基于方向多尺度变换和统计建模的纹理分类方法

纹理是图像分析和识别中经常使用的关键特征, 而小波变换则是图像纹理表示和分类中的常用工具. 然而, 基于小波变换的纹理分类方法常常忽略了小波低频子带信息, 并且无法提取图像纹理的块状奇异信息. 本文提出小波子带系数的局部能量直方图建模方法、轮廓波特征的Poisson 混合模型建模方法和基于轮廓波子带系数聚类的特征提取方法, 并将其应用于图像纹理分类上. 基于局部能量直方图的纹理分类方法解决了小波低频子带的建模难题, 基于Poisson 混合模型的纹理分类方法则首次将Poisson 混合模型用于轮廓子带特征的建模, 而基于轮廓波域聚类的纹理分类方法是一种快速的分类方法. 实验结果显示, 本文所提出的三类方法都超过了当前典型的纹理分类方法.     

基于NSST和MCM纹理特征提取算法

纹理特征提取作为图像处理的重要环节,对图像的后续处理有着至关重要的影响.文中在多分辨共生矩阵算法的基础上,针对标准Brodatz纹理图像检索,通过非下采样剪切波变换的多分辨共生矩阵和混合高斯模型相结合,提出了一种纹理特征提取算法.文中首先对Brodatz纹理图像进行非下采样剪切波变换得到子带系数,通过对细节子带直方图分析,引入了拟合效果较好的混合高斯模型.然后利用优化的非均匀量化策略,提取多分辨共生矩阵纹理特征F2和F10.最后将提取的纹理特征与统计特征级联融合并结合具有权重系数的相似性度量公式,用于最终纹理图像检索.仿真实验表明:与传统多分辨共生矩阵的方法相比,文中所提算法的平均检索率分别提高了2.01%和8.87%.     

高斯测度空间上的拟线性变换与高斯变换

我们证明了从具有高斯测度的Banach空间到任意Banach空间中的两类可测变换的等价性。一类是拟线性变换,它保持平移拟不变性及对称不变性,另一类可测变换是高斯交换,它要求变换在定义域及值域的乘积空间上导出的测度是高斯测度。     

基于小波变换的Laws纹理测度在植被分割中的应用

针对从全色航空影像中进行植被区域提取的随机性和复杂性,阐述了一种基于小波的Laws纹理测度进行植被提取的新算法,它的特点是先用小波变换将图像变换到不同的尺度层上,然后再在多尺度层上提取Laws纹理测度,形成植被区域的特征.与传统的植被提取方法比,它用到了不同频率上纹理的Laws信息,从而更准确的刻画了植被区域的纹理特征,试验结果表明:基于小波变换的Laws纹理测度对植被有较好的分割效果.     

基于多特征信息自适应融合的视频目标跟踪算法

针对单一视觉特征跟踪的局限性,提出一种根据场景变化动态建立目标模型的粒子滤波视觉跟踪算法,方法首先选择简单且具有互补性的色彩与纹理特征描述表示当前图像,然后在粒子滤波框架下,利用民主融合策略进行信息融合,从而提高目标观测模型的鲁棒性;分析和实验表明, 算法对视频运动目标的任意平移、转动、部分遮挡、光照变化以及相似物干扰等情况下的跟踪均具有较好的效果.     

一种基于射影变换不变性的相机标定方法

随着数码相机在科技和生活中的应用范围不断扩大,其标定技术也受到了越来越多的关注.根据射影变换的不变性,提出了一种新的相机标定方法.首先探讨了世界坐标系、相机坐标系、图像像素坐标系、图像坐标系之间的关系,建立了相机标定方程,然后借助射影变换的特性提出了基于射影变换不变性的相机标定方法,并设计了相应的相机标定模版,最后对该方法进行了相关实验.     

关于DMRL序及其相关序的若干性质

证明了DMRL偏序关系在平移和尺度变换下的不变性,并获得了一个充分必要条件.同时考虑了DMRL序与IFR及IFRA序之间的关系.     

一种基于物体质心的图像自动化校准方法

图像校准是将任意相邻两图像都具有公共部分的一列图像合成一副全图的图像处理过程.而图像自动化校准的难点在于对一组图像公共部分特征信息的提取和自动匹配,尤其是自动匹配和图像校准的误差分析.本文基于放射变换不变性,给出了一种基于特征信息质心的自动匹配方法,与目前国际上主流特征提取和特征校准方法相比,本文方法较准确,且适用于卫星图片及一些MRI和CT 图片.本文方法的另一优点是具有线性计算复杂度.     

