A customized low-rank prior model for structured cartoon–texture image decomposition

The mathematical characterization of the texture component plays an instrumental role in image decomposition. In this paper, we are concerned with a low-rank texture prior based cartoon–texture image decomposition model, which utilizes a total variation norm and a global nuclear norm to characterize the cartoon and texture components, respectively. It is promising that our decomposition model is not only extremely simple, but also works perfectly for globally well-patterned images in the sense that the model can recover cleaner texture (or details) than the other novel models. Moreover, such a model can be easily reformulated as a separable convex optimization problem, thereby enjoying a splitting nature so that we can employ a partially parallel splitting method (PPSM) to solve it efficiently. A series of numerical experiments on image restoration demonstrate that PPSM can recover slightly higher quality images than some existing algorithms in terms of taking less iterations or computing time in many cases.     

基于颜色-纹理自相关算法的内镜图像检索

为了同时有效的提取内镜图像的颜色和纹理信息,体现各特征的空间相关性,本文提出了一种颜色-纹理自相关算法应用于内镜图像检索.即通过纹理谱和自动阈值分割算法提取出纹理图像,并与颜色特征向量相融合再计算其相关性,得到距离分别为1、3的颜色-纹理自相关向量.在相似度匹配阶段采用Generalized Tversky Index...     

Discrete-index Markov-type random processes

Discrete-index Markov-type random processes in one and two dimensions are considered, with emphasis on two-dimensional processes (or fields). Important classes of Markov-type models, their properties, and their relationship are described. Although some new results are given, the authors mainly present a systematic study and grouping of processes according to two fundamental Markov-type properties: strict-sense Markov, defined in terms of conditional probabilities, and wide-sense Markov, defined in terms of linear minimum-mean-square error estimates. Classes of models having special cases of the fundamental properties, including many models which are widely used to represent images are obtained by specifying the index set, the conditioning set used to define the Markov property, and the process distribution. The relationships between unilateral and bilateral models in each class are carefully investigated. Particular attention is given to simultaneous autoregressive models which are shown to be both strict-sense and wide-sense Markov. Classification of processes according to their Markov-type properties helps to clarify the consequences of and relationships between different model assumptions     

结合自适应窗口的二维直方图图像增强

针对成像过程中因各种因素导致图像细节和纹理表现不明显的问题,结合Turgay Celik 的二维直方图理论,提出了具有自适应动态窗口的二维直方图图像增强方法。该方法在考虑图像纹理和细节信息的基础上,分析了二维直方图与图像纹理信息的关系特性和固定窗口的弊端,采用基于平均梯度法建立以与区域特性相适应的窗口为单位的二维直方图,将其累积分布与理想分布进行比较优化,实现增强。通过大量实验对比表明,该方法克服固定窗口造成的适应性差、易出现过增强和局部被弱化等问题,其处理后的图像细节丰富,纹理突出,具有更好的视觉效果和工程应用价值。     

Unsupervised Texture Segmentation of Natural Scene Images Using Region-based Markov Random Field

In analyzing natural scene images, texture plays an important role because such images are full of various textures. Although texture is crucial information in analyzing natural scene images, the texture segmentation problem is still hard to solve since the texture often exhibit non-uniform statistical characteristics. Although there are several supervised approaches that partition an image according to pre-defined semantic categories, the ever-changing appearances in the natural images make such schemes intractable. To overcome this limitation, we propose a novel unsupervised texture segmentation method for natural images by using the Region-based Markov Random Field (RMRF) model which enforces the spatial coherence between neighbor regions. We introduce the concept of pivot regions which plays a decisive role to incorporate local data interaction. By forcing pivot regions to adhere to initial labels, we make the Markov Random Field evolve fast and precisely. The proposed algorithm based on the pivot regions and the MRF for encapsulating spatial dependencies between neighborhoods yields high performance for the unsupervised segmentation of natural scene images. Quantitative and qualitative evaluations prove that the proposed method achieves comparable results with other algorithms.     

