基于傅里叶梅林变换的SURF算法

针对加速鲁棒性特征算法,在没有后续去误匹配等处理的情况下,对存在较大旋转角度的两幅待匹配图像,匹配精度较低的问题,提出了一种基于傅里叶梅林变换的SURF算法。该算法通过对待匹配图像和参考图像实施傅里叶变换和梅林变换,利用能量谱求出两幅图像发生的旋转角度,并利用SURF算法找出图像间发生的平移和尺度变化,实现了图像间的匹配。实验结果表明,该算法可有效地实现图像间存在较大旋转角度时的几何匹配,且相比已有的SIFT和SURF算法,具有更好的匹配效果。     

An image registration algorithm based on phase correlation and the classical Lucas–Kanade technique

Image registration is defined as an important process in image processing in order to align two or more images. A new image registration algorithm for translated and rotated pairs of 2D images is presented in order to achieve subpixel accuracy and spend a small fraction of computation time. To achieve the accurate rotation estimation, we propose a two-step method. The first step uses the Fourier Mellin Transform and phase correlation technique to get the large rotation, then the second one uses the Fourier Mellin Transform combined with an enhance Lucas–Kanade technique to estimate the accurate rotation. For the subpixel translation estimation, the proposed algorithm suggests an improved Hanning window as a preprocessing task to reduce the noise in images then achieves a subpixel registration in two steps. The first step uses the spatial domain approach which consists of locating the peak of the cross-correlation surface, while the second uses the frequency domain approach, based on low-frequency (aliasing-free part) of aliased images. Experimental results presented in this work show that the proposed algorithm reduces the computational complexities with a better accuracy compared to other subpixel registration algorithms.     

Blind cross-spectral image registration using prefiltering andFourier-based translation detection

This paper describes a fast technique for registering image pairs from visible and infrared spectra that differ by translation, small rotations, and small changes of scale. The main result of this paper is a nonlinear prefiltering and thresholding technique that substantially enhances the cross-spectral correlation, provided that the image pairs have many features in common. We show the use of the technique in conjunction with a Fourier-based normalized correlation algorithm to perform fast cross-spectral registrations. In the absence of such prefiltering, local reversals of contrast from image to image tend, to impair the quality of correlation-based registrations. The algorithm computes the translation that maximizes the overall normalized correlation of the filtered images without examining individual image features. Small rotations and scale changes can be recovered by computing the translation displacement in several different regions of the image pairs. In an experiment on a moderate-sized database, the algorithm produced a correct registration rate of over 90% at a false positive rate of less than 10%. For a particularly difficult subset of images in the database, the correct registration rate fell to approximately 85% at a false positive rate of less than 10%. This retrieval quality is comparable to that of a mutual-information-based algorithm     

融合显性和隐性度量的多模图像配准算法

为解决基于隐性度量的图像配准算法初始配准误差大容易引起失配的问题,本文提出了一种融合显性度量和隐性度量的多模图像分层配准算法。首先采用金字塔分解算法得到低分辨率待配准图像。然后在每一层金字塔图像中,先采用互信息作为显性度量,利用粒子群算法获得初始的配准参数;以此作为初始值,采用基于隐性度量的配准算法,利用融合粒子群和鲍威尔搜索法的优化算法获取更准确的配准参数。基于低分辨率图像计算得到配准参数后,先对高分辨率待配准图像进行变换,然后利用提出的上述算法进行参数优化,根据每层得到的配准参数计算最终的配准参数。最后,采用可见光与红外图像、多波段SAR图像进行了配准实验。实验结果表明,提出的算法适用于多模图像配准,能够减小配准误差,具有一定的适用性。      

Pseudopolar-based estimation of large translations, rotations, and scalings in images.

One of the major challenges related to image registration is the estimation of large motions without prior knowledge. This paper presents a Fourier-based approach that estimates large translations, scalings, and rotations. The algorithm uses the pseudopolar (PP) Fourier transform to achieve substantial improved approximations of the polar and log-polar Fourier transforms of an image. Thus, rotations and scalings are reduced to translations which are estimated using phase correlation. By utilizing the PP grid, we increase the performance (accuracy, speed, and robustness) of the registration algorithms. Scales up to 4 and arbitrary rotation angles can be robustly recovered, compared to a maximum scaling of 2 recovered by state-of-the-art algorithms. The algorithm only utilizes one-dimensional fast Fourier transform computations whose overall complexity is significantly lower than prior works. Experimental results demonstrate the applicability of the proposed algorithms.     

