基于小波系数相关性的图像隐写分析

提出了一种基于小波包分解和小波系数相关性的通用型图像隐写分析方法。对差分处理后的图像进行小波包分解,提取图像及其小波子带的高阶统计量作为特征。利用图像小波系数在尺度方向和空间方向的相关性,使用马尔可夫模型挖掘小波系数层内和层间相关性,提取转移概率矩阵作为特征。针对H4PGP、F5和OutGuess隐写算法的实验表明,方法对上述三种隐写算法具有较好的检测效果。     

利用背景进行多尺度融合的多分辨率图像分割

提出了一种基于背景的小波域多分辨率图像分割新方法—CLTVWseg。与常见的多分辨率分割方法不同,该方法采用背景信息来实现尺度间的交互;同时采用可变的权重参数连接小波分解的多尺度特征场和标记场。每一尺度上,通过权重参数的调整使得该尺度的特征场和标记场在分割过程中依次起主导作用,获得该尺度更为准确的分割结果。最细尺度上的分割结果作为该方法的分割结果。实验表明,该算法的分割结果,在保持边界的同时,区域一致性也比较好。     

基于小波分形特征提取的图象分割方法

提出了一种基于小波分解和分形纹理特征计算的图象分割方法,首先考虑对图象进行小波变换,然后对不同通道的子图象提取纹理的分形特征和能量特征,最后用直方图阈值分割方法实现图象的分割,实验表明,该方法对模拟纹理图象以及多少谱遥感图象的分割都取得了满意的效果。     

图像隐写分析中小波系数相关性的研究与应用*

提出了一种针对空域图像隐写的盲检测方法。利用互信息分析秘密信息嵌入对图像小波系数在尺度方向和空间方向相关性的影响,使用马尔可夫模型挖掘小波系数层内和层间相关性,提取转移概率矩阵作为特征。针对LSB匹配和随机调制隐写算法的实验表明,此方法能有效检测未经JPEG压缩过的含密图像,相比现有空域盲检测方法,对低嵌入率含密图像的正确检测率提高约8%14% 。     

基于小波包特征的纹理影像分割

利用小波包变换来检测纹理图象上的灰度变化，以得到多分辨率、多方向性的微观统计特征图象，在此基础上用Ｅｎｖｅｌｏｐ算法提取基于边缘信息的纹理特征，为了克服纹理特征之间的相关性，采用了子空间分类器对纹理特征图象进行分类，取得了较好的分割效果。     

基于四元数小波混合统计模型的图像去噪

图像的去噪和压缩一直是图像处理的经典问题,传统的方法中很难将二者 同时兼顾。四元数小波变换是实小波、四元数理论及二维希尔伯特变换相结合的产物,是一 种新的多尺度分析图像处理工具。图像经四元数小波变换后,其小波系数不仅在尺度内具有 相关性,而且在尺度间也具有一定的相关性。文中提出一种混合统计模型,该模型包括尺度 间的二元非高斯分布模型和尺度内的广义高斯分布模型,然后运用最小均方误差（MMSE） 估计从噪声图中的小波系数恢复原图的系数,从而达到去除图像的噪声的目的。仿真实验表 明,论文方法不仅可以获得信噪比上的提高、视觉上达到明显的去噪效果,而且取得了较高 的压缩比。     

An experiment on texture segmentation using modulated wavelets

A textured image is considered to be produced by a smooth signal, representing in part the illumination environment, modulated by some frequencies determined by surface characteristics of the textures. Through expansion of the smooth signal into wavelets, a textured image may be decomposed into modulated “wavelets” providing multiresolution information. It is shown that the corresponding wavelet coefficients can be obtained efficiently by using the standard wavelet transform while the associated h and g filters are modulated accordingly. A set of multichannel filters is designed through the use of a modulated “wavelet' multiresolution decomposition, providing both spatial frequency and orientation selectivity. Based on these multichannel amplitude responses as discriminating features, an image containing multiple textures can be effectively segmented. The potential of this approach is shown by experimental results     

Multiresolution-based watersheds for efficient image segmentation

This paper presents an efficient method for image segmentation based on a multiresolution application of a wavelet transform and watershed segmentation algorithm. The procedure toward complete segmentation consists of four steps: pyramid representation, image segmentation, region merging and region projection. First, pyramid representation creates multiresolution images using a wavelet transform. Second, image segmentation segments the lowest-resolution image of the pyramid using a watershed segmentation algorithm. Third, region merging merges the segmented regions using the third-order moment values of the wavelet coefficients. Finally, the segmented low-resolution image with label is projected into a full-resolution image (original image) by inverse wavelet transform. Experimental results of the presented method can be applied to the segmentation of noise or degraded images as well as reduce over-segmentation. In addition, we applied our method to human face detection with accurate and closed boundaries.     

