基于MRF的自适应正则化红外背景杂波抑制算法

针对复杂背景下红外弱小目标检测难题,将背景杂波抑制归结为从原始红外弱小目标图像中重建目标数据的过程,据此提出了一种基于马尔可夫随机场模型(MRF)的自适应正则化滤波算法.该算法采用MRF,建立了红外弱小目标图像的先验概率模型,并根据图像的粗糙度设计了新的势函数.在此基础上,采用MRF对背景杂波抑制过程进行正则化处理,从而实现了对红外背景杂波的自适应各向异性抑制.理论分析与实验结果表明,该算法能够随图像局部纹理特征的变化自适应地调整滤波算子结构,从而可在复杂背景下自适应地抑制杂波、增强信号,有效地提高了图像的信噪比,且该算法结构简单,更易于硬件实时实现.     

基于改进的各向异性SUSAN算法的MEMS微结构图像滤波

为了改善在滤除微机电系统微结构图像的噪声时导致边缘模糊的问题,提出了一种改进的各向异性SUSAN滤波算法。该方法用独立强度传播模型决定长短轴的方差,由该点的梯度方向决定滤波器的长轴方向,由局部图像的灰度值与核值的差构成的局部均值构成SUSAN滤波器的自适应阈值,从而构建出各向异性SUSAN滤波器。该算法在平滑图像同时能保持图像的边缘特征。结果表明,各向异性SUSAN滤波器能够很好地降噪并保持图像的边缘信息。     

基于区域MRF和贝叶斯置信传播的SAR图像分割

 本文通过定义新的势函数,将贝叶斯置信传播算法和区域MRF模型有效结合,提出了一种SAR图像分割算法.考虑到SAR图像丰富的纹理信息,该算法对分水岭分割后的过分割区域提取纹理特征,在得到的区域邻接图上构建MRF模型,并加入区域灰度均值和方差作为区域特征,利用FCM聚类的初分割结果定义区域的关联势函数,并将区域特征引入到置信传播算法中,定义了新的交互势函数.该算法充分利用了SAR图像空间的背景信息,所定义的新的交互势函数能在促进分割结果区域一致性的同时较好保护边缘.实验结果表明,相对于其他MRF模型分割算法,本文算法能取得更好的分割效果.     

基于区域的MRF模型用于SAR图像分割

本文提出了一种建立在流域算法过分割结果区域图上的马尔可夫随机场模型的SAR图像分割算法.由于将马尔可夫场随机场(MRF)模型建立在预分割的基础上,极大减少了计算复杂度,并利用SAR图像的分布模型建立多层MRF模型,采用模拟退火优化得到MAP估计的分割结果.实验证明较传统的基于像素的马尔可夫随机场分割算法,该方法极大提高了运算速度,并能取得较为满意的分割结果.     

激光雷达主动偏振图像散斑抑制算法

针对激光主动偏振图像的散斑去除问题,提出了一种基于异向扩散的冲击各向异性滤波模型。该模型融合了冲击滤波器和小核值相似区算法,利用小核值相似区算法提取偏振图像边缘,减少了噪声对边缘检测的影响;针对不同的图像区域,自动调整冲击滤波器的系数,使得该算法既能保持图像边缘,又可以很好地抑制图像的散斑。使用八方向一阶差分估计小核值相似区算法的门限,门限估计更加准确;迭代终止条件采用完全散射区域的平均绝对误差作为标准,使得迭代次数更加合理。通过对比等效视数和边缘保持指数可见,冲击各向异性滤波算法的散斑抑制能力和边缘保持能力与传统的Lee和SRAD模型相比更加有效。     

基于多方向结构张量积的快速角点检测算法

针对各向异性高斯方向导数滤波器运算量大、耗时长的问题,利用盒式滤波器拟合出各向异性高斯方向导数滤波模板,并结合积分图像,提出一种性能优良的快速角点检测算法.利用盒式滤波器设计了6个方向的导数滤波模板,并结合积分图像,快速计算输入图像在各个方向上的导数响应;基于角点的稀疏特性,提出一种候选点粗筛选机制,快速筛选出候选角点区域像素以减少后续运算所涉及的像素数量;针对每一个候选像素,利用各个方向的导数响应构建多方向结构张量积,生成角点测度.将提出的算法与9种经典的检测器在仿射变换、高斯噪声干扰等条件下进行性能评估,在尺寸不同的测试图集上进行耗时对比.实验结果表明,新提出的算法具有优良的检测性能,耗时少,满足实时处理的需求.     