柱面全景图像视图合成中的关键问题研究

图像校正和插值是基于柱面全景图像视图合成中的关键问题。首先分析了极线采样方法在柱面图像校正像素保持率和像素有效率上的优势,通过计算极线的数量、范围及起止位置,有效保留了源图像的信息,减少了以往算法中由于使用单应性映射而导致的弱采样和过采样效应;其次,在新视点插值上,根据柱面全景图像的成像模型,推导了合成视图上像素点的位置和颜色计算公式。最后给出了基于仿真图像和实际图像的视图合成结果。      

基于金字塔分解和扇形局部均值二值模式的鲁棒纹理分类方法

针对传统局部二值模式(LBP)的特征鉴别力有限和噪声敏感性问题,该文提出一种基于金字塔分解和扇形局部均值二值模式的纹理特征提取方法。首先,将原始图像进行金字塔分解,得到对应于不同分解级别的低频和高频(差分)图像。为提取兼具鉴别力和稳健性的特征,进一步采用阈值化处理技术将高频图像转化为正、负高频图。然后,基于局部均值操作提出一种扇形局部均值二值模式(SLMBP),用于计算各级分解图像的纹理特征码。最后,对纹理特征码进行跨频带的联合编码和跨级别的直方图加权,从而获得最终的纹理特征。在公开的3个纹理数据库(Outex, Brodatz和UIUC)上进行分类实验,结果表明该文所提方法能够有效地提高纹理图像在无噪声环境和含高斯噪声环境下的分类精度。     

基于图像分解的稀疏正则化多区域图像分割方法

针对图像具有不同特征的成分,提出一种基于图像分解的多区域图像分割模型和算法.首先将图像分解项引入到图像分割模型中,递减了纹理和噪声对分割的影响;其次使用稀疏正则化方法保持分割区域的边缘几何结构;最后基于增广Lagrange乘子法,给出一种由扩散流引导的小波迭代阈值图像分割算法.一系列实验结果表明,提出的方法抗干扰能力强,对噪声具有更好的鲁棒性.提出的方法不仅能够分割结构图像,并且能够分割较复杂的纹理图像.     

Multivariate Gaussian MRF for multispectral scene segmentation andanomaly detection

Gaussian Markov random field texture models and multivariate parametric clustering algorithms have been applied extensively for segmentation, restoration, and anomaly detection of single-band and multispectral imagery, respectively. The present work extends and combines these previous efforts to demonstrate joint spatial-spectral modeling of multispectral imagery, a multivariate (vector observations) GMRF texture model is employed. Algorithms for parameter estimation and image segmentation are discussed, and a new anomaly detection technique is developed. The model is applied to imagery from the Daedalus sensor. Image segmentation results from test images are discussed and compared to spectral clustering results. The test images are collages, with known texture boundaries constructed from larger data cubes. Anomaly detection results for two Daedalus images are also presented, assessed using receiver operating characteristic (ROC) performance curves, and compared to spectral clustering models. It is demonstrated that even the simplest first-order isotropic texture models provide significant improvement in image segmentation and anomaly detection over pure spectral clustering for the data sets examined. The sensitivity of anomaly detection performance to the choice of parameter estimation method and to the number of texture segments is examined for one example data set     

Rotation and gray-scale transform-invariant texture classificationusing spiral resampling, subband decomposition, and hidden Markov model

This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation. First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands. In each band, we take high-order autocorrelation functions as features. Features in different bands, which form a vector sequence, are then modeled as a hidden Markov model (BMM). During classification, the unknown texture is matched against all the models and the best match is taken as the classification result. Simulations showed that the highest correct classification rate for 16 kinds of texture was 95.14%     

采用暗态点光源模型的夜间去雾算法

针对夜间雾、霾场景下的去雾图像存在颜色失真、纹理损失、亮度低等缺陷,本文提出了一种采用暗态点光源模型的夜间去雾算法,通过构建夜间雾、霾场景的暗态点光源模型,利用联合双边滤波、限制对比度自适应直方图均衡化等算法对降质图像进行处理,结合大气散射模型得到去雾图像。实验结果表明,该算法的处理速度快、夜间去雾效果较好,较对比算法在对比度、平均梯度以及信息熵上均有一定程度地改善,有效解决了去雾图像的颜色失真、纹理损失、亮度低等缺陷。     

Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation

Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging applications. To determine the MI between two images, the joint histogram of the two images is required. In the literature, linear interpolation and partial volume interpolation (PVI) are often used while estimating the joint histogram for registration purposes. It has been shown that joint histogram estimation through these two interpolation methods may introduce artifacts in the MI registration function that hamper the optimization process and influence the registration accuracy. In this paper, we present a new joint histogram estimation scheme called generalized partial volume estimation (GPVE). It turns out that the PVI method is a special case of the GPVE procedure. We have implemented our algorithm on the clinically obtained brain computed tomography and magnetic resonance image data furnished by Vanderbilt University. Our experimental results show that, by properly choosing the kernel functions, the GPVE algorithm significantly reduces the interpolation-induced artifacts and, in cases that the artifacts clearly affect registration accuracy, the registration accuracy is improved.     