相位相关技术实现离焦模糊图像运动估计

为解决离焦模糊图像的运动参数估计问题,介绍一种采用相位相关分析的图像配准技术。该方法利用傅里叶变换的平移特性,对产生平移的目标图像进行傅里叶变换,计算位移图像之间归一化互功率谱,其傅里叶逆变换对应二维脉冲函数,通过计算脉冲函数峰值坐标获取位移图像之间的亚像元级位移量。结合相位相关配准原理和线性空间不变退化模型,给出离焦成像系统点扩散函数及其光学传递函数的数学描述,侧重讨论离焦模糊对相位相关配准结果的影响,证明图像经过离焦退化后,位移图像之间归一化的互功率谱具有不变性。动态运动模糊图像最大检测误差0.339像元,标准差0.19像元。该方法具有可行性和有效性,能够满足一般要求。     

A nonlinear least square technique for simultaneous image registration and super-resolution.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.     

基于坐标变换的合成孔径雷达高精度成像算法

针对当前合成孔径雷达成像算法处理低频大斜视角数据存在的散焦和运算量大问题, 提出了一种新的高精度大斜视合成孔径雷达成像算法.算法首先把数据录取坐标系数据变换到零多普勒坐标系数据, 然后在零多普勒坐标系中采用距离时域非均匀傅里叶变换去除距离、方位的耦合, 进而得到聚焦良好的合成孔径雷达图像.算法运算量与距离多普勒算法在一个量级, 具有较高的成像效率.仿真结果表明算法在低频大斜视角下能很好地聚焦, 从而验证了算法的正确性.     

Fast point-based 3-D alignment of live cells.

Typical time intervals between acquisitions of three-dimensional (3-D) images of the same cell in live cell imaging are in the orders of minutes. In the meantime, the live cell can move in a water basin on the stage. This movement can hamper the studies of intranuclear processes. We propose a fast point-based image registration method for the suppression of the movement of a cell as a whole in the image data. First, centroids of certain intracellular objects are computed for each image in a time-lapse series. Then, a matching between the centroids, which have the maximal number of pairs, is sought between consecutive point sets by a 3-D extension of a two-dimensional fast point pattern matching method, which is invariant to rotation, translation, local distortion, and extra/missing points. The proposed 3-D extension assumes rotations only around the z axis to retain the complexity of the original method. The final step involves computing the optimal fully 3-D transformation between images from corresponding points in the least-squares manner. The robustness of the method was evaluated on generated data. The results of the simulations show that the method is very precise and its correctness can be estimated. This article also presents two practical application examples, namely the registration of images of HP1 domains and the registration of images of telomeres. More than 97% of time-consecutive images were successfully registered. The results show that the method is very well suited to live cell imaging.     

异分辨率高光谱图像的空间配准

近年来,高光谱成像仪迅速发展,可应用于越来越多更复杂的应用场景。人们对遥感图像处理精度等方面提出了更高的要求。图像配准作为遥感图像处理的重要步骤,对遥感图像的后续处理至关重要。遥感图像的配准方式很多,目前比较成熟的配准方式大多集中在不同传感器、相同分辨率下的配准;对于不同传感器、不同分辨率的遥感图像配准成果很少,所以本文对此进行了研究。提出了基于边缘特征寻找同名点的遥感图像配准算法,得到了图像配准结果,并对其进行了验证。相对于现有的配准算法,本文算法使得不同传感器、不同分辨率的遥感图像的配准精度大大提高。     

红外与可见光图像融合中的快速配准方法

红外与可光传感器目标跟踪识别系统中常用的两种传感器，对这两种传感器图像进行有效的融合能大大提高探测识别的准确性和可靠性。快速准确地实现图像配准是图像融合的前提，为此提出了一种基于小波变换的快速算法，有利于实时准确地实现图像配准。     

基于点扩散函数支持域扩展的多帧未匹配退化图像盲解卷积复原

多帧盲解卷积算法利用多帧退化图像进行复原可以获得清晰原始图像和点扩散函数的信息,受到了很多研究者的关注。目前大部分多帧盲解卷积算法都需要对多帧退化图像进行匹配预处理,以消除图像间平移引入的算法求解误差。本文利用频率内的多帧盲解卷积算法对未匹配的退化图像进行处理,不需要进行预匹配处理,只需要对点扩散函数的支持域进行扩展就可以复原获取清晰化的图像。利用傅里叶变换的性质对该方法的可行性进行了说明。同时对该方法进行了数字仿真实验,复原结果中的点扩散函数发生了相对移动消除了图像间未匹配的影响,证实了本文方法的有效性。     

Image registration using log-polar mappings for recovery of large-scale similarity and projective transformations.

