Adaptive image registration via hierarchical Voronoi subdivision

Advances in image acquisition systems have made it possible to capture high-resolution images of a scene, recording considerable scene details. With increased resolution comes increased image size and geometric difference between multiview images, complicating image registration. Through Voronoi subdivision, we subdivide large images into small corresponding regions, and by registering small regions, we register the images in a piecewise manner. Image subdivision reduces the geometric difference between regions that are registered and simplifies the correspondence process. The proposed method is a hierarchical one. While previous methods use the same block size and shape at a hierarchy, the proposed method adapts the block size and shape to the local image details and geometric difference between the images. This adaptation makes it possible to keep geometric difference between corresponding regions small and simplifies the correspondence process. Implementational details of the proposed image registration method are provided, and experimental results on various types of images are presented and analyzed.     

Geometric direct search algorithms for image registration.

A widely used approach to image registration involves finding the general linear transformation that maximizes the mutual information between two images, with the transformation being rigid-body [i.e., belonging to SE(3)] or volume-preserving [i.e., belonging to SL(3)]. In this paper, we present coordinate-invariant, geometric versions of the Nelder-Mead optimization algorithm on the groups SL(3), SE(3), and their various subgroups, that are applicable to a wide class of image registration problems. Because the algorithms respect the geometric structure of the underlying groups, they are numerically more stable, and exhibit better convergence properties than existing local coordinate-based algorithms. Experimental results demonstrate the improved convergence properties of our geometric algorithms.     

采用各向异性尺度空间的遥感图像配准

基于多特征点的遥感图像配准方法应用在油田遥感图像上时,很难为其找到足够多的正确对应关系且花费时间也较长.为此,提出了基于各向异性尺度空间的遥感图像配准方法.该算法首先使用自适应的侧窗滤波技术构建各向异性尺度空间,然后采用改进的邻域分块思想进行特征描述,最后使用增强的匹配点过滤增加特征点匹配的数量.实验结果表明,所提方法...     

基于仿射变换模型的图像配准中的平移、旋转和缩放

文中讨论了二维仿射变换的平移、旋转和缩放等特性。对可见光波段和红外波段图像采用基于特征的配准,以边界作为特征进行相关运算,求出边界对应位置的关系,建立仿射变换 模型,按照最大相关原则自适应确定最佳配准参数k、θ、△x和△y,能够实现图像的自动配准。并利用实际可见光与红外图像给出了运用仿射变换模型的实验结果,表明该模型可有效解决图像配准中的平移、旋转和缩放。     

高图像配准精度的非相干数字全息彩色成像

菲涅耳非相干相关全息是一种能记录非相干物体全息图的新型技术,在生物医学成像和三维遥感领域具有重要应用前景。针对高光谱成像过程中图像融合配准困难的问题,文中利用空间光调制器的可编程特性,设计并制作了波长分别为492、562、672 nm的三组焦距恒定的双透镜相位掩模,依次调用三种波长的掩模并记录对应波长下物体的全息图。由于三色记录光经对应波长掩模调制后在CCD表面汇聚的光斑位置及尺寸均相同,因此,全息图重建后获得的重建图像具有相同的横向放大率,可提高图像融合时的配准精度,免去繁杂的光谱图像空间配准算法,真正实现了全息彩色成像的高精度配准和实时融合。采用该系统记录骰子的全息图,经数值重建及色彩融合后得到了颜色重建性较好的彩色三维像。     

Adaptive image denoising using scale and space consistency

This paper proposes a new method for image denoising with edge preservation, based on image multiresolution decomposition by a redundant wavelet transform. In our approach, edges are implicitly located and preserved in the wavelet domain, whilst image noise is filtered out. At each resolution level, the image edges are estimated by gradient magnitudes (obtained from the wavelet coefficients), which are modeled probabilistically, and a shrinkage function is assembled based on the model obtained. Joint use of space and scale consistency is applied for better preservation of edges. The shrinkage functions are combined to preserve edges that appear simultaneously at several resolutions, and geometric constraints are applied to preserve edges that are not isolated. The proposed technique produces a filtered version of the original image, where homogeneous regions appear separated by well-defined edges. Possible applications include image presegmentation, and image denoising.     