基于暗原色先验模型的Retinex算法

 现有雾天图像增强的Retinex算法采用固定滤波器,无法适应多种景深和雾化程度的情况.对此,本文提出一种基于暗原色先验模型的Retinex算法.暗原色先验模型反映了雾天图像中雾的分布与景深信息.受此启发,根据局部区域暗原色值设计一种尺度可变滤波器,针对不同景深和雾化区域采用不同尺度的滤波器估算雾天图像的照度分量,实现对雾天图像的增强.分别使用主观观察和客观数据分析方法,将本文算法与HE算法、固定尺度MSR算法进行对比,本文算法在细节增强以及图像整体效果上均优于HE算法和固定尺度MSR算法.     

基于DAMRF先验模型的MAP超分辨率重建算法

本文提出了一种基于非连续自适应MRF(DAMRF)先验模型的最大后验概率(MAP)超分辨率重建算法,此算法能对图像相邻像素之间的梯度影响进行自适应调整,从而较好的保持图像的连续性.仿真实验结果表明,该算法在保持图像边缘特性的同时,也较好的维持了图像的平滑性.     

一种新的血管造影图像增强方法

本文提出了一种新的血管造影图像增强算法.血管造影图像的质量由于种种原因而比较差,因而有必要对其进行增强以便于后续的分割或者中轴提取.为了解决这一问题,本文先对各向异性增强进行了深入分析,然后结合血管造影图像的特点,建立了一种新的对比度模型.并利用此模型,对原始的各向异性扩散方法中的传导参数加以自适应选择,最后利用改进的算法对血管造影图像进行增强.实验结果表明改进的各向异性扩散算法能很好地增强血管造影图像.     

基于SWT域改进粒子滤波的SAR图像降斑算法

 粒子滤波(PF)非常适合处理非高斯状态空间模型的滤波问题,而SAR图像的非高斯降斑算法正是粒子滤波的一个有效应用,本文在平稳小波变换(SWT)域上提出了一种基于马尔可夫随机场(MRF)的改进PF的SAR图像降斑算法.新算法首先分析验证了SAR图像在SWT域比在DWT域中利用广义高斯分布(GGD)建模更为精确;然后针对基本PF降斑算法中的粒子整体权重偏差问题,引入MRF重新定义粒子权重,并通过权重更新粒子的采样区间以优化粒子分布;最后为了提高本文降斑算法的实时性,依据小波系数的局部统计特性把图像分为平滑和边缘进行分区域处理.本文针对模拟SAR图像和实测SAR图像进行了仿真,仿真结果和分析表明降斑后的图像能够在去除噪声的同时较好的保持图像的边缘和纹理结构特征,而且分区域处理有效地提高了算法的效率.     

基于各向异性的快速超分辨率图像重建

针对传统超分辨率图像重建算法速度慢的缺点,提 出了一种基于自适应各向异性正则化的快速超分辨率图像重建算法。本文 算法兼顾重建图像质量的同时,提升了图形的重建速度。基于传统迭代算法,本文算法通过 优化约束条件,大量剔除了冗余过程, 弥补了传统算法的不足;同时引入一种具有自适应能力的各向异性平滑项,可以适应各种 复杂的运动模型。另外,提出 以图像的峰值信噪比(PSNR)为标准,作为重建迭代的截止 条件。运 用本文算法对序列低分辨率图像进行重建,证明了本文算法可以更快实现超分辨率图像重 建。     

Posterior-mean super-resolution with a causal Gaussian Markov random field prior

We propose a Bayesian image super-resolution (SR) method with a causal Gaussian Markov random field (MRF) prior. SR is a technique to estimate a spatially high-resolution image from given multiple low-resolution images. An MRF model with the line process supplies a preferable prior for natural images with edges. We improve the existing image transformation model, the compound MRF model, and its hyperparameter prior model. We also derive the optimal estimator--not the joint maximum a posteriori (MAP) or the marginalized maximum likelihood (ML) but the posterior mean (PM)--from the objective function of the L2-norm-based (mean square error) peak signal-to-noise ratio. Point estimates such as MAP and ML are generally not stable in ill-posed high-dimensional problems because of overfitting, whereas PM is a stable estimator because all the parameters in the model are evaluated as distributions. The estimator is numerically determined by using the variational Bayesian method. The variational Bayesian method is a widely used method that approximately determines a complicated posterior distribution, but it is generally hard to use because it needs the conjugate prior. We solve this problem with simple Taylor approximations. Experimental results have shown that the proposed method is more accurate or comparable to existing methods.     

