运动模糊图像的复原

分析运动模糊图像的产生过程,给出运动模糊图像恢复的基本方法和关键技术。详细给出运动模糊图像的PSF参数估计,采用Radon变换算法估计模糊相位、基于自相关函数的算法估计模糊长度;随后分析L-R迭代法原理,并结合估计的PSF参数值,对运动模糊图像进行复原处理,基本消除图像由于摄取时的相对运动所造成的模糊和失真。     

基于局部运动模糊图像的测速方法

针对在静止背景中运动的目标对所拍摄图像造成的局部运动模糊,建立了运动速度与图像上运动模糊尺度、目标距离以及曝光时间等相机参数间的对应关系,提出了一种局部运动模糊点扩展函数的参数辨识算法.目标运动方向信息由傅里叶频谱和Radon变换得到.当运动模糊方向调整为水平后,经图像分割,改造的Prewitt算子进行边缘检测,二值图像与边缘图像对应区域上各行进行自相关三个步骤,运动尺度由统计信息确定,并在此基础上完成目标速度的测量.实验结果表明,本文算法对形体较为复杂目标的局部运动模糊参数辨识具有良好的效果,实验测得的运动速度与实际平均速度的误差都在7%以内,有利于自动完成,具有较好的实时性.     

基于快速CCD位移探测的运动模糊图像的恢复

提出了一种可以对被任意形式的运动所造成的退化图像进行恢复的方法,给出了理论推导,并建立了一套实现系统.解决了以前的恢复方法存在的只能恢复特定运动形式(如匀速直线运动)的模糊图像的局限性问题.在主CCD曝光的同时,利用低分辨力的黑白快速CCD获得多帧图像,根据这些图像序列来计算位移,然后求得相对运动的瞬时速度,根据理论分析,点扩展函数和瞬时速度有着对应的关系,进而计算出模糊过程的点扩散函数(PSF),并用来RL迭代法来恢复模糊图像.实验结果表明,这种恢复方法对任意运动形式造成的模糊,能够得到较好的恢复效果.     

运动模糊不变量研究

图像的运动模糊是摄像机在曝光期间与场景间相对运动而造成的图像退化,但现有算法对运动模糊的恢复精度有限.本文分析了图像运动模糊的退化模型,提出了运动模糊不变量的概念,证明了图像零阶矩、第一个Hu不变矩和SIFT为运动模糊不变量,并给出了测试和应用.结果表明运动模糊不变量是存在的,而且可以用于不确定性运动模糊图像的匹配和识别领域.     

基于双谱的点扩展函数参数辨识

点扩展函数的参数辨识对于图像复原具有非常重要的意义.运动模糊和散焦模糊是常见的两种图像模糊类型.针对这两种模糊以及成像过程往往存在噪声的情况,提出了一种基于双谱的点扩展函数参数辨识的方法.介绍了这两种模糊类型及其传统的计算公式.推导了两种模糊类型的双谱,理论上双谱不仅不受噪声的影响,并且具有与模糊函数相似的结构,由此利用传统的模糊参数计算公式就可以计算出模糊参数.实验结果表明,本方法适用于含有噪声的一定模糊参数范围内的散焦模糊和运动模糊图像,即在模糊参数不太大的情况下,本方法可以取得更准确的结果.在信噪比为25 dB的情况下,辨别出该范围内的模糊尺度的偏差不超过1个像素.     

一种模糊红外目标边缘和不变特征提取方法

针对红外图像中模糊目标难以分割和识别的情况,提出以小波多尺度滤波分解后的逼近图像作为自适应域值进行图像二值化的方法,由此分割出模糊图像中的目标,提取目标的轮廓边缘.然后,提出一种新的矩计算方法提取目标的不变性特征.实验结果表明,该方法具有很强的抗噪和抗扰性能,能有效提取复杂背景中模糊目标的平移、缩放和旋转不变量,极大提高了运动模糊目标识别的可靠性.     

