显微图像拼接的误差分析

针对显微图像拼接中的误匹配和误差积累问题,把图像对准误差划分为第一类误差和第二类误差,并提出了一种新的图像拼接方法.首先利用所有重叠图像对的局部对准约束建立全局对准模型,它可以消除第一类误差引起的误差积累;然后根据全局对准误差的分布特性,提出消除第二类误差的最小回路一致性方法.实验表明,该图像拼接方法计算简单、有效,适用于大规模的显微图像拼接.     

大规模显微图像拼接算法

针对大规模显微图像拼接中的误差累积问题，提出了一种新的图像拼接模型。首先利用基于区域的对准方法，按照由粗到细的分层对准策略，提高图像对准的速度与准确性。然后，利用所有重叠图像对的对准约束,建立了一种图像拼接模型，并通过求解线性方程组得到拼接模型的最优解，从而实现了大规模显微图像的自动拼接。从实验结果看，该算法有效地消除了误差的累积效应，获得了理想的拼接效果。     

多层显微图像的自动拼接方法

针对多层显微图像的拼接,提出一种基于3D拓扑图的图像拼接方法.利用基于区域的方法和基于特征的方法,分别对同层和异层图像对准;利用3D拓扑图的修正以消除空白区的影响;根据修正的3D拓扑图和多层图像投影坐标的误差建立多层图像的全局对准模型;利用非线性最优化方法,得到具有全局一致性的多层图像拼接结果.     

基于手持相机的文档图像拼接算法

为了把手持相机拍摄的多幅文档图像拼接成一幅大的图像,提出了一种基于全局对准模型的文档图像拼接算法。该算法首先通过估计文档图像的消隐点坐标来校正透视失真,使相邻图像的几何关系可以用仿射变换表示;然后采用随机采样方法调整特征点之间的距离,使其尽可能均匀地分布在整个重叠区域内;接着利用所有重叠图像对的局部对准约束通过建立文档图像拼接的全局对准模型来有效地消除误差积累;最后利用二值函数对图像进行剪切,以减小重叠区内的对准误差。实验结果表明,该方法无需事先标定摄像机的内外参数和限制相机的位置,不仅具有较高的对准精度,且可有效地拼接手持相机拍摄的各种文档图像。     

基于最小路由代价树的大规模显微图像拼接方法

为了对大规模显微图像进行高质量的拼接,首先提出拼接图的概念及获得高质量全景图像的3个原则,然后采用分块-空间聚类算法配准相邻图像,同时评估配准质量,并计算拼接图的边的权值;最后在此基础上,提出了一种基于最小路由代价生成树的图像拼接方法,该方法通过计算拼接图的最小路由代价生成树来确定所有图像的全局位置,并用来生成全景图像。实验结果表明,该方法可获得高质量的全景图像。     

空中侦察序列图像连续拼接的累积误差分析与消除

分析研究了空中侦察序列图像连续拼接产生误差的原因,连续拼接中的误差累积与传播往往导致拼接图像质量难以接受甚至拼接过程失败,而拼接获取大视野战场毁伤全景图像具有重要军事应用价值;针对空中飘弋平台运动姿态复杂,成像模型难以建立的特点,提出了一种在连续拼接过程中变换基准图的拼接策略以消除累积误差的产生,通过对空中飘弋平台实拍序列图像和模拟仿真的序列图像进行拼接的实验结果表明,基准图的变换可大大减弱拼接过程中的累计误差影响,同时多次变换也会造成局部边缘的失真,但不影响后续的处理和使用,该策略的提出可为类似视点变化复杂的成像拼接提供误差消除方法和有效手段。     

基于全局拼接的航拍图像拼接算法研究

研究航拍图像的拼接问题,提高图像拼接的准确度。由于当通过航拍获取的图像中部分图像相互存在重叠区域的比例不大时,造成拼接不准确。传统的区域的图像拼接算法无法将具有较小重叠区域的图像准确拼接。为了提高航拍图像拼接的准确率,提出一种全局的图像拼接算法,通过使用SIFT算法提取图像的SIFT特征点,根据位置误差最小的原则完成两幅图像的SIFT特征点匹配,最后利用整体最优化方法对拼接结果进行优化,采用全局特征点的拼接方法,可避免传统方法只利用重叠区域灰度特征而不能准确拼接低重叠度图像的问题。实验证明,改进方法利用图像的全局信息,准确地实现图像的拼接,取得了满意的结果。     