This paper describes a novel technique to recover large similarity transformations (rotation/scale/translation) and moderate perspective deformations among image pairs. We introduce a hybrid algorithm that features log-polar mappings and nonlinear least squares optimization. The use of log-polar techniques in the spatial domain is introduced as a preprocessing module to recover large scale changes (e.g., at least four-fold) and arbitrary rotations. Although log-polar techniques are used in the Fourier-Mellin transform to accommodate rotation and scale in the frequency domain, its use in registering images subjected to very large scale changes has not yet been exploited in the spatial domain. In this paper, we demonstrate the superior performance of the log-polar transform in featureless image registration in the spatial domain. We achieve subpixel accuracy through the use of nonlinear least squares optimization. The registration process yields the eight parameters of the perspective transformation that best aligns the two input images. Extensive testing was performed on uncalibrated real images and an array of 10,000 image pairs with known transformations derived from the Corel Stock Photo Library of royalty-free photographic images.     

基于椭圆傅里叶描述子的遥感图像配准算法

传统的基于点特征的图像配准算法在遥感图像配 准时提取的特征点数量大、分布密,易产生误匹配,导致特 征匹配的效率和精度低。为此,本文利用椭圆傅里叶描述子(EFD)能较好保留 形状信息的特点,提出了一种基于EFD的 图像配准算法,根据匹配的边界对预估变换参数,给出特征搜索范围,从而有效地提 高特征搜索的效率。实 验结果表明,所提出算法能有效抑制误匹配的产生,提升特征匹配的效率,尤其是对于纹理 丰富的大尺寸图像,提 升效果更加明显,验证了本文算法对遥感图像配准的适用性。     

Image Registration Using Adaptive Polar Transform

Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration, or analysis. Recently, researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, it suffers from nonuniform sampling which makes it not suitable for applications in which the registered images are altered or occluded. Inspired by LPT, this paper presents a new registration algorithm that addresses the problems of the conventional LPT while maintaining the robustness to scale and rotation. We introduce a novel adaptive polar transform (APT) technique that evenly and effectively samples the image in the Cartesian coordinates. Combining APT with an innovative projection transform along with a matching mechanism, the proposed method yields less computational load and more accurate registration than that of the conventional LPT. Translation between the registered images is recovered with the new search scheme using Gabor feature extraction to accelerate the localization procedure. Moreover an image comparison scheme is proposed for locating the area where the image pairs differ. Experiments on real images demonstrate the effectiveness and robustness of the proposed approach for registering images that are subjected to occlusion and alteration in addition to scale, rotation, and translation.     

A Reconstruction Algorithm for Photoacoustic Imaging Based on the Nonuniform FFT

Fourier reconstruction algorithms significantly outperform conventional backprojection algorithms in terms of computation time. In photoacoustic imaging, these methods require interpolation in the Fourier space domain, which creates artifacts in reconstructed images. We propose a novel reconstruction algorithm that applies the one-dimensional nonuniform fast Fourier transform to photoacoustic imaging. It is shown theoretically and numerically that our algorithm avoids artifacts while preserving the computational effectiveness of Fourier reconstruction.      

Fusion of thermal and visible cameras for the application of pedestrian detection

In this paper, we propose methods to calibrate visible and thermal cameras and register their images in the application of pedestrian detection. We calibrate the camera using a checkerboard pattern mounted on a heated rig. We implement the image registration using three different approaches. In the first approach, we use the camera calibration information to generate control points from the checkerboard pattern. These control points are then used to register the images. In the second approach, we generate trajectory points for image registration using an external illuminated object. In the third approach, we achieve the registration through face tracking without the aid of any external object. The particle swarm optimization algorithm performs the image registration using the generated control and trajectory points, observed in both the cameras. We demonstrate the advantages of fusing the thermal and visible camera within a pedestrian detection algorithm. We evaluate the proposed registration algorithms and perform a comparison with baseline algorithms, i.e. genetic and simulated annealing algorithms. Additionally, we also perform a detailed parameter evaluation of the particle swarm optimization algorithm. The experimental results demonstrate the accuracy of the proposed algorithm and the advantages of thermal-visible camera fusion.     

基于支持向量回归的光度配准算法

在空域内将图像之间的光度对应假设为线性的、或一些简单的非线性模型进行处理．配准后的图像仍存在较大的光度差异。将支持向量回归技术应用到光度配准中，利用支持向量回归估计图像问的光度对应关系，再根据估计得到的回归曲线对图像进行光度变换，消除图像间的光度差异，针对实际图像的对比实验表明，基于支持向量回归的光度配准算法。优于常用的空域内回归估计光度配准算法。     

A projection-based extension to phase correlation image alignment

Phase-correlation (PC) is a computationally efficient scheme for estimating 2-D and 3-D translations. We present a masking operator that significantly improves the accuracy and robustness of the PC scheme. The masking projects the estimated correlation function on the space of correlation functions that result from a certain range of translations, while rejecting components that are unrelated to the estimated motion. Thus, the registration accuracy is improved by an order of magnitude, especially when registering noisy images and volumes. The scheme is also shown to improve the registration of rotated images in the Fourier domain.