurring theory, we formulate an edge-to-noise ratio (ENR) to characterize the image quality change due to deblurring. Our blind deblurring algorithm estimates the parameter of the PSF by maximizing the ENR, and deblurs images. In the phantom studies, the blind deblurring algorithm reduces image blurring by about 24%, according to our blurring residual measure. Also, the blind deblurring algorithm works well in patient studies. After fully automatic blind deblurring, the conspicuity of the submillimeter features of the cochlea is substantially improved.     

Blur identification by residual spectral matching

The estimation of the point spread function (PSF) for blur identification, often a necessary first step in the restoration of real images, method is presented. The PSF estimate is chosen from a collection of candidate PSFs, which may be constructed using a parametric model or from experimental measurements. The PSF estimate is selected to provide the best match between the restoration residual power spectrum and its expected value, derived under the assumption that the candidate PSF is equal to the true PSF. Several distance measures were studied to determine which one provides the best match. The a priori knowledge required is the noise variance and the original image spectrum. The estimation of these statistics is discussed, and the sensitivity of the method to the estimates is examined analytically and by simulations. The method successfully identified blurs in both synthetically and optically blurred images.     

数字全息干涉测量中消相关误差的理论分析与仿真估计

为了对数字全息干涉测量中消相关误差进行理论分析与估计,首先推导了数字全息威像系统的点扩散函数的一般形式,对其作出了一定近似;并通过对二次曝光数字全息干涉法中同一像点的复振幅进行统计光学分析,得出了消相关误差的统计性质;最后通过MATLAB对其进行了仿真.仿真结果表明:理论分析得出的消相关误差的统计性质是符合消相关现象的;进行数字全息干涉测量的条件是每两次曝光的数字全息图的消相关度应满足ρx+ρy＜0.1.     

Blind image restoration by anisotropic regularization

This paper presents anisotropic regularization techniques to exploit the piecewise smoothness of the image and the point spread function (PSF) in order to mitigate the severe lack of information encountered in blind restoration of shift-invariantly and shift-variantly blurred images. The new techniques, which are derived from anisotropic diffusion, adapt both the degree and direction of regularization to the spatial activities and orientations of the image and the PSF. This matches the piecewise smoothness of the image and the PSF which may be characterized by sharp transitions in magnitude and by the anisotropic nature of these transitions. For shift-variantly blurred images whose underlying PSFs may differ from one pixel to another, we parameterize the PSF and then apply the anisotropic regularization techniques. This is demonstrated for linear motion blur and out-of-focus blur. Alternating minimization is used to reduce the computational load and algorithmic complexity.     

Performance of reconstruction-based super-resolution with regularization

From the perspective of linear algebra, the performance of super-resolution reconstruction (SR) depends on the conditioning of the linear system characterizing the degradation model. This is analyzed in the Fourier domain using the perturbation theory. By proposing a new SR error bound in terms of the point spread function (PSF), we reveal that the blur function dominates the condition number (CN) of degradation matrix, and the advantage of non-integer magnification factors (MFs) over the integer ones comes from sampling zero crossings of the DFT of the PSF. We also explore the effect of regularization by integrating it into the SR model, and investigate the influence of the optimal regularization parameter. A tighter error bound is derived given the optimal regularization parameter. Two curves of error bounds vs. MFs are presented, and verified by processing real images. It explains that with proper regularization, SR at the integer MFs is still valid.     

转台模式的近场微波成像研究

针对转台模式的这场微波成像进行了深入研究。首先给出了转台模式下图像函数（描述目标的几何特征）与球投影的积分变换关系，并导出了图像函数的精确解。然后从线性系统的角度出发，研究了理想情况和实际情况下转台模式近场微波成像的分辨特性，理想情况下成像系统的点扩散函数（ＰＳＦ）就是狄拉克－函数。ＰＳＦ的数值模拟结果表明：实际情况下，扫描频带越宽分辨率越高。