Multiresolution video object extraction fitted to scalable wavelet-based object coding

To enable content based functionalities in video processing algorithms, decomposition of scenes into semantic objects is necessary. A semi-automatic Markov random field based multiresolution algorithm is presented for video object extraction in a complex scene. In the first frame, spatial segmentation and user intervention determine objects of interest. The specified objects are subsequently tracked in successive frames and newly appeared objects/regions are also detected. The video object extraction algorithm includes discrete wavelet transform decomposition multiresolution Markov random field (MRF)-based spatial segmentation with emphasis on border smoothness at different resolutions, and an MRF-based backward region classification that determines the tracked objects in the scene. Finally, a motion constraint, embedded in the region classifier, determines the newly appeared objects/regions and completes the proposed algorithm towards an efficient video segmentation algorithm. The results are applicable for generic segmentation applications, however the proposed multiresolution video segmentation algorithm supports scalable object-based wavelet coding in particular. Moreover, compared to traditional object extraction algorithms, it produces smoother and more visually pleasing shape masks at different resolutions. The proposed effective multiresolution video object extraction method allows for larger motion, better noise tolerance and less computational complexity     

基于多分辨率马尔可夫随机场运动目标分割

提出一种基于三维时空小波变换和马尔可夫随机场(MarkovRandomField)模型的多分辨率运动目标分割算法。该算法利用三维时空小波变换对图像序列进行分解得到多分辨率的图像序列,并在此基础上建立多分辨率的马尔可夫随机场模型,构造相应的能量函数。通过条件迭代模型优化算法(IteratedConditionalModes)求解能量函数的最优解,得出标记场,提取出运动目标。实验结果证明,该算法能够很好地消除了单一分辨率的MRF运动检测结果中"空洞"现象,对运动目标分割具有很好的分割效果。     

基于区域分割的小波分解运动补偿编码算法

为了提高视频运动估值的速度和运动矢量编码的效率，提出了一种基于区域分割的小波分解运动补偿的视频编码算法，该算法的几个核心部分的特点如下：（1）运动估值部分，在基本的可变块多分辨率运动估值算法的基础上，充分利用了小波分解后各子带所表征的运动结构相关性，因而提高了运动矢量的编码效率，同时提出了基于区域分割的多分辨率运动估值算法，因而在有效减少运动估值时间消耗的同时，进一步提高了运动矢量的编码效率；（2）在量化部分，提出了一种基于人的视觉系统的量化方法；（3）采用改进的高效的零树小波编码算法对帧内和帧间信息进行编码，实验结果表明，所提出的算法是有效的。     

Simple parallel hierarchical and relaxation algorithms for segmenting noncausal markovian random fields

The modeling and segmentation of images by MRF's (Markov random fields) is treated. These are two-dimensional noncausal Markovian stochastic processes. Two conceptually new algorithms are presented for segmenting textured images into regions in each of which the data are modeled as one of C MRF's. The algorithms are designed to operate in real time when implemented on new parallel computer architectures that can be built with present technology. A doubly stochastic representation is used in image modeling. Here, a Gaussian MRF is used to model textures in visible light and infrared images, and an autobinary (or autoternary, etc.) MRF to model a priori information about the local geometry of textured image regions. For image segmentation, the true texture class regions are treated either as a priori completely unknown or as a realization of a binary (or ternary, etc.) MRF. In the former case, image segmentation is realized as true maximum likelihood estimation. In the latter case, it is realized as true maximum a posteriori likelihood segmentation. In addition to providing a mathematically correct means for introducing geometric structure, the autobinary (or ternary, etc.) MRF can be used in a generative mode to generate image geometries and artificial images, and such simulations constitute a very powerful tool for studying the effects of these models and the appropriate choice of model parameters. The first segmentation algorithm is hierarchical and uses a pyramid-like structure in new ways that exploit the mutual dependencies among disjoint pieces of a textured region.     