Image registration for image-based rendering.

Image-based rendering (IBR) has received much attention in recent years for its ability to synthesize photo-realistic novel views. To support translational motion, existing IBR methods either require a large amount of reference images or assume that some geometric information is available. However, rendering with a large amount of images is very expensive in terms of image acquisition, data storage, and memory costs. As IBR accepts various kinds of geometric proxy, we may use image registration techniques, such as stereo matching and structure and motion recognition, to obtain geometric information to help reduce the number of images required. Unfortunately, existing image registration techniques only support a small search range and require closely sampled reference images. This results in a high spatial sampling rate, making IBR impractical for use in scalable walkthrough environments.Our primary objective of this project is to develop an image registration technique that would recover the geometric proxy for IBR while, at the same time, reducing the number of reference images required. In this paper, we analyze the roles and requirements of an image registration technique for reducing the spatial sampling rate. Based on these requirements, we present a novel image registration technique to automatically recover the geometric proxy from reference images. With the distinguishing feature of supporting a large search range, the new method can accurately identify correspondences even though the reference images may only be sparsely sampled. This can significantly reduce the acquisition effort, the model size, and the memory cost.     

Bayesian Separation of Images Modeled With MRFs Using MCMC

We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution, we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior densities of pixels using Markov random fields based on a statistical model of the gradient image, and we use a fully Bayesian method with modified-Gibbs sampling. We contrast our work to approximate Bayesian solutions such as iterated conditional modes (ICM) and to non-Bayesian solutions of ICA variety. The performance of the method is tested on synthetic mixtures of texture images and astrophysical images under various noise scenarios. The proposed method is shown to outperform significantly both its approximate Bayesian and non-Bayesian competitors.     

Image retrieval using both color and texture features

In order to improve the retrieval performance of images, this paper proposes an efficient approach for extracting and retrieving color images. The block diagram of our proposed approach to content-based image retrieval (CBIR) is given firstly, and then we introduce three image feature extracting arithmetic including color histogram, edge histogram and edge direction histogram, the histogram Euclidean distance, cosine distance and histogram intersection are used to measure the image level similarity. On the basis of using color and texture features separately, a new method for image retrieval using combined features is proposed. With the test for an image database including 766 general-purpose images and comparison and analysis of performance evaluation for features and similarity measures, our proposed retrieval approach demonstrates a promising performance. Experiment shows that combined features are superior to every single one of the three features in retrieval.     

红外与可见光图像自动配准算法的研究

提出了一种新的红外与可见光图像的配准算法,该方法基于图像的小波变换与互信息最大化完成图像的配准过程.首先通过搜索小波模极大提取图像感兴趣区域(ROI),完成图像预处理,并采用仿射变换,建立图像变换模型;其次将低分辨率图像进行图像插值,计算灰度直方图,进行概率分布的估计;最后选择模拟退火法,逼近全局最优解.实验结果证明了此算法的有效性.     

Hierarchical Multiple Markov Chain Model for Unsupervised Texture Segmentation

In this paper, we present a novel multiscale texture model and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled, in turn, by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The “fragmentation” step allows one to find the elementary textures of the model, while the “reconstruction” step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural and remote sensing images.      

Bayesian image interpolation using Markov random fields driven by visually relevant image features

In this paper we present a Markov Random Field (MRF) based image interpolation procedure suited to both noise-free and noisy measurements. Specifically, after introducing a MRF characterized by means of a novel complex line process representing the visually relevant image features, we derive the global Maximum A Posteriori (MAP) interpolator under the hypothesis of spatially variant additive Gaussian noise. Besides, we derive a closed form local Bayesian MAP interpolator, on the base of which we develop a suboptimal, computationally efficient, single pass interpolation procedure. Numerical simulations demonstrate that the interpolation procedure outperforms state-of-the-art techniques, from both a subjective and objective point of view, in the case of noise-free and noisy measurements.