核反应堆燃料组件水下热湍流图像复原

核电站在停堆复查期限,需采用视觉手段对燃料组件进行核心验证,但是燃料组件的余热会导致水下热湍流,严重影像图像质量。为获取热湍流下核燃料组件的清晰图像,提出了一种去除热湍流干扰的燃料组件图像复原算法,算法主要包括时域高通滤波和空间解卷积滤波。首先构建信噪比时域响应高通滤波器取得以稳定但模糊的视频,接着利用基于经验模型的点扩散函数,对水下核燃料组件模型图像进行空间解卷积滤波,并计算复原图像的质量参数值,确定合适的经验模型。实验结果显示,利用离焦模糊模型解卷积复原的燃料组件图像质量最好,表明该算法能够去除湍流对图像产生的干扰影响。     

针对多重退化因素的印刷图像复原方法

目的对于由多种因素所导致的印刷图像退化问题,文中提出一种针对椒盐噪声、高斯噪声和模糊退化等多重退化因素的图像复原方法。方法首先针对印刷图像椒盐噪声密度不高的特点,设计一种基于灰度范围准则和局部差别准则的椒盐噪声二级检测和滤除方法,并通过评价实验得出合适的阈值参数设置。在去除高斯噪声和图像模糊的过程中,利用边缘保持平滑滤波的原理和特性,将双边滤波器和引导滤波器应用于图像复原中,又在此基础上设计和应用图像细节增强的二次引导滤波器。结果在椒盐噪声去除方面,新方法对大部分图像都能取得较好的复原效果,尤其对细微边缘不多的图像效果最佳,复原后的PSNR值能达到40以上。二次引导滤波器对高斯噪声和图像模糊的复原效果最好。结论通过对不同图像复原方法的效果进行评价和分析,验证了文中方法的性能,为今后图像复原技术的应用提供了指导。     

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.     

图像插值分析的交互式分层复原

为了复原模糊图像以得到可辨识的目标,分析相机与目标高速相对运动时产生的两种模糊性质,针对其退化函数难以确定的特点,提出一种基于图像插值分析的交互式分层复原方法,该方法首先对图像进行插值,以提高对低分辨率图像退化机理的分析能力,并在分析的基础上进行图像预处理.然后在图像复原的优化准则下,交互地选择出降质模型的最优参数组合,以得到两个降质函数,最后对原图像实施分层复原.对仿真图像进行实验,基本上恢复了目标图像的特征,证明该方法复原效果比较理想.     

变量分裂机制耦合非循环模糊模型的图像复原

目的针对当前非二次正则化图像复原算法在图像边界的周期拓展难度较大,导致复原图像的边界存在振铃效应,算法的通用性削弱,且时耗严重的问题,提出变量分裂机制耦合非循环卷积模型的抗失真图像快速复原算法。方法引入掩模算子,设计非循环模糊模型,显著消除复原图像的振铃效应;改进数据保真项,构造了最小成本化函数;定义二次辅助变量,嵌入变量分裂策略,设计基于变量分裂的乘数交替方向算法,对非循环模糊模型中的循环与掩模矩阵进行解耦,降低算法复杂度;构造增广拉格朗日函数,耦合交替最小机制与成本函数,以单步封闭式更新估算图像,快速完成图像重构。结果仿真结果显示:与当前复原算法相比,提出算法的失真度最小,且收敛速度更快。结论提出算法能够快速复原多种类型的退化图像,有效消除了复原图像的振铃效应。     

基于零相位滤波的聚焦形貌恢复技术

在聚焦形貌恢复技术中,为获取更好的恢复精度,提出了一种高精度的聚焦形貌恢复算法,该方法用零相位滤波器对窗口序列图像的评价函数值进行滤波,在消除干扰的同时,保持各空间数据点的位置不变.设计了基于切比雪夫Ⅱ滤波器的零相位滤波器;利用二次曲线的最小二乘拟合峰值位置作为窗口序列的聚焦位置,进一步提高了峰值定位精度;采用三次曲面...     

