基于上下文和隐类属的小波域马尔可夫随机场SAR图像分割

该文针对合成孔径雷达(Synthetic Aperture Radar, SAR)图像含有大量的乘性斑点噪声的特点,提出了一种小波域隐类属的马尔可夫随机场(Markov Random Field, MRF)图像分割算法来抑制噪声的影响。考虑到小波的聚集性和持续性,该算法重新构造了待分图像小波域模型以类属为隐状态的混合长拖尾模型,将隐类属的马尔可夫随机场推广到小波域上,并用改进的上下文模型估计尺度间转移概率,最后推导出了新的最大后验(Maximum A Posteriori, MAP)分割公式。仿真结果证明,该算法具有鲁棒性能够有效地抑制噪声对图像的影响,得到准确的分割结果。     

马尔可夫随机场在SAR图像处理中的应用

马尔可夫随机场（MRF）可以很好地描述空间连续性，选择适当的邻域系统，能对图像的结构特征建模。利用以能量函数表示的联合概率分布，可以使用优化算法进行参数估计。高斯MRF能够准确、简洁地表示图像的纹理，而且具有线性特性，计算方便。本文回顾了在SAR图像处理中使用的MRF模型，详细说明了其中2种在图像复原及分割中的应用。     

小波变换在扫描电镜图像处理中的应用研究

根据扫描电镜图像的特点，对小波变换应用于扫描电镜图像的增强、降噪及融合方法进行了阐述,并利用所述方法对扫描电镜的图像进行了处理，结果表明，利用小波变换对扫描电镜的图像进行处理是有效的，可行的。     

小波在运动图像处理中的应用

在小波变换的基础上对运动图像的数据压缩编码过程进行了研究，建立了基于小波变换的运动图像编码系统模型，实现了数据的高效压缩并得到了良好的恢复图像。     

在变换域中用HMT图像降噪的研究

研究了HMT（隐马尔科夫树）在变换域中进行图像降噪的应用,就不同复杂程度的图像,不同类型噪声,胶用不同的变换域与传统方法进行比较,得到结论,变换域中的方法比在非变换域中的传统方法优势,HMT在小波域和DCT域有着近似的效果。在高斯白噪声下HMT方法是最有效的,在相关噪声下,降噪效果与图像纹理的复杂度及噪声的强度有关。     

结合小波域马尔可夫树模型的压缩采样图像重建

已有的研究表明基于模型的压缩采样信号重建可以取得更好的重建效果。本文提出一种结合小波域马尔可夫树模型的压缩采样图像重建方法。马尔可夫树模型很好的匹配了图像小波变换后的系数在尺度间的持续性。这种统计特性可以在正交匹配追踪算法中协助原子的选取,从而更准确的选取具有大幅值系数的原子。在本文提出的新算法中,每次迭代新增的原子是从与残差信号较匹配的候选原子中选取。候选原子中使模型的状态似然函数最大的原子被选出。实验结果表明,新算法可以更准确选出具有大系数原子,重建的图像质量好于其它传统方法。     

马尔可夫及隐马尔可夫模型的应用

通过对马尔可夫模型进行深入的分析的基础上对隐马尔科夫模型做了详细的讨论,对马尔科夫模型在语音识别、疾病分析等方面的应用做了介绍,同时针对隐马尔科夫模型在估值问题、解码问题和学习问题等经典问题上的应用做了研究。最后讨论了马尔科夫模型其隐马尔可夫模型的缺陷,并提出相关的改进建议。     

基于改进小波域隐马尔可夫模型的遥感图像分割

该文提出了一种基于改进小波域隐马尔可夫树(HMT)模型进行图像分割的方法。该方法利用基于希尔伯特变换对的二维方向小波,这种小波变换具有平移不变性、方向检测性好的特点。同时该方法还利用拓展HMT对该改进小波域中尺度间的小波系数相关性进行建模,并结合多背景融合技术进行遥感图像的分割,得到了优于已有文献的分割结果,而且与同类算法相比,降低了算法所需的计算量。     

基于小波域TS-MRF模型的监督图像分割方法

定义在单一空间分辨率上的树结构马尔可夫场(Tree-Structured Markov Random Field,TS-MRF)模型能够表达图像的分层结构信息,但难以描述图像的非平稳性,针对该问题,提出小波域的TS-MRF图像建模方法-WTS-MRF模型,按照图像分类层次树的结构形式,该模型将一系列的MRF嵌套定义在多...     

