Image Correlation with Geometric Distortion Part II: Effect on Local Accuracy

The effect of geometric distortion on the local accuracy of the image registration algorithms using cross correlation is presented. Using a probabilistic model describing images as homogeneous random patterns, expressions for the mean and covariance of the local error vector in terms of image and noise autocorrelation functions, geometric distortion, and reference image area are derived. The geometric distortions considered are those represented by an affine transformation of image coordinates. It is shown that for a fixed geometric distortion there is an image size (integration area) that minimizes the local error. The optimum area decreases with increasing geometric distortion.     

Optimum Windows for Image Registration

Two-dimensional cross-correlation techniques are applied to the problem of image registration under the assumption of small geometric distortion. Optimum window functions are derived for two performance measures of interest: peak-to-sidelobe ratio and mean-square registration error. The latter is examined in terms of the contribution caused by noise and the contribution caused by geometric distortion. A generalized Lagrange multiplier approach is used to derive approximate solutions assuming random images. The case of Gaussian autocorrelation functions is examined in detail. Results of applying the theoretically derived window functions to real data are presented, showing significant improvement in correlator performance.     

Optimum Filters for Image Registration

Two-dimensional cross-correlation techniques are applied to the problem of image registration under the assumption of small geometric distortion. Optimum filter functions for use with arbitrary window functions are derived for two performance measures of interest: peak-to-sidelobe ratio, and mean- square registration error; the latter is examined in terms of the contribution due to distortion and the contribution due to noise. A generalized Lagrange multiplier approach is used to derive at least approximate ate solutions to the case where the images are modeled as random processes and to the case where the images are assumed to be deterministic.     

一种局部自适应内窥镜图像降噪模型

在内窥镜图像处理中,如何消除图像中的噪声一直是个热点问题。由于图像二进小波变换在每次分解时不进行下抽样,所以其表示同小波级数相比是冗余的,且图像二进小波变换的部分系数扰动不会带来重构图像的严重失真。因此,在相同的误判概率下,二进小波变换的图像去噪效果会好于小波级数变换的图像去噪效果。基于这个思想,文章从二进小波理论入手,提出一种自适应二进小波去噪模型,简称ADWD。该方法利用图像信息、噪声信息与小波系数之间的关系,采用局部自适应的方法识别噪声像素,避免了直接确定噪声门限的困难。实验结果及分析表明该方法对Gaussian噪声和Pepper噪声均有较高的信噪比,且对图像的细节有较好的保持能力。     

复杂地形下基于三视约束的景象匹配方法

 景象匹配通过将飞行器实拍图像与机载的基准图像比较而进行导航。当飞行高度较低时,由地面大起伏引起的实时图几何变形会严重影响匹配性能,造成匹配精度下降甚至出现误匹配,这一问题严重制约了现有景象匹配系统的应用范围,比如无法应用于建筑林立的城镇地区和梯田、丘陵地带。本文首先推导了小视场内无几何变形的高空竖直透视投影模型;在此基础上,依据三视约束关系,把相邻两幅下视实时图像的特征赋给该透视模型所成的像,得到消除几何变形的特征模板,再和基准图进行匹配。因此,所提方法不仅能处理由飞行器姿态引起的实时图几何变形问题,而且适用于城镇等地面存在较大起伏的地形。理论分析和仿真实验表明,该算法取得了较好的结果,具有良好的环境适应能力,实用性较强。     

步进式序列虹膜图像质量评价

在虹膜识别系统中,质量较差的虹膜图像可能被系统拒识,导致身份识别或身份认证的失败.因此有必要在虹膜图像的采集端引入质量评价环节,从虹膜图像采集仪输出的视频序列中挑选出符合识别系统要求的虹膜图像.为了减少序列图像质量评价的时间,根据图像的性质,对输入的序列图像进行步进式质量评价,不同的阶段设置不同的门限,只有满足要求的图像才能进入下一阶段的评价,这样将大大减少进行全部步骤评价的图像数量,能有效缩短序列图像评价的时间,保证了选取图像的质量和速度.试验结果表明,该算法能够有效地选择符合质量要求的虹膜图像.     