运动背景中结合特征位移矢量场模糊分割与 OTSU法的运动检测

运动背景中的运动检测难度较大,背景运动补偿后差分以及分割光流场可实现动目标和背景的分离,差分前需进行鲁棒的背景估计,且差分后易出现空洞,而光流估计在噪声以及目标运动速度较大时并不准确,尤其在光照变化时,两种方法均易失效。本文提出一种特征点位移矢量场模糊分割与图像自适应阈值化相结合的运动检测方法,实现在无任何关于运动目标或者运动背景先验信息条件下的动目标检测。通过改进的 SIFT匹配方法生成鲁棒的特征位移矢量场,采用模糊 C均值聚类算法对 SIFT位移矢量场进行无监督分类,实现动目标与背景特征的自适应分离。 OTSU法和形态学操作实现图像的自适应分割,用以修正特征点凸包,最终分割出动目标区域。与鲁棒的背景运动补偿后差分以及光流估计的对比实验表明,在目标运动速度较大、光照变化以及噪声情况下,本文方法均能够检测出运动目标,且在光照变化下的优势明显。     

基于Radon变换的运动模糊方向估计的改进方法

为了有效地进行运动模糊图像的复原处理,研究了模糊图像点扩散函数(PSF)的两个关键模糊参数——模糊方向(角度)和模糊尺度的估计,重点研究了运动模糊方向的精确估计。针对模糊图像频谱中十字亮线对方向估计的干扰问题,给出了基于Radon变换的运动模糊方向估计方法的改进方法。该方法根据频谱图像中心条纹尺度信息,自适应调节形态学腐蚀算子的执行次数,有效克服十字亮线对检测精度的干扰,最后利用Radon变换得到运动模糊方向的精确估计。试验结果验证了该方法的正确性和有效性。     

一种基于印刷图像处理技术的模糊图像复原方法

根据印刷图像处理技术的基本原理,针对印前的电子出版图像处理中存在的原稿图像运动模糊现象,介绍了约束最小二乘方滤波复原技术消除图像模糊方法.阐述了运动模糊图像的退化模型和复原模型,利用二值图的信息来估计模糊方向,通过对二值图的矩阵变换来估计模糊尺度,再利用约束最小二乘方滤波的方法复原图像.实验表明,该方法行之有效.     

质心识别及模糊判决方法在室内监控系统的应用

在有效提取特征参数后,结合具体的应用环境,提出一种模糊判决算法,依据运动物体的面积和运动物体的运动距离等特征参数,对目标进行识别报警判断,捕获并存储异常现象,最后发出报警信号.研究表明,通过异常识别及报警模糊判决方法可以使监控系统有效地消除日光灯频闪光线,部分运动阴影以及局部小扰动带来的区域干扰.     

Parametric blind deconvolution: a robust method for the simultaneous estimation of image and blur.

Blind-deconvolution microscopy, the simultaneous estimation of the specimen function and the point-spread function (PSF) of the microscope, is an underdetermined problem with nonunique solutions that are usually avoided by enforcing constraints on the specimen function and the PSF. We derived a maximum-likelihood-based method for blind deconvolution in which we assume a mathematical model for the PSF that depends on a small number of parameters (e.g., less than 20). The algorithm then estimates the unknown parameters together with the specimen function. The mathematical model ensures that all the constraints of the PSF are satisfied, and the maximum-likelihood approach ensures that the specimen is nonnegative. The method successfully estimates the PSF and removes out-of-focus blur. The PSF estimation is robust to aberrations in the PSF and to noise in the image.     