A tree-structured Markov random field model for Bayesian image segmentation

We present a new image segmentation algorithm based on a tree-structured binary MRF model. The image is recursively segmented in smaller and smaller regions until a stopping condition, local to each region, is met. Each elementary binary segmentation is obtained as the solution of a MAP estimation problem, with the region prior modeled as an MRF. Since only binary fields are used, and thanks to the tree structure, the algorithm is quite fast, and allows one to address the cluster validation problem in a seamless way. In addition, all field parameters are estimated locally, allowing for some spatial adaptivity. To improve segmentation accuracy, a split-and-merge procedure is also developed and a spatially adaptive MRF model is used. Numerical experiments on multispectral images show that the proposed algorithm is much faster than a similar reference algorithm based on "flat" MRF models, and its performance, in terms of segmentation accuracy and map smoothness, is comparable or even superior.     

Markov random field model-based edge-directed image interpolation.

This paper presents an edge-directed image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the interpolated image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired interpolated image corresponds to the minimal energy state of a 2-D random field given the low-resolution image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the interpolated edges while maintaining a high PSNR level.     

小波分析在图像处理中的应用

小波分析应用在图像处理中是小波分析较常见的应用之一，在小波分析的理论基础上。简要介绍了小波分析在图像处理中应用，重点介绍了在图像压缩中的应用。结果表明，小波分析应用在图像压缩中，压缩效果较好。     

SAR speckle reduction using wavelet denoising and Markov random field modeling

The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-field-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model, and the iterated-conditional-modes method is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.     

An adaptive edge model in the wavelet domain for wavelet image coding

State-of-art wavelet coders owe their performance to smart ideas for exploiting inter and intra-band dependencies of wavelet coefficients. We claim that developing more efficient coders requires us to look at the main source of these dependencies; i.e., highly localized information around edges. This paper investigates the structural relationships among wavelet coefficients based on an idealized view of edge behavior, and proposes a simple edge model that explains the roots of existing dependencies. We describe how the model is used to approximate and estimate the significant wavelet coefficients. Simulations support its relevance for understanding and analyzing edge information. Specifically, model-based estimation within the space-frequency quantization (SFQ) framework increases the peak signal-to-noise ratio (PSNR) by up to 0.3 dB over the original SFQ coding algorithm. Despite being simple, the model provides valuable insights into the problem of edge-based adaptive modeling of value and location information in the wavelet domain.     

Wavelet-based image denoising using a Markov random field a priorimodel

This paper describes a new method for the suppression of noise in images via the wavelet transform. The method relies on two measures. The first is a classic measure of smoothness of the image and is based on an approximation of the local Holder exponent via the wavelet coefficients. The second, novel measure takes into account geometrical constraints, which are generally valid for natural images. The smoothness measure and the constraints are combined in a Bayesian probabilistic formulation, and are implemented as a Markov random field (MRF) image model. The manipulation of the wavelet coefficients is consequently based on the obtained probabilities. A comparison of quantitative and qualitative results for test images demonstrates the improved noise suppression performance with respect to previous wavelet-based image denoising methods.     

一种基于非均匀马尔可夫随机场的图像分割方法

提出了一种新的基于非均匀马尔可夫随机场(MRF)的图像分割算法。基于非均匀马尔可夫随机场的图像分割的关键是对MRF中耦合系数的估计。本文结合四叉树分解提出了一种新的非均匀MRF的耦合系数估计方法。先对图像用传统的MRF分割方法进行预分割,再在预分割的基础上用边缘检测算子检验出预分割图像中的边缘,再利用图像的边缘信息对图像进行四叉树分解,把图像分成不同大小的子块。再根据每个子块的大小,估计出非均匀MRF的耦合系数。实验表明,将本文方法估计出来的耦合系数应用到分割算法中去,能明显改善图像分割的效果,而且具有更好的自适应性。     

Dual-tree complex wavelet hidden Markov tree model for image denoising

A new non-training complex wavelet hidden Markov tree (HMT) model, which is based on the dual-tree complex wavelet transform and a fast parameter estimation technique, is proposed for image denoising. This new model can mitigate the two problems (high computational cost and shift-variance) of the conventional wavelet HMT model simultaneously. Experiments show that the denoising approach with this new model achieves better performance than other related HMT- based image denoising algorithms.