Optimization of mutual information for multiresolution image registration

We propose a new method for the intermodal registration of images using a criterion known as mutual information. Our main contribution is an optimizer that we specifically designed for this criterion. We show that this new optimizer is well adapted to a multiresolution approach because it typically converges in fewer criterion evaluations than other optimizers. We have built a multiresolution image pyramid, along with an interpolation process, an optimizer, and the criterion itself, around the unifying concept of spline-processing. This ensures coherence in the way we model data and yields good performance. We have tested our approach in a variety of experimental conditions and report excellent results. We claim an accuracy of about a hundredth of a pixel under ideal conditions. We are also robust since the accuracy is still about a tenth of a pixel under very noisy conditions. In addition, a blind evaluation of our results compares very favorably to the work of several other researchers.     

Motion estimation of skeletonized angiographic images using elastic registration

An approach for estimating the motion of arteries in digital angiographic image sequences is proposed. Binary skeleton images are registered using an elastic registration algorithm in order to estimate the motion of the corresponding arteries. This algorithm operates recursively on the skeleton images by considering an autoregressive (AR) model of the deformation in conjunction with a dynamic programming (DP) algorithm. The AR model is used at the pixel level and provides a suitable cost function to DP through the innovation process. In addition, a moving average (MA) model for the motion of the entire skeleton is used in combination with the local AR model for improved registration results. The performance of this motion estimation method is demonstrated on simulated and real digital angiographic image sequences. It is shown that motion estimation using elastic registration of skeletons is very successful especially with low contrast and noisy angiographic images.     

基于时序多帧投影的空间目标检测算法

为了对星图中空间目标进行检测识别,对基于 SURF（Speed-Up Robust Featrues）算法的星图精确配准技术和美国 SBV（Space-Based Visual）计划采用的 MTI（Moving Target Indicati-or）在轨目标检测算法进行了深入研究,提出一种针对16 Bits 星图的多目标检测算法,具体包括：首先利用 SURF 算法提取序列星图的特征点,根据最小二乘法计算得到的全局运动参数对星图进行精确配准;然后利用一种改进的 MTI 算法对序列星图进行时序多帧投影以抑制背景,得到仅含有疑似目标的序列图像;最后经过目标初始运动状态的建立,速度滤波以及坐标插值得到目标的运动轨迹。利用实拍的20帧序列星图验证算法性能,经本文算法配准后,星像质心的均方误差（RMSE,Root Mean Square Error）最小达到0.3269 pixel,平均值为0.5441 pixel;序列图像中的3个运动目标均被检出,且无虚警。实验结果表明,本文配准算法的精度能够满足时序多帧投影的要求,且目标检测算法符合恒虚警原理。     

Simultaneous homographic and comparametric alignment of multiple exposure-adjusted pictures of the same scene

An approach is presented that can simultaneously align multiple exposure-adjusted pictures of the same scene both in their spatial coordinates as well as in their pixel values. The approach is featureless and produces an image mosaic at a common spatial and exposure reference and also addresses the misalignment problem common to methods that compose mosaics from only pair-wise registered image pairs. The objective function considered minimizes the sum of the collective variance over pixels of a global coordinate grid on which to create the final image. The models employed relate images spatially by homographic transformations and tonally by comparametric functions. The importance of performing joint spatial and tonal registration on exposure-adjusted images is emphasized by providing two examples in which spatial-only registration fails. The performance between pair-wise and simultaneous registration under both spatial-only and joint registration procedures is discussed.     

一种从低分辨率图像序列获取高分辨率图像的算法

在某些应用领域,常常需要得到目标的高分辨率图像,考虑到在这些应用场合中,往往可以获得对同一景物或目标的多帧图像,本文提出了一种基于亚像素级图像配准与类似于中值滤波插值的从多幅低分辨率(LR)图像中获取一幅高分辨率(HR)图像的算法。算法首先采用梯度方法计算出LR图像之间的位移量,经过图像配准后,每个HR像素点被赋予其作用域内所有LR像点值的中值。仿真结果表明,该算法简单有效,既能提高图像分辨率,又能较好地去除非线性噪声。     

基于匹配跟踪置信度的自适应对应像素距离图像匹配跟踪算法

本文结合最小绝对差度量(MAD)和(Hausdroff)距离度量两者的基本思想的优点,提出了一种新的对应像素距离相似性度量方法和自适应模板图像匹配跟踪算法,设计了帧间和帧内匹配跟踪置信度,提出了基于匹配跟踪置信度的自适应对应像素距离图像匹配跟踪算法.并对基于匹配跟踪置信度的自适应对应像素距离图像匹配跟踪算法的匹配跟踪性能进行分析,实验结果表明:该算法具有较强的抗噪、抗畸变能力和匹配跟踪稳定性,具有较高的匹配精度和匹配概率.     