基于灰度梯度的数字图像评价函数

提出用图像灰度梯度向量模方和、Roberts梯度和与拉普拉斯(8邻域微分)算子和作为数字图像的评价函数。建立了上述几种评价函数的数学模型,针对离焦模糊和运动模糊等情况分别给出了实验结果,并进行了比较。它们都有无偏性好、单峰性强、灵敏度高等特点,可用于离焦模糊和运动模糊情况的评价,且灰度梯度向量模方和最理想。     

潜望镜图像序列的时域Kalman滤波

根据潜望镜图像序列的噪声模型，提出了用非线性模糊滤波器和自适应Kalman在空域和时域对其进行滤波的方法。     

基于离焦状态模糊显微图像反馈的微操作方法

提出了一种基于离焦状态模糊显微图像反馈的微操作方法,此方法关键在于提取离焦状态显微图像的三维位置信息.给出了基于系统辨识的显微图像深度信息提取方法和扫描线算法,分别用于提取离焦状态显微图像的深度信息和平面位置信息,上述方法在精度和效率两方面均达到在线应用水平.进一步,将上述方法应用于微操作机器人系统,成功完成了离焦状态双针互插实验.这样,微操作在聚焦状态和离焦状态下均可进行,也就将微操作机器人的工作空间在Z方向(光轴方向)进行了拓展,实验条件下,从聚焦区内约2μm范围扩展到聚焦带上下70μm.     

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。     

General restoration filter for vibrated-image restoration

Mechanical vibrations are often the principal cause of image degradation. Low temporal-frequency mechanical vibrations involve random image degradation that depends on the instant of exposure. Exact restoration requires the calculation of a specific filter unique to each vibrated image. To calculate the restoration filter for each image, one needs the specific optical transfer function unique to the motion in the image. Therefore the instant of exposure and the motion function have to be measured or estimated by some other means. We develop a restoration filter for individual images blurred randomly by low-frequency mechanical vibrations. The filter is independent of the instant of exposure. The filter is designed to give its best performance averaged over a complete ensemble of vibrated images. Although when applying the new filter to any vibrated image the restoration achieved is slightly poorer than that achieved with an exact filter unique to the specific motion function, the new filter has the advantage of simplicity.     

Adaptive deblurring of noisy images

We propose a practical sensor deblurring filtering method for images that are contaminated with noise. A sensor blurring function is usually modeled via a Gaussian-like function having a bell shape. The straightforward inverse function results in the magnification of noise at high frequencies. To address this issue, we apply a special spectral window to the inverse blurring function. This special window is called the power window, which is a Fourier-based smoothing window that preserves most of the spatial frequency components in the passband and attenuates quickly at the transition band. The power window is differentiable at the transition point, which gives a desired smooth property and limits the ripple effect. Utilizing the properties of the power window, we design the deblurring filter adaptively by estimating the energy of the signal and the noise of the image to determine the passband and the transition band of the filter. The deblurring filter design criteria are (a) the filter magnitude is less than 1 at the frequencies where the noise is stronger than the desired signal (the transition band), and (b) the filter magnitude is greater than 1 at the other frequencies (the passband). Therefore the adaptively designed deblurring filter is able to deblur the image by a desired amount based on the estimated or known blurring function while suppressing the noise in the output image. The deblurring filter performance is demonstrated by a human perception experiment in which 10 observers are to identify 12 military targets with 12 aspect angles. The results of comparing target identification probabilities with blurred and deblurred images and adding two levels of noise to blurred and deblurred noisy images are reported.     

Defocus deblurring for catadioptric omnidirectional imaging based on spatially invariant point spread function

The problem of catadioptric omnidirectional imaging defocus blur, which is caused by lens aperture and mirror curvature, becomes more severe when high resolution sensors and large apertures are applied. In order to overcome this problem, a novel method based on computational photography is proposed. Firstly, the defocus blur of catadioptric omnidirectional imaging is analyzed to calculate the point spread function for different scene points. Then, the defocus blur kernel of omnidirectional image is confirmed to be spatially invariant when rotating the focus ring of camera lens during an image’s integration time. Lastly, the deconvolution algorithm using prior sparse derivatives is applied to obtain all-focused/sharp omnidirectional images. Experimental results demonstrate that the proposed method is effective for omnidirectional image deblurring and can be applied to most existing catadioptric omnidirectional imaging systems.     