基于Arnold变换的数字图像自适应隐写算法

以往的图像自适应隐写算法大多数以顺序满嵌的方式嵌入秘密信息,这类算法秘密信息的隐蔽性不够高,因此提出一种随机非满嵌算法。通过分析出图像的系统参数使图像的满嵌容量刚好大于秘密信息的长度让载体图像达到非满嵌,增强了隐写的灵活性,减少了载体图像的嵌入修改量。再使用Arnold变换对数据的嵌入顺序进行置乱,防止攻击者按顺序分析出秘密信息,使秘密信息的隐蔽性变高,进而提高了算法的安全性。实验结果表明本算法提高了隐写的隐蔽性,减少了图像的嵌入失真度,且随机非满嵌操作适用于很多同类算法。     

基于直线空间结构特征的图像匹配方法

直线匹配在图像配准、目标识别等领域有重要应用。提出利用直线空间结构特征进行匹配的方法,算法采用粗精两级匹配的思路,粗匹配阶段用四参数仿射变换模型对待匹配图像进行粗步校正,精匹配阶段利用反映直线空间结构分布特征的直线交点方位角直方图,把直线匹配映射成两个点集的匹配,结合随机抽样共识(RANSAC)算法和参数迭代一致性方法剔除错配点对,实现图像的自动匹配。实验结果表明,本文方法能够取得较高的匹配精度。     

Approximate all nearest neighbor search for high dimensional entropy estimation for image registration

Information theoretic criteria such as mutual information are often used as similarity measures for inter-modality image registration. For better performance, it is useful to consider vector-valued pixel features. However, this leads to the task of estimating entropy in medium to high dimensional spaces, for which standard histogram entropy estimator is not usable. We have therefore previously proposed to use a nearest neighbor-based Kozachenko-Leonenko (KL) entropy estimator.Here we address the issue of determining a suitable all nearest neighbor (NN) search algorithm for this relatively specific task.We evaluate several well-known state-of-the-art standard algorithms based on k-d trees (FLANN), balanced box decomposition (BBD) trees (ANN), and locality sensitive hashing (LSH), using publicly available implementations. In addition, we present our own method, which is based on k-d trees with several enhancements and is tailored for this particular application.We conclude that all tree-based methods perform acceptably well, with our method being the fastest and most suitable for the all-NN search task needed by the KL estimator on image data, while the ANN and especially FLANN methods being most often the fastest on other types of data. On the other hand, LSH is found the least suitable, with the brute force search being the slowest.     

Consistent image registration

This paper presents a new method for image registration based on jointly estimating the forward and reverse transformations between two images while constraining these transforms to be inverses of one another. This approach produces a consistent set of transformations that have less pairwise registration error, i.e., better correspondence, than traditional methods that estimate the forward and reverse transformations independently. The transformations are estimated iteratively and are restricted to preserve topology by constraining them to obey the laws of continuum mechanics. The transformations are parameterized by a Fourier series to diagonalize the covariance structure imposed by the continuum mechanics constraints and to provide a computationally efficient numerical implementation. Results using a linear elastic material constraint are presented using both magnetic resonance and X-ray computed tomography image data. The results show that the joint estimation of a consistent set of forward and reverse transformations constrained by linear-elasticity give better registration results than using either constraint alone or none at all.     

图像配准技术研究进展

图像配准是解决图像融合、图像镶嵌和变化检测等问题的必要前提,其应用遍及军事、遥感、医学和计算机视觉等多个领域.简要回顾了图像配准技术的发展史和研究现状,重点阐述了当前的技术热点和应用趋势,最后展望了进一步的研究方向.     

稳健的图像匹配方法

景象匹配是计算机视觉研究的一个重要方向,如何在复杂的环境下提高匹配概率与定位精度是其亟待解决的难点之一。在分析畸变校正的量化噪声为高斯噪声的基础上,提出了基于特征和时空关联的积相关图像匹配算法。采用基于噪声抑制小波边缘检测方法,提取实时图和基准图的边缘特征;基于归一化积相关实现序列实时图(3帧)与基准图的匹配,再根据位置信息实现相关峰值的数据融合得到匹配点。此方法克服了因面积增大由几何失真导致的匹配概率下降的缺点,提高了匹配概率,具有较好的匹配稳健性。     

Signal scaling in cascade digital filters

Scaling of filter variables is central to fixed-point implementation of digital filters. A simple method of scaling based on scaling constants of individual sections of a cascade digital filter is presented. The proposed method saves much computational labor in the implementation of higher-order digital filters. Maximal amplitude expressions of second-order filter transfer functions are also presented.This work was supported by the Department of Science and Technology, Government of India, under Grant No. D.O.No.III-5(17)/85-ET.     