基于对齐与优化的图像拼接

图像拼接技术将存在重叠区域的多幅图像经过配准和融合后得到单幅宽视场图像。由于误差的积累,多幅图像拼接后在重叠区域会有明显的拼接痕迹,所以需要对拼接后的图像进行优化。首先研究了变换矩阵及其参数,然后提出一种图像对齐方法,完成图像拼接,最后用全局优化策略消除累积误差。实验证明,该方法在存在较大光照强度变化,重叠区域小的情况下能够鲁棒地完成多幅图像的拼接。     

优化的多幅眼底图像拼接方法

针对多幅眼底图像拼接的问题,提出一种优化的多幅眼底图像拼接方法。该方法在基于特征的层次鲁棒估计方法完成两两图像初始配准的基础上,提出一种基于重叠区域的配准修正,得到两幅图像之间的良好配准;然后建立图像之间配准关系的无向图,并将配准图像中匹配特征的几何误差和相似度作为图中边的权值,通过计算关系图的最短路径,确定基准图像;再构造基于基准图像的直接约束和基于非基准图像的间接约束的多幅图像配准整体模型,用来生成多幅拼接图像。最后在图像融合部分,提出基于距离变换的多频带融合方法,消除拼接图像中的接缝,达到平滑融合效果。实验结果表明,该方法可有效消除多幅配准的累积误差,实现多幅眼底图像输入顺序无关性的精确配准和无缝拼接。     

空中侦察图像连续拼接中的平滑算法研究

侦察图像在现代战争中具有重要意义,利用图像处理技术对序列空中侦察图像进行拼接所生成的全场景图能为战场毁伤评估提供重要信息。对配准后的图像重叠部分进行平滑处理是图像拼接过程中的一个关键环节,能消除拼接处的图像不连续现象,可实现图像的无缝拼接。本文在研究现有平滑算法的基础上提出一种新的适用于侦察图像拼接的平滑算法,该平滑算法较易实现。实验结果表明,经该平滑算法处理后的拼接结果图既消除了拼接缝,又较好地保持了清晰度。     

Graph-based representation for similarity retrieval of symbolic images

Image retrieval from an image database by the image objects and their spatial relationships has emerged as an important research subject in these decades. To retrieve images similar to a given query image, retrieval methods must assess the similarity degree between a database image and the query image by the extracted features with acceptable efficiency and effectiveness. This paper proposes a graph-based model SRG (spatial relation graph) to represent the semantic information of the contained objects and their spatial relationships in an image with no file annotation. In an SRG graph, the image objects are symbolized by the predefined class names as vertices and the spatial relations between object pairs are represented as arcs. The proposed model assesses the similarity degree between two images by calculating the maximum common subgraph of two corresponding SRG’s through intersection, which has quadratic time complexity owing to the characteristics of SRG. Its efficiency remains quadratic regardless of the duplication rate of the object symbols. The extended model SRG_T is also proposed, with the same time complexity, for the applications that need to consider the topological relations among objects. A synthetic symbolic image database and an existing image dataset are used in the conducted experiments to verify the performance of the proposed models. The experimental results show that the proposed models have compatible retrieval quality with remarkable efficiency improvements compared with three well-known methods LCS_Clique, SIM_R, and 2D Be-string, where LCS_Clique utilizes the number of objects in the maximum common subimage as its similarity function, SIM_R uses accumulation-based similarity function of similar object pairs, and 2D Be-string calculates the similarity of 2D patterns by the linear combination of two 1D similarities.     

Skeleton-based image warping

Image warping refers to the 2D resampling of a source image onto a target image. Despite the variety of techniques proposed, a large class of image warping problems remains inadequately solved: mapping between two images which are delimited by arbitrary, closed, planar curves, e.g., handdrawn curves. This paper describes a novel algorithm to perform image warping among arbitrary planar shapes whose boundary correspondences are known. A generalized polar coordinate parameterization is introduced to facilitate an efficient mapping procedure. Images are treated as collections of interior layers, extracted via a thinning process. Mapping these layers between the source and target images generates the 2D resampling grid that defines the warping. The thinning operation extends the standard polar coordinate representation to deal with arbitrary shapes.     

