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。     

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.     

基于频域方法的运动模糊方向识别

本文通过深入研究匀速直线运动模糊图像的点扩展函数,提出一种识别运动模糊图像模糊方向的频域方法.首先对模糊图像的频谱图出现平行暗条纹的原因进行分析.从理论证明和实验说明指出暗条纹的方向与模糊方向垂直的特性,并采用RADON变换方法检测这些暗条纹.最后,给出了运动模糊方向精确的数值估算方法.实验结果表明,该算法可以准确地识别图像运动模糊方向,绝对误差不超过2°.     

匀速直线运动产生的图像模糊及复原研究

在拍照的曝光期间,由于景物和照相机之间的相对运动,往往造成图像的模糊,致使照片效果差无法使用。另外,图像在获取和传输过程中,往往产生大量随机噪声。运动模糊和噪声使图像质量退化,对进一步的边缘检测、图像分割、特征提取及模式识别带来不便。本文将对匀速直线运动的情况下产生的图像模糊以及复原进行研究。     

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.     

运动模糊图像恢复在汽车同步环检测中的应用

同步环是汽车变速箱的关键零件，同步环漏装会产生传动轴打齿现象，严重影响汽车的安全性能和使用寿命。采用机器视觉技术对同步环装配与否进行检测，是一种高效准确的在线检测方法。由于在生产线上水平运动的同步环和静止的摄像机之间存在相对运动，因此摄像机采集的图像模糊不清。针对运动模糊图像的复原算法进行了研究，分析了其退化的原因，建立了图像复原的数学模型，并根据图像复原的数学模型对运动模糊图像进行了图像复原处理，为后续图像处理做好了前期准备，并取得了令人满意的效果。     

流动颗粒显微图像测量技术研究

针对显微环境下流动颗粒测量的需要,构建了测量平台,研制了基于MEMS工艺的测量器件。在分析流动颗粒显微图像特征的基础上,提出了基于颗粒运动特性的图像测量流程,其中提出了基于基准桢差分的目标提取算法,解决了流动颗粒目标分割的问题,根据图像运动模糊的退化模型,研究了图像模糊恢复的问题。最后以润滑油中的磨损颗粒为例,验证了该文的分析方法对流动颗粒检测的有效性。     

基于模糊融合的红外弱小目标快速检测算法

提出了一种基于模糊融合的红外弱小目标快速检测新算法。算法以差分图像为基础，根据差分图像的噪声特性引入衡量像素点灰度变化程度的隶属度函数，将经过隶属度函数“模糊化”的数帧差分图像进行模糊“与”操作实现融合，按照规则对融合后的图像进行两次模糊推理，实现了弱小目标的检测。仿真实验结果表明，该算法保留了差分法良好的实时性，克服了确定检测阈值难和“硬”判决带来检测概率低的缺点，能快速有效地检测出低信噪比红外图像序列中的像素个数不小于4的弱小运动目标。     

基于维纳滤波模糊图像复原算法的改进

简述维纳滤波复原算法原理以及维纳滤波的改进算法,通过仿真实验分别利用维纳滤波算法及其改进算法从不同角度对运动模糊图像进行复原,并对结果进行比较分析,事实证明维纳滤波改进算法能有效的消除图像复原中的振铃效应,达到比较满意的复原效果。     

基于序列图像中二次曲线对应的纯旋转运动参数估计算法

采用特征对应的视觉运动分析方法，建立了基于二次曲线的刚体纯旋转运动方程，提出了求解刚体纯旋转运动参数的算法。在刚体纯旋转运动分析中，最少利用前后两幅图像中7对匹配二次曲线，就可用线性方程组求解刚体旋转运动参数，线性算法的提出使旋转运动参数的求解精度得到很大提高。最后利用合成数据和真实图像对纯旋转运动估计算法的精度和有效性进行了验证，表明该算法能准确、可靠地估计旋转运动参数。     

Motion-distorted composite-frame restoration

Many imaging systems produce pictures by the superimposition of two fields of frames of interlaced sequences. Pictures obtained in this way, which are termed composite frames, are severely degraded if relative motion between the camera and the scene occurs. In the presence of motion the composite frame is affected by two types of distortion: the edge staircase effect that is due to the fact that objects appear at different positions in successive fields and motion blur that is due to scene motion during each field exposure. Motion-deinterlacing methods previously proposed to recover the staircase effect neglect motion blur. However, motion blur may be significant, especially in systems designed for low-intensity radiometric imaging that use long exposures or even in short-exposure systems that happen to be in moving vehicles such as tanks, planes, ships, etc. We introduce an algorithm for the restoration of the two types of distortion in a composite frame degraded by linear uniform motion.     

