基于相位相关的小波域图像配准方法研究

小波变换是一种多尺度信号分析方法，近几年在图像处理领域受到广泛关注，它克服了傅立叶变换的固定分辨率的弱点，既可分析信号概貌，又可分析信号的细节。相位相关是一种频率域的图像配准参数估计方法，是利用傅立叶变换的平移、旋转等特性进行参数估计的。在研究多尺度小波分析和相位相关理论的基础上，提出基于小波系数的像素级相位相关图像配准方法：首先对待配准图像进行小波分解，获得低频小波系数后，再对小波系数应用相位相关进行配准参数估计。实验结果表明了该方法的可行性和有效性。     

快速区域互相关InSAR图像配准方法

在分析复图像干涉相位对快速互相关算法配准精度影响的基础上,提出了一种快速区域互相关InSAR图像配准方法。该方法对图像的幅度谱进行区域相关操作,获得干涉相位空间角频率的粗估计,并对主图像进行干涉相位补偿。该算法通过上述步骤消除干涉相位变化对算法的影响实现了高精度配准。在仿真实验和实测实验中,通过与传统的快速区域互相关算法及最大频谱法进行对比,验证了本文算法的稳健性与有效性。     

一种基于权重的相位相关峰值拟合方法

在基于相位相关图像配准方法中,受傅里叶逆变换旁瓣峰值、累积误差的影响,传统相位相关方法常不能稳定、高效地对配准参数进行高精度估计。为此,提出一种基于权重的相位相关峰值拟合方法:构建一个反比例函数,证明其满足Kronecker delta函数平移性质;构建一种基于权重反比例函数的峰值拟合方法,通过三个点实现图像配准参数的高效、高精度估计。实验结果显示,该算法的稳定性、时效性得到有效增强,具有一定的现实意义。     

光纤耦合的亚像元图像超分辨率重建

光纤耦合图像之间存在亚像元级位移问题,为此,提出一种基于图像互补信息的超分辨率重建算法。采用传统相位相关法进行整像元级配准,通过拟合多个谱峰值进行亚像元级配准,利用待插值点周围的4条B样条曲线实现图像插值融合。实验结果表明,该算法能提高图像空间分辨率,且复杂度较低、重建图像质量较好。     

利用稠密结构特征的航空影像与LiDAR数据配准北大核心CSCD

针对航空影像与激光雷达点云数据间存在显著的几何畸变和辐射差异导致难以精确配准的问题,提出了一种基于几何约束和稠密结构特征的自动配准方法。该方法包括粗配准和精配准两个阶段。粗配准通过基于分块FAST算子的特征点提取和局部几何校正两个步骤,消除影像间明显的尺度和旋转差异。在精配准阶段,首先构建了一个结合一阶和二阶梯度信息的新描述符(second-and first-order channel features of orientated gradients,S-CFOG)来提取影像间稠密的结构特征,然后在频率域采用三维相位相关作为相似性度量进行同名点匹配。最后,利用同名点对外方位元素进行精化,实现对这两类数据的精配准。通过两组不同覆盖场景的数据进行实验,结果表明,该方法可达到1~2个像素的配准精度。     

基于Harris角点量与相位相关的亚像素级图像配准方法*

相位相关法是一种常用的图像配准方法,而直接基于傅里叶变换的快速图像配准方法（ＦＤＦＡ）使之具有亚像素级的配准精度,它需要根据图像内容细心选择参与相位拟合的频率分量,并在图像上施加窗口以抑制频域的图像边界效应,从而提高其偏移量估计精度。从边界效应抑制的角度出发,利用Harris角点量代替原始图像进行相位相关计算,与传统的对原始图像加窗的方法不同,该方法既抑制了边界效应,又避免引入截断误差,同时只用对其第一项频率分量进行拟合,而不需要进行频率项的选择,使相位拟合过程更简单。提取图像的Harris角点并选择其中     

基于相位相关的图像配准算法

提出了一种新的基于相位相关的图像配准方法：首先在参考图像中心处截取一个小区域图像，然后在待配准图像中寻找一个同样大小的区域，使得在对数极坐标表示下，这两个小区域图像的互功率谱经傅里叶反变换后是一个二维脉冲信号。所要寻找的小区域中心点在待配准图像中的位置即为所要估计的平移参数，二维脉冲信号中脉冲的位置与缩放因子和旋转角度有关，由此而得到图像配准参数。实验结果表明该方法具有很好的配准效果。     