改进的基于神经网络的卫星遥感图像去条带方法

由于焦平面阵列的非均匀性影响,线阵列推扫所成的遥感图像表现出规律的条带失真,严重地影响图像质量。本文介绍一种基于神经网络的条带消除方法,以及利用该方法对实际遥感图像校正的结果。     

Optimal Target t Designation Techniques

Studies of signal processing techniques are presented for achieving optimal autonomous acquisition of stationary targets located in a moderately intense nonhomogeneous radar ground clutter background. The process of target acquisition comprises three basic functions: 1) target detection, 2) target selection or designation, and 3) reacquiring the target for tracking. It is the second of these functions, i.e., autonomous designation of a target, that is the subject of this discussion. Theoretical analyses and predictions of the probability of correct target designation for two acquisition search methods are presented and compared with results achieved using real radar imagery.     

景象匹配中的几何透视校正和归一化互相关模板匹配

几何失真是使景象匹配性能下降的一个很重要的因素,基于参考图与失真实时图中四对对应点,本文采用透视变换方法,对失真实时图进行校正。对校正后的实时图,本文采用归一化互相关方法,进行模板匹配。仿真实验表明,这两种方法简单易行,校正与匹配结果精确。     

单脉冲成像算法性能分析

单脉冲技术通过比较单个脉冲多路回波信号的信息实现对目标角度位置的精确测量,广泛运用于跟踪雷达中。在雷达成像中引入单脉冲技术可以显著提高前视这一合成孔径雷达(SAR)与多普勒波束锐化(DBS)成像盲区雷达图像的清晰度。本文着重对单脉冲成像算法的成像效果分析方法进行研究。从单脉冲和差比的概率密度函数出发,提出了目标图像位置失真、分辨率以及图像信噪比3个对图像质量进行衡量的指标。分析了决定这3个指标的系统及外部环境参数,并给出了相应的计算方法。最终通过数值积分以及Monte-Carlo仿真实验对理论分析结果进行了验证。     

Predicting an upper bound on SAR ATR performance

We present a method for predicting a tight upper bound on performance of a vote-based approach for automatic target recognition (ATR) in synthetic aperture radar (SAR) images. In such an approach, each model target is represented by a set of SAR views, and both model and data views are represented by locations of scattering centers. The proposed method considers data distortion factors such as uncertainty, occlusion, and clutter, as well as model factors such as structural similarity. Firstly, we calculate a measure of the similarity between a given model view and each view in the model set, as a function of the relative transformation between them. Secondly we select a subset of possible erroneous hypotheses that correspond to peaks in similarity functions obtained in the first step. Thirdly, we determine an upper bound on the probability of correct recognition by computing the probability that every selected hypothesis gets less votes than those for the model view under consideration. The proposed method is validated using MSTAR public SAR data, which are obtained under different depression angles, configurations, and articulations     

基于Contourlet的SSIM图像融合质量评价方法

利用contourlet能有效表示图像的边缘和轮廓信息的特点,并将其引入到图像融合后的评价中,提出了一种基于Contourlet的结构相似度(SSIM)图像融合质量评价方法。以失真信道模拟融合过程,通过对标准参考图像与融合图像之间的结构相似度来实现对融合方法、融合规则性能优劣的评判。实验表明该方法与主观评价方法有很好的一致性,且与经典的SSIM算法相比具有更好的区分度。     

图像匹配技术在航空电子系统中的应用

 本文概述了匹配定位原理、方法及相似度度量、快速搜索、正确匹配概率、预处理、几何失真及其改善等问题,还指出了特征匹配及匹配寻的跟踪等两个值得重视的发展趋势。最后给出了相关匹配技术在组合导航及导引头中应用的结构组成。     

一种鲁棒的红外与可见光多级景象匹配算法

针对基于特征的景象匹配方法对图像噪声和局部形变适应性差以及对特征质量依赖性强等问题,从提高图像特征描述能力入手,将具有局部光照和对比度不变性的相位一致性变换引入到红外与可见光景象匹配的特征表征中,以获得对噪声和局部形变较强的适应性。在此基础上,首先采用抗旋转的圆投影实现图像粗略匹配,然后基于Zernike矩推导出图像的互相关匹配重构函数并用来剔除错误匹配点,最后基于曲面拟合方法获得亚像素定位,从而实现图像精确与鲁棒匹配。仿真实验验证了所提算法的有效性。     