Image denoising using statistical model based on quaternion wavelet domain

Image denoising is the basic problem of image processing.Quaternion wavelet transform is a new kind of multiresolution analysis tools.Image via quaternion wavelet transform,wavelet coefficients both in intrascale and in interscale have certain correlations.First,according to the correlation of quaternion wavelet coefficients in interscale,non-Gaussian distribution model is used to model its correlations,and the coefficients are divided into important and unimportance coefficients.Then we use the non-Gaussian distribution model to model the important coefficients and its adjacent coefficients,and utilize the MAP method estimate original image wavelet coefficients from noisy coefficients,so as to achieve the purpose of denoising.Experimental results show that our algorithm outperforms the other classical algorithms in peak signal-to-noise ratio and visual quality.     

A class of discrete multiresolution random fields and its application to image segmentation

In this paper, a class of Random Field model, defined on a multiresolution array is used in the segmentation of gray level and textured images. The novel feature of one form of the model is that it is able to segment images containing unknown numbers of regions, where there may be significant variation of properties within each region. The estimation algorithms used are stochastic, but because of the multiresolution representation, are fast computationally, requiring only a few iterations per pixel to converge to accurate results, with error rates of 1-2 percent across a range of image structures and textures. The addition of a simple boundary process gives accurate results even at low resolutions, and consequently at very low computational cost.     

A wavelet-based image fusion tutorial

The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.     

利用分水岭分割的多分辨率遥感图像融合算法

对全色图像和多光谱图像进行融合可以获得更加清晰的图像信息。提出了一种基于分水岭分割和小波变换的多分辨率图像融合算法。利用正交小波变换得到原图像的小波金字塔表示。对近似图像进行分水岭分割,并且用小波逆变换把原始分割结果逐步映射回更高的分辨率层。通过联合区域分析,得到各层的联合区域分割图,并用此图来指导各层小波系数的融合。对融合系数进行小波逆变换,得到融合的图像。实验结果表明,该法对遥感图像的融合十分有效,能很好地兼顾融合图像的光谱质量和空间清晰度。     

基于多尺度随机模型的SAR图像无监督分割

合成孔径雷达（synthetic aperture radar,SAR）是一种基于相干原理的成像系统,在SAR图像中存在严重影响图像质量的斑点噪声,使得SAR图像的可靠分割非常困难。〖BP）〗根据SAR图像的成像机理,利用两种多尺度随机模型,即多尺度自回归（Multiscale Autoregressive,MAR）模型和多尺度自回归滑动平均（Multiscale Aautoregressive Moving Average, MARMA）模型,分别来描述同一场景不同分辨率SAR图像像素间的统计相关性,并构造了相应的多分辨混合算法实现SAR图像的无监督分割。试验结果表明,提出的两种无监督分割方法是可行的,且MARMA模型比MAR模型能够更精确地捕捉SAR图像多尺度序列中不同类型地形的统计信息,使分割质量具有明显的改进。     

低对比度图像的自适应阈值化算法

在处理光照不均匀的图像分割时用常用的阈值分割方法不能得到良好的分割效果。提出通过小波多分辨力滤波将滤波以后的低通图像作为图像的自适应阈值进行二值化 ,获得了比较理想的效果。该算法在枪支在线OCR识别系统中得到实际应用。     

基于三角网格模型多分辨分解的图像压缩

构造了图像的三角网格模型.基于三角网格模型的多分辨分解,提出了一个新的图像压 缩方法.通过图像的三角网格模型的多分辨分解、压缩与重构,实现了图像的多分辨解、压缩与重 构.构造了一个在小波图像压缩中未曾使用过的小波滤波器组,该小波滤波器组算法具有O(n)运 行时间.实验表明,该方法能获得较好的图像压缩性能.     

图像多尺度统计模型综述

基于小波变换的图像处理方法是目前的主流方法,而对图像特征的多尺度统计建模则是图像压缩、去噪、分割、纹理分析与合成等统计应用的关键问题。本文综述了图像的多尺度统计模型,包括边缘分布模型以及层内、层间和混合相关模型,分析了各模型的优缺点,给出了对各种相关模型捕捉系数间相关性能力的归一化量测。最后,简单介绍了基于多尺度几何分析的统计图像模型,并对多尺度统计建模的前景进行了展望。