遥感面阵凝视图像并行超分辨重建方法

遥感面阵凝视成像系统可以得到同一场景的多幅图像,研究者常利用这一特点进行多幅图像超分辨重建,以提高遥感图像空间分辨率。但是这类研究往往将超分辨过程独立出来,很少结合成像系统的几何参数优化超分辨重建模型。因此,对成像姿态影响图像不同方向上分辨率的问题进行了分析,提出了基于姿态角的各向异性模糊估计,使退化模型更加准确。同时,为了进一步精确面阵凝视成像系统超分辨重建中的匹配参数估计,提高由系统引起的全局初始匹配误差的包容性,基于最大后验法提出并行优化超分辨率图像和匹配参数的方法。算法充分利用成像过程信息并实时优化匹配参数,实验结果证明与现有方法相比,不仅可以得到细节信息更丰富,更易于人眼观察的遥感图像,并且均方误差降低0.3倍左右,信息熵平均提高1.2。     

A Markov random field model for classification of multisourcesatellite imagery

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery     

Fast and robust multiframe super resolution

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their short-comings. We propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.     

Pixon-based image segmentation with Markov random fields

Image segmentation is an essential processing step for many image analysis applications. We propose a novel pixon-based adaptive scale method for image segmentation. The key idea of our approach is that a pixon-based image model is combined with a Markov random field (MRF) model under a Bayesian framework. We introduce a new pixon scheme that is more suitable for image segmentation than the "fuzzy" pixon scheme. The anisotropic diffusion equation is successfully used to form the pixons in our new pixon scheme. Experimental results demonstrate that our algorithm performs fairly well and computational costs decrease dramatically compared with the pixel-based MRF algorithm.     

Efficient multiple-example based super-resolution for symmetric mixed resolution stereoscopic video coding

In this paper, we first propose a new symmetric mixed resolution stereoscopic video coding (SMRSVC) model which can provide clear bitrate-reduction and visual merits. Based on the newly proposed SMRSVC model, we then propose a quality-efficient multiple-example based super-resolution method. In the proposed super-resolution method, the four block examples selected from the forward and backward key-frames, the reference super-resolved frame, and the interview super-resolved frame are referred so as to effectively fuse the high frequency component of the super-resolved current block of the downsampled non-key-frame, and then an enhanced super-resolved non-key-frame is followed. Based on six test stereoscopic video sequences, the experimental results demonstrate that besides the bitrate-saving effect, the proposed super-resolution method for the proposed SMRSVC model also has better quality performance in terms of six well-known quality metrics when compared with several state-of-the-art methods for the previous asymmetric resolution stereoscopic video coding model and the SMRSVC model.     

基于MG-CG算法的图像超分辨率重建

提出一种基于多重网格(MG)和共扼梯度(CG)算法相结合的图像超分辨率重建快速算法.首先采用Tikhonov正则化方法给出图像超分辨率重建模型;然后在系统介绍MG和CG算法的基础上,针对超分辨率重建中常见对称正定稀疏线性方程的求解,提出多重网格-共扼梯度(MG-CG)算法;详细讨论了MG-CG算法的光滑、限制、插值操作以及计算复杂度.实验结果表明该算法与MG、CG和Richardson迭代算法相比,具有更快的收敛速度.     

Foreground object detection using top-down information based on em framework

In this paper, we present a novel foreground object detection scheme that integrates the top-down information based on the expectation maximization (EM) framework. In this generalized EM framework, the top-down information is incorporated in an object model. Based on the object model and the state of each target, a foreground model is constructed. This foreground model can augment the foreground detection for the camouflage problem. Thus, an object's state-specific Markov random field (MRF) model is constructed for detection based on the foreground model and the background model. This MRF model depends on the latent variables that describe each object's state. The maximization of the MRF model is the M-step in the EM framework. Besides fusing spatial information, this MRF model can also adjust the contribution of the top-down information for detection. To obtain detection result using this MRF model, sampling importance resampling is used to sample the latent variable and the EM framework refines the detection iteratively. Besides the proposed generalized EM framework, our method does not need any prior information of the moving object, because we use the detection result of moving object to incorporate the domain knowledge of the object shapes into the construction of top-down information. Moreover, in our method, a kernel density estimation (KDE)-Gaussian mixture model (GMM) hybrid model is proposed to construct the probability density function of background and moving object model. For the background model, it has some advantages over GMM- and KDE-based methods. Experimental results demonstrate the capability of our method, particularly in handling the camouflage problem.