基于传感器参数和改良CPD算法的红外与可见光图像点云配准

为实现前下视红外图像与可见光图像的有效配准,提出了一种基于传感器参数和改良CPD算法的红外与可见光图像自动配准算法.首先,利用传感器的姿态和高度信息,对前下视红外图像进行几何透视校正,消除图像间的旋转和比例缩放等差异;然后,对可见光图像和校正后的红外图像提取边缘特征点,基于相似变换模型,利用改良的CPD算法对其实现精配准.实测数据验证表明,该方法能实现对红外与可见光图像的良好配准,配准精度达到1个像素左右.     

基于核回归正则项的图像超分辨率重建

讨论了图像成像的基本模型,并提出了一种基于调整核回归函数作为正则项的序列图像重建算法。该算法是对已经提出的核回归算法的改进,减少其在超分辨率图像重建时的运算量。而且在图像配准过程中针对图像间只存在平移和旋转变换,采用了基于矩形像素值的亚像素配准方法,以提高配准的速度和精度。利用此算法对序列图像进行重建仿真,并通过结论得出其在噪声严重的情况下具有更好的边缘保留特性。     

基于直方图信息和灰度偏移网格的数字减影图像灰度矫正算法

本文首次提出一种利用数字减影图像直方图信息和灰度偏移网格进行灰度矫正的新的矫正算法.该算法能够有效去除灰度失真带来的蒙盈片灰度级差异,使减影图像丢失的细节信息得以重现.通过分析减影图像直方图,提取其中的灰度偏移量信息,有效消除了减影图像中的灰度级噪声.首次提出灰度偏移网格,较好地解决了数字减影图像灰度矫正过程中减影质量与矫正速度的矛盾.     

基于标定信息的高低分辨率图像配准方法

针对激光三维成像系统分辨率较低,特征点不易提取,同时与可见光成像系统图像配准实时性不高的问题,提出了一种基于标定信息的低分辨率距离图像与高分辨率强度图像配准方法。首先,通过对激光三维成像系统与可见光成像系统进行标定,得到了二者的内参数、外参数以及畸变系数;其次,利用二者的标定信息,通过构造像素匹配模型,确定了低分辨率距离图像像素点所对应的高分辨率强度图像像素点。最后,通过遍历低分辨率距离图像每一个像素点,实现低分辨率距离图像与高分辨率强度图像的像素配准。实验结果表明:该方法在保持准配精度基本不变的情况下,配准时间从2.111 s减为0.856 s,降低了算法的时间消耗,具有一定的可行性。     

非凸性优化与动态自适应滤波的湍流退化视频复原

针对目标探测器在大气中高速飞行时受湍流干扰,导致光学系统接收到的视频/图像产生像素偏移、模糊、信噪比降低等问题,本文对湍流退化视频/图像复原的复杂性及复原方法进行了研究,提出了一种基于非凸势函数优化与动态自适应滤波的湍流退化视频复原方法。首先,研究了湍流退化视频的求和与去模糊框架,并通过利用非刚性配准方法对刚性全局配准方法进行改进,进一步缩小了模糊核的尺度;然后,在计算机视觉的非凸优化框架下,构建了图像解卷积的非凸性算法,有效地解决了图像解卷积难题;最后,结合湍流退化视频自身特点,对超分辨率视频复原的动态自适应滤波框架进行了扩展与改进,使其适用于湍流退化视频的复原。仿真实验结果表明,本文方法的复原效果不仅有较大提升,而且实现了对湍流退化视频序列的动态自适应复原。     

COMPACT SUPPORT THIN PLATE SPLINE ALGORITHM

Common tools based on landmarks in medical image elastic registration are Thin Plate Spline (TPS) and Compact Support Radial Basis Function (CSRBF). TPS forces the corresponding landmarks to exactly match each other and minimizes the bending energy of the whole image. However, in real application, such scheme would deform the image globally when deformation is only local. CSRBF needs manually determine the support size, although its deformation is limited local. Therefore, to limit the effect of the deformation, new Compact Support Thin Plate Spline algorithm (CSTPS) is approached, analyzed and applied. Such new approach gains optimal mutual information, which shows its registration result satisfactory. The experiments also show it can apply in both local and global elastic registration.     

The dual-bootstrap iterative closest point algorithm with application to retinal image registration

Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5% of the pairs containing at least one common landmark, and 100% of the pairs containing at least one common landmark and at least 35% image overlap.     

Nonrigid registration using free-form deformations: application tobreast MR images

In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.     