Real-time image deblurring by optoelectronic hybrid processing

An efficient approach is presented to restore a motion-blurred image in real time by optoelectronic hybrid processing, by which an image motion vector can be effectively detected and an accurate point spread function is constructed rapidly. A simple Wiener filter algorithm is employed to restore the blurred image. It greatly alleviates the complexity of the restoration algorithm. The proposed approach can apply to arbitrary motion-blurred image restoration. An optoelectronic hybrid joint transform correlation is constructed to verify the efficiency. The experimental results show that the proposed method has distinct advantages of preferable effect and good real time.     

Performance of focus measures in the presence of nondefocus aberrations

The purposes of the study were to compare the performance of ten representative focus measures in the presence of nondefocus aberrations and to evaluate their applicability to the eye. For fixed amounts of nondefocus aberrations, the amount of defocus was changed to generate a series of blurred images from which focus measure curves were derived. In the presence of small amounts of nondefocus aberrations, all focus measures showed unimodal and monotonic behavior, although there were large differences in their sensitivity to defocus and effective ranges. There were breakdowns in monotonicity and unimodality for some focus measures when applied to data from human eyes, while other focus measures could detect the shift in the best-focus plane in the blurred image series resulting from spherical aberration.     

Holographic Image Restoration by Using an Unconstrained Single Deblurring Filter

The unconstrained single deblurring filter for coherent optical restoration of blurred image is produced in a modified Rayleigh interferometer with blurred point spread function (PSF) h(x, y) and doubly blurred PSF h(x, y)=h(x, y)?h(x, y),? denoting convolution. The linear-motion blurred images and the defocusing blurred images are corrected with the present holographic filter, and it is shown that the restored images are significantly improved.     

Quantitative analysis of error bounds in the recovery of depth from defocused images

Depth from defocus involves estimating the relative blur between a pair of defocused images of a scene captured with different lens settings. When a priori information about the scene is available, it is possible to estimate the depth even from a single image. However, experimental studies indicate that the depth estimate improves with multiple observations. We provide a mathematical underpinning to this evidence by deriving and comparing the theoretical bounds for the error in the estimate of blur corresponding to the case of a single image and for a pair of defocused images. A new theorem is proposed that proves that the Cramér-Rao bound on the variance of the error in the estimate of blur decreases with an increase in the number of observations. The difference in the bounds turns out to be a function of the relative blurring between the observations. Hence one can indeed get better estimates of depth from multiple defocused images compared with those using only a single image, provided that these images are differently blurred. Results on synthetic as well as real data are given to further validate the claim.     

Depth-based computational photography

A depth-based computational photography model is proposed for all-in-focus image capture. A decomposition function, a defocus matrix, and a depth matrix are introduced to construct the photography model. The original image acquired from a camera can be decomposed into several sub-images on the basis of depth information. The defocus matrix can be deduced from the depth matrix according to the sensor defocus geometry for a thin lens model. And the depth matrix is reconstructed using the axial binocular stereo vision algorithm. This photography model adopts an energy functional minimization method to acquire the sharpest image pieces separately. The implementation of the photography method is described in detail. Experimental results for an actual scene demonstrate that our model is effective.     

Unconstrained single deblurring filter made from blurred PSF and doubly blurred PSF

The theory of an unconstrained single deblurring filter is developed considering the effect of the higher-order terms. It is based on the interference pattern of the Fourier transform of the blurred point spread function (PSF) h and the doubly blurred PSF hD = h * h, * being the convolution symbol. Deblurring results of transverse sinusoidal motion blurred images obtained by using the present theory and the filter are presented.     

Off-axis Analysis of the Strehl Ratio Using the Wigner Distribution Function

Abstract

An approach based on the Wigner distribution function (WDF) is used for analysing the Strehl ratio behaviour for varying focus errors and object source locations. A relationship is found between the WDF of the pupil function and the WDF of the light field in the Fresnel region about the image plane. Tolerance criteria to defocus can be specified in terms of the ‘amplitude’ of the parageometrical rays that intersect a tilted optical axis defined by the locations of the object and image points.