Minimizing user intervention in registering 2D images to 3D models

This paper proposes a novel technique to speed up the registration of 2D images to 3D models. This problem often arises in the process of digitalization of real objects, because pictures are often taken independently from the 3D geometry. Although there are a number of methods for solving the problem of registration automatically, they all need some further assumptions, so in the most general case the process still requires the user to provide some information about how the image corresponds to geometry, for example providing point-to-point correspondences. We propose a method based on a graph representation where the nodes represent the 2D photos and the 3D object, and arcs encode correspondences, which are either image–to–geometry or image–to–image point pairs. This graph is used to infer new correspondences from the ones specified by the user and from successful alignment of single images and to factually encode the state of the registration process. After each action performed by the user, our system explores the states space to find the shortest path from the current state to a state where all the images are aligned, i.e. a final state and, therefore, guides the user in the selection of further alignment actions for a faster completion of the job. Experiments on empirical data are reported to show the effectiveness of the system in reducing the user workload considerably.     

Image denoising via sparse coding using eigenvectors of graph Laplacian

Image denoising plays an important role in image processing, which aims to separate clean images from the noisy images. A number of methods have been presented to deal with this practical problem in the past decades. In this paper, a sparse coding algorithm using eigenvectors of the graph Laplacian (EGL-SC) is proposed for image denoising by considering the global structures of images. To exploit the geometry attributes of images, the eigenvectors of the graph Laplacian, which are derived from the graph of noised patches, are incorporated in the sparse model as a set of basis functions. Sequently, the corresponding sparse coding problem is presented and efficiently solved with a relaxed iterative method in the framework of the double sparsity model. Meanwhile, as the denoising performance of the EGL-SC significantly depends on the number of the used eigenvectors, an optimal strategy for the number selection is employed. A parameter called as out-of-control rate is set to record the percentage of the denoised patches that suffer from serious residual errors in the sparse coding procedure. Thus, with the eigenvector number increasing, the appropriate number can be heuristically selected when the out-of-control rate falls below an empirical threshold. Experiments illustrate that the EGL-SC can achieve a better performance than some other well-developed denoising methods, especially in the structural similarity index for the noise of large deviations.     

自适应最小误差阈值分割算法

对二维最小误差法进行三维推广, 并结合三维直方图重建和降维思想提出了一种鲁 棒的最小误差阈值分割算法. 但该方法为全局算法, 仅适用于分割均匀光照图像. 为 提高其自适应性, 本文采用Water flow模型对非均匀光照图像进行背景估计, 以此获 得原始图像与背景图像的差值图像, 达到降低非均匀光照对图像分割造成干扰的目的. 为进 一步提高分割性能, 本文对差值图像采用γ 矫正进行增强, 然后采用鲁棒最小误差 法进行全局分割, 从而完成目标提取. 最后本文对均匀光照下以及非均匀光照下图像进行了 实验, 并与一维最小误差法、二维最小误差法、三维直方图重建和降维的Otsu阈值分割 算法、灰度波动变换自适应阈值方法以及一种改进的FCM方法在错误分割率和运行时间上进 行了对比. 实验结果表明, 相对于以上方法, 本算法的分割性能均有明显提升.     

DiCyc: GAN-based deformation invariant cross-domain information fusion for medical image synthesis

Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.     

一种基于多层次MRF的多源图象融合算法

图象融合技术的主要目的是将多种图象传感器数据中的互补信息组合起来 ,使形成的新图象更适合于计算机处理 (如分割、特征提取和目标识别 )等 .在多层次 MRF模型的基础上 ,提出了一种应用于多源图象分类的图象融合算法 .该融合算法将定义在多层次图结构上的非线性因果 Markov模型与贝叶斯 SMAP(sequential m axi-mum a posteriori)最优化准则结合起来 ,克服了 MAP(maximum a posteriori)准则在多层次图结构上计算不合理的缺陷 .该算法可应用于多源遥感图象中的信息融合 ,使像素分类更精确 ,并解决多源海量数据的富集表示 .另外还利用合成图象与自然图象分别针对多层次 MRF模型的改进及算法中可最优化准则的不同进行了对比实验 ,结果表明 ,该算法具有许多优越性