Continuous digital zooming of asymmetric dual camera images using registration and variational image restoration

This paper presents a theoretical basis to realize a high-quality digital zooming using two camera modules with different focal lengths. First, we describe an image degradation model of the asymmetric dual camera system to analyze the characteristic of the wide- and tele-view images. In an asymmetric dual camera system, we assume that the shorter focal length module produces the wide-view image with the low-resolution. On the other hand, the longer focal length module produces the tele-view image by an optical zooming. To reconstruct a wide-view image of a continuous digital zooming, the proposed method first estimates the point spread function (PSF) between the wide- and tele-view images. Next, the proposed method performs variational-based image restoration using the estimated PSF. In addition, since the tele-view image inserted into appropriate region of the wide-view image, the proposed method can provide significantly improved wide-view image.

     

基于ICC色彩空间转换的彩色印刷图像数字水印

对现有的数字水印方法应用到彩色印刷图像中存 在水印透明性和鲁棒性差等缺陷, 利用人眼视觉对黄-蓝颜色轴高频变化的敏感度低的特性,提出了基于ICC色彩空间转换 技术的彩色印刷图像数字水印方 法。首先选择与设备无关的CIE L*a*b*颜色空间,在Y-B分 量中嵌入有意义的水印信息;为了抵抗印刷-扫描过程中导致的 图像几何失真,同时避免在同一分量中多次嵌入水印信息导致的图像质量下降,在红- 绿(R-G)分量中嵌入了一个水印同步模 板信号;然后使用调幅加网技术对水印图像进行半色调化处理,使用胶印印刷方式输出;同 时,为了提高水印信号检测的正 确率,在水印检测时,对印刷-扫描图像进行了去网处理。实验结果表明,本文算法对彩 色印刷图像具有很好的水 印透明性,对印刷过程具有较强的鲁棒性,对常规的JPEG压 缩、放大/缩小、旋转等图像处理和几何变换攻击均具 有很强的鲁棒性,可以用于彩色数字图像和印刷图像版权的同步保护。     

Accurate image registration for MAP image super-resolution

The accuracy of image registration plays a dominant role in image super-resolution methods and in the related literature, landmark-based registration methods have gained increasing acceptance in this framework. In this work, we take advantage of a maximum a posteriori (MAP) scheme for image super-resolution in conjunction with the maximization of mutual information to improve image registration for super-resolution imaging. Local as well as global motion in the low-resolution images is considered. The overall scheme consists of two steps. At first, the low-resolution images are registered by establishing correspondences between image features. The second step is to fine-tune the registration parameters along with the high-resolution image estimation, using the maximization of mutual information criterion. Quantitative and qualitative results are reported indicating the effectiveness of the proposed scheme, which is evaluated with different image features and MAP image super-resolution computation methods.     

医学图像配准中的数据抽样方法研究

针对在基于灰度的医学图像配准中,传统数据抽样方法在过抽样时产生较多的局部极值点问题,提出了基于浮动图像灰度概率分布和其梯度信息的抽样方法.通过对人体脑部的刚体配准实验,从函数曲线和收敛性能方面,对比分析了五种数据抽样方法.实验结果表明,新抽样方法可以有效地减少局部极值点,提高归一化互信息测度的收敛性能.     

Fundamental performance limits in image registration

The task of image registration is fundamental in image processing. It often is a critical preprocessing step to many modern image processing and computer vision tasks, and many algorithms and techniques have been proposed to address the registration problem. Often, the performances of these techniques have been presented using a variety of relative measures comparing different estimators, leaving open the critical question of overall optimality. In this paper, we present the fundamental performance limits for the problem of image registration as derived from the Cramer-Rao inequality. We compare the experimental performance of several popular methods with respect to this performance bound, and explain the fundamental tradeoff between variance and bias inherent to the problem of image registration. In particular, we derive and explore the bias of the popular gradient-based estimator showing how widely used multiscale methods for improving performance can be explained with this bias expression. Finally, we present experimental simulations showing the general rule-of-thumb performance limits for gradient-based image registration techniques.     

F-information measures in medical image registration

A measure for registration of medical images that currently draws much attention is mutual information. The measure originates from information theory, but has been shown to be successful for image registration as well. Information theory, however, offers many more measures that may be suitable for image registration. These all measure the divergence of the joint distribution of the images' grey values from the joint distribution that would have been found had the images been completely independent. This paper compares the performance of mutual information as a registration measure with that of other F-information measures. The measures are applied to rigid registration of positron emission tomography (PET)/magnetic resonance (MR) and MR/computed tomography (CT) images, for 35 and 41 image pairs, respectively. An accurate gold standard transformation is available for the images, based on implanted markers. The registration performance, robustness and accuracy of the measures are studied. Some of the measures are shown to perform poorly on all aspects. The majority of measures produces results similar to those of mutual information. An important finding, however, is that several measures, although slightly more difficult to optimize, can potentially yield significantly more accurate results than mutual information.