局部相位相关用于图像亚像素级配准技术研究

提出了一种基于局部相位相关的高效和鲁棒的亚像素级图像配准方法。通过传统的相位相关算法估计出初始平移参数后,在初始位置的引导下对互相关功率谱进行上采样矩阵Fourier变换,实现了图像局部相位相关,得到图像间亚像素级平移参数。实验结果表明,算法配准精度较高,且对随机噪声和光照变化具有较强的鲁棒性。     

一种基于多峰拟合的相位相关扩展方法

鉴于相位相关配准法具有较强的抗光照干扰能力,提出一种扩展的相位相关法,根据相位相关函数峰值的分布特性,利用多峰拟合的方法获得亚像素级的配准结果。实验结果表明,该方法克服了传统相位相关法只能达到整像素配准精度的缺点,并具有较强的鲁棒性和较高的效率。     

基于相位相关的指纹拼接方法研究

基于指纹图像的旋转、平移、距离等不变特性,从增加指纹图像的有效特征点出发,提出了指纹拼接方法。该方法分为三步：第一步,提出基于中值滤波及局部属性的归一化预处理算法,去除图像噪声和影响相位相关度准确性的频率信息;第二步,图像进行伪极坐标快速傅立叶变换,根据旋转和平移的不变性,利用相位相关对图像进行快速配准;第三步,对配准的指纹图像完成平滑拼接。大量的实验结果证明本方法可以增加有效的指纹图像特征点数量,改善比对效果,计算复杂度较低,可以实现指纹图像的快速拼接。     

Image registration by matching relational structures

We present a method of image registration where we use relational structures as representations of images and we register two images by finding a match between the two relational structures. It is better than traditional cross-correlation like techniques in several aspects: it is insensitive to scaling, rotation, gray level modification and small relative motion between the two images. The matching process makes use of star structures, which have several advantages over conventional node structures.     

基于形状和灰度信息的断层图像二次配准算法

将一种新的基于形状信息和灰度信息的二次配准方法引入CT-MRI配准过程,首先通过力学分解的原理描述了两幅待配准图像的轮廓,并利用该轮廓对两幅图像进行粗配准,通过该方法将两幅图像的配准误差限定到一个较小范围内;继而利用最大互信息的方法继续对经过粗配准的两幅图像进行二次配准,最终得到精度更高的配准效果。仿真结果表明,由于该算法结合了轮廓比对方法的高效性和最大互信息方法的精确性,因此与其它配准算法相比在保证了配准精度的同时大大缩短了配准时间。最后该算法被成功地应用到了准备进行开颅手术的病人的CT-MRI图像配准上。     

多模态硬币图像单应性矩阵预测

对不同成像条件下拍摄的硬币图像进行配准是硬币表面缺陷算法的前置任务。然而,基于互信息的传统多模态配准方法速度慢、精度低,现有的通过基于深度学习的单应性矩阵预测方法实现的图像配准只适用于单模态的任务。为此,提出一种基于深度学习的适用于多模态硬币图像的单应性矩阵预测方法,进而使用预测的单应性矩阵完成图像配准。首先,使用单应性矩阵预测层预测输入图像对间的单应性矩阵,使用单应性矩阵对待配准图像进行透视变换;然后,使用图像转换层将透视变换后的待配准图像和目标图像映射到同一域,图像转换层可在推理时去除从而减少推理时间;最后,计算同一域下的图像间的损失,并进行训练。实验表明,该方法在测试集上的平均距离误差为 3.417 像素,相较于基于互信息的传统多模态配准方法 5.575 像素的平均距离误差降低 38.71%。且配准单对图像耗时为 17.74 ms,远低于基于互信息的传统多模态配准方法的 6 368.49 ms。     

一种结合互信息和模板匹配的配准方法

传统的互信息法直接对大图进行互信息配准,容易出现局部极值并产生误匹配现象,针对该问题,提出一种将互信息与模板匹配相结合的配准方法,采用互信息为相似度准则进行图像的模板匹配,得到候选的匹配子图,通过待配准大图其余部分与模板和子图的空间关系,得到配准后的大图,分别计算按照各个候选子图配准情况下的待配准图的互信息,得到最大的互信息所对应的子图,确定最终的配准结果。实验结果表明,该方法在对灰度差较大的图像进行配准时,能够获得良好的效果。     