基于分类学习字典全局稀疏表示模型的图像修复算法研究

基于稀疏重构的图像修复依赖于图像全局自相似性信息的利用和稀疏分解字典的选择,为此提出了基于分类学习字典全局稀疏表示模型的图像修复思路.该算法首先将图像未丢失信息聚类为具有相似几何结构的多个子区域,并分别对各个子区域用K-SVD字典学习方法得到与各子区域结构特征相适应的学习字典.然后根据图像自相似性特点构建能够描述图像块空间组织结构关系的全局稀疏最大期望值表示模型,迭代地使用该模型交替更新图像块的组织结构关系和损坏图像的估计直到修复结果趋于稳定.实验结果表明,方法对于图像的纹理细节、结构信息都能起到好的修复作用.     

基于HSI颜色空间和暗原色先验的去雾算法

为了消除雾天对图像采集的影响,提高图像的质量,解决传统去雾技术对图像信息保留不完整,清晰度不好的问题,文章提出一种转换颜色空间的暗原色先验去雾改进算法.首先将图像的RGB颜色空间转换到HSI颜色空间,然后保持色调分量H不变;对亮度分量I进行暗原色先验去雾,并在进行暗原色去雾时,采用更为精确的四叉树算法求取大气光值;对饱和度分量S进行V变换,低频重构出新的饱和度分量,降低纹理、噪声等信息的影响并提高饱和度.对于含有大片天空区域的图像,则通过进一步提高最小透射率,可以有效地去除图像中的雾和霾,同时还避免了图像出现颜色失真的状况.实验结果证明,与经典的去雾算法相比较,文章算法去雾效果明显,图像清晰度高,图像信息保留比较完整,色彩更加真实自然,且时间复杂度较低.     

基于小波变换的图像去噪方法的研究

小波变换能有效的去除高斯噪声,中值滤波能有效的去除脉冲噪声,两者结合可以更有效的去除高斯噪声和脉冲噪声的混合噪声.当医学图像去除混合噪声时,先进行中值滤波再进行小波去噪的方法优于先进行小波去噪后再进行中值滤波的方法,且去噪后图像视觉效果较好,而且图像均方误差(M SE)也较小.在图像去噪处理中这种方法具有实际应用价值.     

Construction of parameterizations of masks for tight wavelet frames with two symmetric/antisymmetric generators and applications in image compression and denoising

In this paper, we present a general construction framework of parameterizations of masks for tight wavelet frames with two symmetric/antisymmetric generators which are of arbitrary lengths and centers. Based on this idea, we establish the explicit formulas of masks of tight wavelet frames. Additionally, we explore the transform applicability of tight wavelet frames in image compression and denoising. We bring forward an optimal model of masks of tight wavelet frames aiming at image compression with more efficiency, which can be obtained through SQP (Sequential Quadratic Programming) and a GA (Genetic Algorithm). Meanwhile, we present a new model called Cross-Local Contextual Hidden Markov Model (CLCHMM), which can effectively characterize the intrascale and cross-orientation correlations of the coefficients in the wavelet frame domain, and do research into the corresponding algorithm. Using the presented CLCHMM, we propose a new image denoising algorithm which has better performance as proved by the experiments.     

利用对偶树复数小波与全变差模型实现图像去噪的新方法

本文首先研究了一种三层小波系数相关萎缩的概念与性质,利用对偶树复数小波与全变差模型相结合,提出了一种新的图像去噪方法。实验结果表明,与现有的图像去噪方法相比,本文方法无论是在视觉还是在均方误差等方面均有更好的效果。     

SHAH: SHape-Adaptive Haar Wavelets for Image Processing

We propose the shape-adaptive Haar (SHAH) transform for images, which results in an orthonormal, adaptive decomposition of the image into Haar-wavelet-like components, arranged hierarchically according to decreasing importance, whose shapes reflect the features present in the image. The decomposition is as sparse as it can be for piecewise-constant images. It is performed via a stepwise bottom-up algorithm with quadratic computational complexity; however, nearly linear variants also exist. SHAH is rapidly invertible. We show how to use SHAH for image denoising. Having performed the SHAH transform, the coefficients are hard- or soft-thresholded, and the inverse transform taken. The SHAH image denoising algorithm compares favorably to the state of the art for piecewise-constant images. A clear asset of the methodology is its very general scope: it can be used with any images or more generally with any data that can be represented as graphs or networks.     