Application of the Hotelling and ideal observers to detection and localization of exoplanets

The ideal linear discriminant or Hotelling observer is widely used for detection tasks and image-quality assessment in medical imaging, but it has had little application in other imaging fields. We apply it to detection of planets outside of our solar system with long-exposure images obtained from ground-based or space-based telescopes. The statistical limitations in this problem include Poisson noise arising mainly from the host star, electronic noise in the image detector, randomness or uncertainty in the point-spread function (PSF) of the telescope, and possibly a random background. PSF randomness is reduced but not eliminated by the use of adaptive optics. We concentrate here on the effects of Poisson and electronic noise, but we also show how to extend the calculation to include a random PSF. For the case where the PSF is known exactly, we compare the Hotelling observer to other observers commonly used for planet detection; comparison is based on receiver operating characteristic (ROC) and localization ROC (LROC) curves.     

Iterative statistical approach to blind image deconvolution

Image deblurring has long been modeled as a deconvolution problem. In the literature, the point-spread function (PSF) is often assumed to be known exactly. However, in practical situations such as image acquisition in cameras, we may have incomplete knowledge of the PSF. This deblurring problem is referred to as blind deconvolution. We employ a statistical point of view of the data and use a modified maximum a posteriori approach to identify the most probable object and blur given the observed image. To facilitate computation we use an iterative method, which is an extension of the traditional expectation-maximization method, instead of direct optimization. We derive separate formulas for the updates of the estimates in each iteration to enhance the deconvolution results, which are based on the specific nature of our a priori knowledge available about the object and the blur.     

Analysis of depth profiling data obtained by confocal Raman microspectroscopy

The nominal depth resolution achieved in confocal Raman microscopy is on the order of a few micrometers. Often, however, the depth resolution is decreased by light refraction at the sample surface. The problem can be avoided with the use of an immersion objective and index matching oils. Through this intervention the instrument point-spread function (PSF) can be assumed to be independent of the depth of focus in the sample, and spatially invariant depth profiles can be acquired. In this work the instrument PSF was determined by measuring a depth profile of a thick uniform sample and calculating the first derivative of the depth profile curve. The first-derivative method was also used to determine sample thickness. Convolution with the PSF makes it possible to simulate the behavior of the instrument with different sample functions. It is also possible to use the instrument PSF to deconvolve depth-profiling data. Deconvolution reduces the blurring effect of the instrument and increases the depth resolution. Deconvolution can also be used in analysis of the sample surface position and in layer structure analysis. In this paper we show how the convolution integral can be used with the immersion sampling technique to determine the PSF and how the sample thickness can be determined.     

Trajectories in parallel optics

In our previous work we showed the ability to improve the optical system's matrix condition by optical design, thereby improving its robustness to noise. It was shown that by using singular value decomposition, a target point-spread function (PSF) matrix can be defined for an auxiliary optical system, which works parallel to the original system to achieve such an improvement. In this paper, after briefly introducing the all optics implementation of the auxiliary system, we show a method to decompose the target PSF matrix. This is done through a series of shifted responses of auxiliary optics (named trajectories), where a complicated hardware filter is replaced by postprocessing. This process manipulates the pixel confined PSF response of simple auxiliary optics, which in turn creates an auxiliary system with the required PSF matrix. This method is simulated on two space variant systems and reduces their system condition number from 18,598 to 197 and from 87,640 to 5.75, respectively. We perform a study of the latter result and show significant improvement in image restoration performance, in comparison to a system without auxiliary optics and to other previously suggested hybrid solutions. Image restoration results show that in a range of low signal-to-noise ratio values, the trajectories method gives a significant advantage over alternative approaches. A third space invariant study case is explored only briefly, and we present a significant improvement in the matrix condition number from 1.9160e+013 to 34,526.     