Wavelet-based image registration technique for high-resolution remote sensing images

Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints at different times or by different sensors. It is an important image processing procedure in remote sensing and has been studied by remote sensing image processing professionals for several decades. Nevertheless, it is still difficult to find an accurate, robust, and automatic image registration method, and most existing image registration methods are designed for a particular application. High-resolution remote sensing images have made it more convenient for professionals to study the Earth; however, they also create new challenges when traditional processing methods are used. In terms of image registration, a number of problems exist in the registration of high-resolution images: (1) the increased relief displacements, introduced by increasing the spatial resolution and lowering the altitude of the sensors, cause obvious geometric distortion in local areas where elevation variation exists; (2) precisely locating control points in high-resolution images is not as simple as in moderate-resolution images; (3) a large number of control points are required for a precise registration, which is a tedious and time-consuming process; and (4) high data volume often affects the processing speed in the image registration. Thus, the demand for an image registration approach that can reduce the above problems is growing. This study proposes a new image registration technique, which is based on the combination of feature-based matching (FBM) and area-based matching (ABM). A wavelet-based feature extraction technique and a normalized cross-correlation matching and relaxation-based image matching techniques are employed in this new method. Two pairs of data sets, one pair of IKONOS panchromatic images from different times and the other pair of images consisting of an IKONOS panchromatic image and a QuickBird multispectral image, are used to evaluate the proposed image registration algorithm. The experimental results show that the proposed algorithm can select sufficient control points semi-automatically to reduce the local distortions caused by local height variation, resulting in improved image registration results.     

基于改进头脑风暴优化算法的医学图像配准方法

针对精准医疗中图像配准方法收敛速度慢、精度不够高的问题,提出一种基于改进头脑风暴优化（Improved brain storm optimization, IBSO）算法的医学图像配准方法。配准过程分为3个阶段：首先,将待配准图像进行多分辨率分解;然后,使用IBSO算法对低分辨率图像进行全局粗配准;最后,利用单纯形搜索法对高分辨图像精配准。相比粒子群和单纯形结合算法、差分进化和Powell结合算法,以及头脑风暴和Powell结合算法,在单模态实验中,所提算法平均耗时较以上3种算法分别降低了32.89%、13.91%和13.66%,且最大误差、平均误差最小;在多模态实验中,互信息、归一化互信息、交叉累计剩余熵与归一化互相关指数均优于上述3种配准算法。实验结果表明,所提算法可以有效地提升医学图像配准的精度与速度。     

小物体3维重建中影像与几何模型的配准

目的 近景摄影测量中的目标几何形状复杂,且拍摄影像的角度变化大,给影像与几何模型的配准带来了困难。传统单幅影像与几何模型配准的做法,由于缺乏自动粗配准的方法,效率相对较低。将多视影像首先统一坐标系的做法,在近景目标的复杂背景下也难以实现。方法 为此,将近景目标置于平面标定板上,利用相机标定的方法同时解算出影像的内外方位元素,实现多视影像坐标系的统一。然后人工选取3组以上同名点,做多视影像与几何模型的绝对定向,得到初始配准参数。最后使用多视影像与几何模型漫反射渲染图之间的归一化互信息作为相似性测度,用Powell非线性优化方法得到配准参数的精确值。结果 实验结果表明,使用标定板可以稳定地获取多视影像的内外方位元素,用绝对定向得到的配准参数进行影像和几何模型的交替显示仍然可以看到明显的裂缝,在经过互信息优化后裂缝现象得到明显改善。结论 多视影像与几何模型配准相比传统单幅影像与几何模型配准,人工选取同名点的工作量大大减少,由于人工选点存在误差,影响绝对定向的精度,使用归一化互信息作为测度进行非线性优化,可以获得更高的精度。     