Accelerated Non-Overlapping Domain Decomposition Method for Total Variation Minimization

We concern with fast domain decomposition methods for solving the total variation minimization problems in image processing. By decomposing the image domain into non-overlapping subdomains and interfaces, we consider the primal-dual problem on the interfaces such that the subdomain problems become independent problems and can be solved in parallel. Suppose both the interfaces and subdomain problems are uniformly convex, we can apply the acceleration method to achieve an $\mathcal{O}(1 / n^2)$ convergent domain decomposition algorithm. The convergence analysis is provided as well. Numerical results on image denoising, inpainting, deblurring, and segmentation are provided and comparison results with existing methods are discussed, which not only demonstrate the advantages of our method but also support the theoretical convergence rate.     

小波分析在信号去噪声中的应用

小波分析是近年来发展起来的一种数学方法,在信号与图象处理中有重要的应用.中值滤波是信号处理中常用的一种非线性滤波器,它能够有效地消除瞬时脉冲干扰,并且能够很好地保持信号的边缘信息,在信号和图象处理中得到广泛应用.对中值滤波器与小波变换的结合进行了比较系统的研究.通过实例说明中值滤波器与小波变换相结合具有比单一滤波器更好的效果.     

基于广义交叉认证的多小波阈值的图像降噪

提出一种新的小波收缩阈值降噪方法,该方法是通过对噪声图像进行多小波变换,然后用广义交叉认证的方法来确定小波阈值参数．由于本文采用的是多小波变换,而多小波一般同时具有正交性和线性相位,另外广义交叉认证方法不需要对噪声的强度进行估计,所以这种方法有比较好的降噪效果．实验结果表明该方法与基于小波变换的广义交叉认证的图像降噪方法相比较,其降噪效果有一定的提高;同时也表明在一定的条件下,其降噪效果要明显好于传统的Wiener滤波方法．     

A fractional partial differential equation based multiscale denoising model for texture image

Traditional integer‐order partial differential equation based image denoising approach can easily lead edge and complex texture detail blur, thus its denoising effect for texture image is always not well. To solve the problem, we propose to implement a fractional partial differential equation (FPDE) based denoising model for texture image by applying a novel mathematical method—fractional calculus to image processing from the view of system evolution. Previous studies show that fractional calculus has some unique properties that it can nonlinearly enhance complex texture detail in digital image processing, which is obvious different with integer‐order differential calculus. The goal of the modeling is to overcome the problems of the existed denoising approaches by utilizing the aforementioned properties of fractional differential calculus. Using classic definition and property of fractional differential calculus, we extend integer‐order steepest descent approach to fractional field to implement fractional steepest descent approach. Then, based on the earlier fractional formulas, a FPDE based multiscale denoising model for texture image is proposed and further analyze optimal parameters value for FPDE based denoising model. The experimental results prove that the ability for preserving high‐frequency edge and complex texture information of the proposed fractional denoising model are obviously superior to traditional integral based algorithms, as for texture detail rich images. Copyright © 2013 John Wiley & Sons, Ltd.     

Nonlocal patches based Gaussian mixture model for image inpainting

We consider the inpainting problem for noisy images. It is very challenge to suppress noise when image inpainting is processed. An image patches based nonlocal variational method is proposed to simultaneously inpainting and denoising in this paper. Our approach is developed on an assumption that the small image patches should be obeyed a distribution which can be described by a high dimension Gaussian Mixture Model. By a maximum a posteriori (MAP) estimation, we formulate a new regularization term according to the log-likelihood function of the mixture model. To optimize this regularization term efficiently, we adopt the idea of the Expectation Maximization (EM) algorithm. In which, the expectation step can give an adaptive weighting function which can be regarded as a nonlocal connections among pixels. Using this fact, we built a framework for non-local image inpainting under noise. Moreover, we mathematically prove the existence of minimizer for the proposed inpainting model. By using a splitting algorithm, the proposed model are able to realize image inpainting and denoising simultaneously. Numerical results show that the proposed method can produce impressive reconstructed results when the inpainting region is rather large.     

过抽样M通道小波星图去噪方法研究

小波图像去噪已经成为目前图像去噪的主要方法之一,在分析了小波变换的基本理论和小波变换的多尺度分析基础上,根据多尺度小波变换的多分辨特性,提出了过抽样M通道小波变换去噪方法,并将此方法用于星图降噪处理中,收到良好的效果.