EEMD-ICA联合降噪的旋转机械故障信号检测方法

针对旋转机械前期故障信号微弱、易被噪声淹没、故障特征难以提取的问题,提出一种聚合经验模态分解（Ensemble Empirical Mode Decomposition,EEMD）和独立成分分析（Independent Component Analysis,ICA）相结合的故障特征提取方法。首先,运用EEMD理论将振动信号分解为一系列的固有模态函数（Intrinsic Mode Function,IMF）,然后根据相关系数和均方根准则选取含有原始信号多的IMF分量构造观测信号,引入虚拟噪声通道;最后,通过FastICA算法将噪声与故障特征信号进行分离,并对分离出的有用信号进行频谱分析,突显故障频率。通过仿真信号验证所提出方法的有效性,并将其应用于轴承的内外圈故障识别,与传统的EEMD-WTD降噪方法对比,结果表明：所提出的方法能提取出清晰微弱故障特征信号,对低频噪声的抑制效果明显优于EEMD-WTD方法。     

Anisoplanatic deconvolution of adaptive optics images

A modified method for maximum-likelihood deconvolution of astronomical adaptive optics images is presented. By parametrizing the anisoplanatic character of the point-spread function (PSF), a simultaneous optimization of the spatially variant PSF and the deconvolved image can be performed. In the ideal case of perfect information, it is shown that the algorithm is able to perfectly cancel the adverse effects of anisoplanatism down to the level of numerical precision. Exploring two different modes of deconvolution (using object bases of pixel values or stellar field parameters), we then quantify the performance of the algorithm in the presence of Poissonian noise for crowded and noncrowded stellar fields.     

MLEM deconvolution of protein X-ray diffraction images based on a multiple-PSF model

In this paper we analyze the degradation of protein X-ray diffraction images by diffuse light distortion (DLD). In order to correct the degradation, a new multiple point spread function (PSF) model is introduced and used to restore X-ray diffraction image data (XRD). Raw PSFs are collected from isolated spots in high-resolution areas on the diffraction patterns which represent the orientation of DLDs. An adaptive ridge regression (ARR) technique is used to remove noise from the raw PSF data. A target Gaussian function is used to model the raw PSFs. A maximum likelihood expectation maximization (MLEM) algorithm combined with a multi-PSF model is employed to restore high intensity, asymmetrical protein X-ray diffraction data. Experimental results using a single and multiple PSFs are presented and discussed. We show that using a multiple PSF model in the deconvolution algorithm improved the quality of the XRD and as a result the spot integration error (/spl chi//sup 2/) and corresponding electron density map are improved.     

Development of a novel starch-derived porous silica monolith for enhancing the dissolution rate of poorly water soluble drug

A novel starch-derived porous silica monolith (PSM) and porous starch foam (PSF) were developed as a carrier in order to improve the dissolution of lovastatin. PSM was prepared by the starch gel template method and PSF was prepared by the solvent exchange method. The morphology and structure of PSM and PSF were characterized by scanning electron microscopy (SEM) and nitrogen adsorption/desorption analysis. Lovastatin was loaded into PSM and PSF by immersion/solvent evaporation. Nano-pore spatial confinement effect on the drug dissolution was systematically studied by SEM, Fourier transform infrared spectroscopy (FTIR), thermogravametric analysis (TGA), x-ray powder diffraction (XRPD), differential scanning calorimetry (DSC), and in-vitro drug dissolution studies. Lovastatin adsorbed in PSM was amorphous and lovastatin absorbed on PSF was partially present as microcrystal in the pores of PSF and partially in crystalline form distributed on the surface of PSF. PSM and PSF allowed immediate release of lovastatin and enhanced the dissolution rate. These results provide important information about the mechanism of drug adsorption and release. Accordingly, PSM and PSF have a promising future as a vehicle for the oral delivery of poorly water soluble drugs. Moreover, the effect of PSM is better than that of PSF.     

Measurement of the point-spread function of a noisy imaging system

The averaged point-spread function (PSF) estimation of an image acquisition system is important for many computer vision applications, including edge detection and depth from defocus. The paper compares several mathematical models of the PSF and presents an improved measurement technique that enables subpixel estimation of 2D functions. New methods for noise suppression and uneven illumination modeling were incorporated. The PSF was computed from an ensemble of edge-spread function measurements. The generalized Gaussian was shown to be an 8 times better fit to the estimated PSF than the Gaussian and a 14 times better fit than the pillbox model.