Multispectral processing for color particle image velocimetry

Since the adoption of digital video cameras and cross-correlation methods for particle image velocimetry (PIV), the use of color images has largely been abandoned. Recently, however, with the re-emergence of color-based stereo and volumetric techniques, and the extensive use of color microscopy, color imaging for PIV has again become relevant. In this work, we explore the potential advantages of color PIV processing by developing and proposing new methods for handling multi-color images. The first method uses cross-correlation of every color channel independently to build a color vector cross-correlation plane. The vector cross-correlation can then be searched for one or more peaks corresponding to either the average displacement of several flow components using a color ensemble operation, or for the individual motion of colored particles, each with a different behavior. In the latter case, linear unmixing is used on the correlation plane to separate each known particle type as captured by the different color channels. The second method introduces the use of quaternions to encode the color data, and the cross-correlation is carried out simultaneously on all colors. The resulting correlation plane can be searched either for a single peak, corresponding to the mean flow or for multiple peaks, with velocity phase separation to determine which velocity corresponds to which particle type. Each of these methods was tested using synthetic images simulating the color recording of noisy particle fields both with and without the use of a Bayer filter and demosaicing operation. It was determined that for single-phase flow, both color methods decreased random errors by approximately a factor of two due to the noise signal being uncorrelated between color channels, while maintaining similar bias errors as compared to traditional monochrome PIV processing. In multi-component flows, the color vector correlation technique was able to successfully resolve displacements of two distinct yet coupled flow components with errors similar to traditional grayscale PIV processing of a single phase. It should be noted that traditional PIV processing is bound to fail entirely under such processing conditions. In contrast, the quaternion methods frequently failed to properly identify the correct velocity and phase and showed significant cross talk in the measurements between particle types. Finally, the color vector method was applied to experimental color images of a microchannel designed for contactless dielectrophoresis particle separation, and good results were obtained for both instantaneous and ensemble PIV processing. However, in both the synthetic color images that were generated using a Bayer filter and the experimental data, a significant peak-locking effect with a period of two pixels was observed. This effect is attributed to the inherent architecture of the Bayer filter. In order to mitigate this detrimental artifact, it is suggested that improved image interpolation or demosaicing algorithms tuned for use in PIV be developed and applied on the color images before processing, or that cameras that do not use a Bayer filter and therefore do not require a demosaicing algorithm be used for color PIV.     

融合密集残差块和GAN变体的医学图像非刚性配准

目的 现有的医学图像配准算法在处理较大非刚性形变的医学图像时,存在配准精度低和泛化能力不足的问题。针对该问题,提出结合密集残差块和生成对抗网络（generative adversarial network,GAN）的图像配准方法,用于多模态医学图像的非刚性配准。方法 将密集残差块引入到生成器,提取待配准图像对的更多高层特征,从而提高配准精度;在GAN中采用最小二乘损失代替逻辑回归构造的交叉熵损失,由于最小二乘损失的收敛条件更严格,同时能缓解梯度消失和过拟合,从而提高配准模型的稳定性;在判别器网络中引入相对平均GAN（relative average GAN,RaGAN）,即在标准判别器的基础上增加一项梯度惩罚因子,降低判别器的判别误差,从而相对减少配准模型的配准误差,有助于稳定配准精度。结果 在DRIVE（digital retinal images for vessel extraction）数据集上进行训练和验证,在Sunybrook Cardiac数据集和Brain MRI数据集上进行泛化性能测试,并与几种主流医学图像非刚性配准算法进行对比实验,表明,本文配准模型在精度和泛化能力上均有一定程度的提升,相比其他方法,视网膜图像、心脏图像和脑部图像的配准Dice值分别提升了3.3%、3.0%、1.5%。结论 提出的配准方法能够获取更多高层特征信息,从而提升配准精度;同时基于最小二乘法构建损失函数并对判别器进行加强,能够使得配准模型在训练阶段快速收敛,提高了配准模型的稳定性和泛化能力,适合存在较大非刚性形变的医学图像的配准。     

自适应样本块大小的图像修复方法

传统基于样本块的图像修复算法中样本块大小是固定不变的,在修复过程中无法根据图像的具体情况进行调节,这在很大程度上影响了图像的整体修复效果。为了解决这一问题,提出一种自适应确定样本块大小的方法。该算法通过分析图像的梯度域变化,获得各像素点处的结构信息,进而自适应确定待修复样本块的大小。仿真实验结果表明,该算法能够有效克服传统方法中经常出现的诸如结构误传播、图像整体结构丢失等缺点,对具有明显结构变化的图像取得了比较理想的修复效果。