基于APEX算法改进的图像复原算法

在高斯类点扩函数退化图像复原的研究中,提出了一种基于降晰图像频谱特征改进的APEX图像复原算法.该算法采用APEX算法的基本原理,根据图像频谱信息特征,对点扩散函数(PSF)估计过程进行了改进,采用加权最小二乘算法拟合出降晰图像频谱主方向,采用图像频谱主方向上的数据进行PSF估计,以利用更多的有效数据,从而减少PSF的估计误差.针对模拟和实际采集的降晰图像进行实验,采用主观视觉和峰值信噪比进行评价.实验结果表明,改进的算法较使用非主频谱方向上的频谱数据的复原算法在复原效果上有一定的提高.     

Enhanced-resolution image restoration from a sequence of low-frequency vibrated images by use of convex projections

An algorithm to increase the spatial resolution of digital video sequences captured with a camera that is subject to mechanical vibration is developed. The blur caused by vibration of the camera is often the primary cause for image degradation. We address the degradation caused by low-frequency vibrations (vibrations for which the exposure time is less than the vibration period). The blur caused by low-frequency vibrations differs from other types by having a random shape and displacement. The different displacement of each frame makes the approach used in superresolution (SR) algorithms suitable for resolution enhancement. However, SR algorithms that were developed for general types of blur should be adapted to the specific characteristics of low-frequency vibration blur. We use the method of projection onto convex sets together with a motion estimation method specially adapted to low-frequency vibration blur characteristics. We also show that the random blur characterizing low-frequency vibration requires selection of the frames prior to processing. The restoration performance as well as the frame selection criteria is dependent mainly on the motion estimation precision.     

Recognition of motion-blurred images by use of the method of moments

Image motion causes a blur that changes features of objects and therefore complicates the task of automatic recognition. In this work we develop two recognition methods for motion-blurred images. For the first method we assume that the motion function and direction during the exposure are given. We develop the relation between the blurred-image moments and the original-image moments based on the motion function only. The recognition is carried out by comparing the moments of the restored image against the moments of the image database. In the second method the motion function is not known. In this case image moments that are invariant with respect to the motion blur are identified, and only these moments are used for recognition. The advantage of the suggested methods is that no time-consuming image restoration is required prior to recognition.     

基于超拉普拉斯先验与核谱特性噪声图像盲去模糊

现有大部分盲图像去模糊方法对噪声敏感,即使少量的噪声可大大降低恢复图像的质量．考虑到模糊图像中同时隐含有清晰图像信息和模糊核信息,我们同时利用卷积核谱特性先验和清晰图像梯度域超拉普拉斯先验联合建立含噪图像盲去模糊模型,较单独使用卷积核先验与清晰图像先验建模更合理,也能获得更精确的估计图像．本文借助于Hessian矩阵,利用模糊图像及卷积核联合生成先验子,而非单独的估计图像先验子,建立优化模型．求解模型时,通过迭代策略交替细化模糊核和清晰图像．在清晰图像恢复阶段,因存在超拉普拉斯先验项,提出用变量分离法计算清晰图像．清晰图像采用快速傅里叶变换及封闭阈值公式求解,以提高优化速度．实验结果表明：与其他方法相比,本文方法能获得更鲁棒的模糊核和更精确的清晰图像,且收敛速度更快．     

基于深度强化学习的图像去模糊方法

目的为了有效地去除多种图像模糊,提高图像质量,提出基于深度强化学习的图像去模糊方法。方法选用GoPro与DIV2K这2个数据集进行实验,以峰值信噪比(PSNR)和结构相似性(SSIM)为客观评价指标。通过卷积神经网络获得模糊图像的高维特征,利用深度强化学习结合多种CNN去模糊工具建立去模糊框架,将峰值信噪比(PSNR)作为训练奖励评价函数,来选择最优修复策略,逐步对模糊图像进行修复。结果通过训练与测试,与现有的主流算法相比,文中方法有着更好的主观视觉效果,且PSNR值与SSIM值都有更好的表现。结论实验结果表明,文中方法能有效地解决图像的高斯模糊和运动模糊等问题,并取得了良好的视觉效果,在图像去模糊领域具有一定的参考价值。     

Restoration of images captured by a staggered time delay and integration camera in the presence of mechanical vibrations

Staggered time delay and integration (TDI) scanning image acquisition systems are usually employed in low signal-to-noise situations such as thermal imaging. Analysis and restoration of images acquired by thermal staggered TDI sensors in the presence of mechanical vibrations that may cause space-variant image distortions (severe geometric warps and blur) are studied. The relative motion at each location in the degraded image is identified from the image when a differential technique is used. This information is then used to reconstruct the image by a technique of projection onto convex sets. The main novelty is the implementation of such methods to scanned images (columnwise). Restorations are performed with simulated and real mechanically degraded thermal images.