基于组合跟踪的景象匹配分析评估方法

景象匹配飞行试验中,图像序列中存在的剧烈尺度变化及多种外界因素影响导致的几何变形等成像质量变化,增加了景象匹配全流程自动评估的难度.为了解决这个问题,提出1种基于组合跟踪的景象匹配图像全流程评估方法.首先,基于序列图关键帧选取原则,提出序列图关键帧匹配方法;然后将该方法与多种目标跟踪算法进行对比分析;再根据对比分析的结...     

基于不变特征和映射抑制的航拍视频图像配准

针对航拍视频图像在光学变换和几何变换下特征匹配容易产生误匹配,以及匹配精度不一致等问题,提出一种结合不变特征和映射抑制的航拍视频配准算法。首先,利用Harris检测器在尺度变化下的局部区域内选择出最稳定角点,并利用分区检测方法提取出分布均匀的角点作为待配准点。然后,计算以特征点为中心邻域窗口的描述符,通过比较两幅待配准图像特征点邻域描述符的欧式距离,提取出初始匹配点对。对匹配对点集建立D-三角网图,利用映射抑制方法提取出精确度最高的少数匹配点,并对模型参数进行估计,完成视频帧间的配准。大量实验表明,所提方法对航拍视频图像配准具有较强的适应性且配准精度高。     

光照处理中改进的Gamma矫正方法

在图像处理中，光照始终是影响图像质量的重要因素。为了有效弱化光照对图像质量的影响，利用非线性函数叠加的方法构造了一个合理的Gamma值变化曲线，使Gamma矫正方法充分适应了图像中光照变化的实际情况，并有效防止了Gamma矫正可能产生的图像失真。实验证明该方法在改善图像光照条件、提高图像质量方面具有良好的性能。     

无人机载多传感器图像融合评述

为了能为作战指挥系统提供清晰的局部战场信息,提高对局部战场低可观测目标的检测概率、定位精度及识别概率,迫切需要对无人机载SAR、可见光传感器、红外探测器等图像信息及其他非图像信息融合处理。提出了无人机载多传感器图像融合技术需要研究的内容,如: 图像融合新方法研究;无人机载SAR图像的非平稳性处理;基于图像融合目标检测和处理技术; 无人机载图像和非图像信息的融合问题;无人机载多传感器图像融合的实现及评估;多无人机载合成孔径雷达的协同成像;用图像融合的方法实现对运动目标检测等。分析了所提出研究内容的可行性,剖析了其中的关键技术,拟定了可能的技术路线。     

Target localization based on cross-view matching between UAV and satellite

Matching remote sensing images taken by an unmanned aerial vehicle (UAV) with satellite remote sensing images with geolocation information. Thus, the specific geographic location of the target object captured by the UAV is determined. Its main challenge is the considerable differences in the visual content of remote sensing images acquired by satellites and UAVs, such as dramatic changes in viewpoint, unknown orientations, etc. Much of the previous work has focused on image matching of homologous data. To overcome the difficulties caused by the difference between these two data modes and maintain robustness in visual positioning, a quality-aware template matching method based on scale-adaptive deep convolutional features is proposed by deeply mining their common features. The template size feature map and the reference image feature map are first obtained. The two feature maps obtained are used to measure the similarity. Finally, a heat map representing the probability of matching is generated to determine the best match in the reference image. The method is applied to the latest UAV-based geolocation dataset (University-1652 dataset) and the real-scene campus data we collected with UAVs. The experimental results demonstrate the effectiveness and superiority of the method.     

An Analytical Expression for Correlation Accuracy

An analytical closed-form expression for the uncertainty in aligning two images is derived as a function of image area, data fidelity, scene correlation length, limiting sensor resolution length, and degree of geometrical distortion between the two images.