一种快速的亚像素图像配准算法

图像超分辨率重建是在现有红外探测器基础上提升空间分辨率的一种有效方法。超分辨率图像重建是利用一组相互之间存在亚像素位移的低分辨率图像构造出一幅高分辨率的图像,快速、高精度估计图像间的位移是其关键技术之一。提出了一种用于超分辨率重建的亚像素配准算法,算法由特征检测、像素级配准和亚像素级配准三个处理过程组成。在特征检测过程,首先采用梯度算子对图像进行边缘检测,然后对边缘点进行角点预检测,排除非角点像素点,之后再进行Harris角点检测,大大减少了计算量;在像素级配准过程,用NCC算法进行像素级配准,用统计方法去除误匹配点对;在亚像素级配准过程,先对像素级匹配点的邻域进行插值放大,再进行亚像素匹配,误匹配点剔除,相对偏移量计算。对提出的算法进行了仿真实验,结果显示本算法的速度较类似算法速度有较大的提高。     

基于混沌粒子群优化的图像相关匹配算法研究

该文将混沌优化搜索技术应用于粒子群优化算法(PSO),提出了一种基于混沌搜索的粒子群优化算法(CPSO),该算法利用了PSO算法的收敛快速性和混沌运动的遍历性、随机性等特点,采用混沌初始化粒子初始位置,在运行过程中根据粒子群适应度的方差来自适应混沌更新粒子位置。几种典型函数的测试结果表明:CPSO提高了对多维空间的全局搜索能力,并可以有效避免早熟现象。将该方法用于图像相关匹配算法,提出了一种新的基于CPSO的图像相关匹配算法。实验结果证明该方法对解决噪声情况下的图像匹配问题十分有效。     

A fast direct Fourier-based algorithm for subpixel registration ofimages

This paper presents a new direct Fourier-based algorithm for performing image-to-image registration to subpixel accuracy, where the image differences are restricted to translations and uniform changes of illumination. The algorithm detects the Fourier components that have become unreliable estimators of shift due to aliasing, and removes them from the shift-estimate computation. In the presence of aliasing, the average precision of the registration is a few hundredths of a pixel. Experimental data presented here show that the new algorithm yields superior registration precision in the presence of aliasing when compared to several earlier methods and has comparable precision to the iterative method of P. Thevenaz et al. (1998)     

Appearance based background subtraction for PTZ cameras

Traditional background subtraction algorithms assume the camera is static and are based on simple per-pixel models of scene appearance. This leads to false detections when the camera moves. While this can sometimes be addressed by online image registration, this approach is prone to dramatic failures and long-term drift. We present a novel background subtraction algorithm designed for pan-tilt-zoom cameras that overcomes this challenge without the need for explicit image registration. The proposed algorithm automatically trains a discriminative background model, which is global in the sense that it is the same regardless of image location. Our approach first extracts multiple features from across the image and uses principal component analysis for dimensionality reduction. The extracted features are then grouped to form a Bag of Features. A global background model is then learned from the bagged features using Support Vector Machine. The proposed approach is fast and accurate. Having a single global model makes it computationally inexpensive in comparison to traditional pixel-wise models. It outperforms several state-of-the-art algorithms on the CDnet 2014 pan-tilt-zoom and baseline categories and Hopkins155 dataset. In particular, it achieves an F-Measure of 75.41% on the CDnet dataset PTZ category, significantly better than the previously reported best score of 62.07%. These results show that by removing the coupling between detection model and spatial location, we significantly increase the robustness to camera motion.     

基于兴趣点检测和仿射变换模型的序列图像拼接

针对序列图像的拼接问题,提出了一种基于兴趣点检测和仿射变换模型的图像配准算法,利用Harris算子检测兴趣点,并引入灰度交叉匹配和兴趣点坐标离散度两级约束策略确定同名兴趣点,通过在图像间建立仿射变换模型实现图像的配准;提出了一种自适应加权归一化函数对拼接图像进行处理,消除由于图像间灰度差异而导致的拼接缝。实验结果表明算法具有较高的配准精度和良好的像素融合效果。     

Maximum a posteriori spatial probability segmentation

An image segmentation algorithm that performs pixel-by-pixel segmentation on an image with consideration of the spatial information is described. The spatial information is the joint grey level values of the pixel to be segmented and its neighbouring pixels. The conditional probability that a pixel belongs to a particular class under the condition that the spatial information has been observed is defined to be the a posteriori spatial probability. A maximum a posteriori spatial probability (MASP) segmentation algorithm is proposed to segment an image such that each pixel is segmented into a particular class when the a posteriori spatial probability is a maximum. The proposed segmentation algorithm is implemented in an iterative form. During the iteration, a series of intermediate segmented images are produced among which the one that possesses the maximum amount of information in its spatial structure is chosen as the optimum segmented image. Results from segmenting synthetic and practical images demonstrate that the MASP algorithm is capable of achieving better results when compared with other global thresholding methods