基于神经网络的MRF纹理图象恢复

本文提出了一种基于平均场神经网络的纹理图象恢复算法，并且利用多状态神经元表达方式，压缩问题解的搜索空间，算法是一个自适应过程，交替地进行模型参数估计和图象恢复，实验结果说明了算法是行之有效的。     

基于纹理基元的图象分割

纹理分割是图象处理的基本问题之一．针对广泛的纹理图象，需要一个高效、鲁棒的分割方法，因此提出了一种基于纹理基元的纹理图象分割算法．首先，以Harr小波为变换工具，得到具有方向性的纹理子图象；然后给出了一种新的纹理基元提取方法，并在此基础上，应用统计方法和矢量场，对纹理区域进行由粗到细的分割．通过这种方法不仅可以对纹理图象进行分割，还可以对同一区域的纹理结构进行描述，从而有利于在这种分割方法基础上，进行更高层次的图象处理．     

噪声和纹理图象的自适应(Adaptive)分割

本文提出了一种自适应的噪声和纹理图象分割算法.观察图象被模拟为由区域过程、映射过程和噪声过程三个层次综合作用构成的.整个算法包括两个独立的步骤:第一步是层次图象模型的参数估计算法,可以处理高斯噪声和出格点(Outlier)的混合噪声情况,因此具有鲁棒性.第二步是基于模型参数的图象分割算法,其核心是一个改进的多值模拟退火技术.计算机模拟实验证明了算法的有效性和鲁棒性.     

基于小波变换的彩色图象数字水印算法

提出了一种基于离散小波变换（DWT）域的彩色图象序列加密数字水印新方法，算法选用了彩色图象RGB色彩空间的G分量嵌入水印，数字水印图象含有密钥信息，因此算法具有很好的安全性。同时利用人类视觉系统（HVS）的亮度掩蔽特性和纹理掩蔽特性，把低频分量根据局部纹理的强弱分成两类，将二值水印图象加密后自适应的嵌入到宿主图象的DWT域的低频分量中，从而很好的兼顾了水印的不可见性与鲁棒性。大量仿真结果表明本算法对于诸如JPEG压缩、高斯噪声干扰和图象剪切具有很好的鲁棒性。     

基于局部直方图相关的造影图象边缘检测方法

造影图象的边缘检测是造影图象的组织或器官分割、测量和分析的基础 .由于造影图象的信噪比低、低电平纹理多 ,而且大量边缘是渐变的小幅度微弱边缘 ,因而其检测一直是造影图象研究与临床应用的重点之一 .针对这一问题 ,提出了一种检测数字造影图象边缘的新方法 .由于图象边缘区域与非边缘区域的局部直方图明显不同 ,因而可以利用这种差别来检测图象的边缘 ,同时还基于局部直方图构造了一种匹配滤波器算法——最大统计相关算法 ,该方法不敏感于图象的噪声和低电平纹理 ,而且能够有效地从噪声和纹理中分离提取造影图象的微弱边缘 .     

基于模糊C均值聚类和统计特性的医学图象检索

本文提出了一种基于灰度、形状和纹理特征的医学图象检索方法.图象被模糊C均值聚类算法预先分割成互相不重叠的子图象,然后对这些子图象分别提取特征,从而获得整幅图象的特征向量.分割后的各子图象和均方差特征描述了原图象的灰度分布情况,二值化后的7个不变矩和7个纹理特征描述了图象的形状和纹理信息.实验结果表明,该算法能够比较有效地应用于基于内容的医学图象检索中,在查全率和查准率上都优于实验中其他两种方法.     

一种基于整体变分的图象修补算法

图象修补是图象恢复研究中的一个重要内容，它的目的是根据图象现有的信息来自动恢复丢失的信息，可以用于旧照片中丢失信息的恢复。由于图象中的边缘代表了图象的重要信息，所以在设计修补算法时，必须着重考虑边缘的恢复，采用整体变分模型设计了一个图象修补算法，整体变分模型能够模拟人的低层视层，在修补图象时可以恢复图象中的边缘，数值实验表明，该模型能够较好地恢复待修补区域的信息，但是受修补区域大小的影响，同时又采用了一种向前传播操作来缩小修补区域。     

基于纹理分析的磁共振图象区域分割

本文介绍了一种用于医学磁共振脑部图象中的主要解剖结构区域分割的纹理分析方法. 利用纹理的二阶统计参数和局部分形维数,组成特征空间后,对象元进行两步分类:首先利用 K平均方法进行二值决策分类,然后采用概率松弛方法获得象元对各类的隶属概率.     

二值图象广义边界编码压缩方法

本文提出一种与游程相关的二值图象广义边界概念和及对编码方法，并给出实现算法的恢复方法，最后通过对几幅二值图象压缩结果与游程编码，二维自适应跳白块编码方法进行比较和分析，表明本文提出了的广义边界编码压缩方法的压缩性能比另两种压缩方法优越，是一种易于实现的高效率的二值图象压缩方法。     

基于静态小波分解的多尺度SAR图像滤波

由于雷达回波的相干性，合成孔径雷达（SAR）图象上存在着斑点噪声，因此，为消除这种噪声，提出了一种基于静态小波分解的硬阈值滤波方法，该方法首先将SAR图象分解至静态小波域，然后在静态小波域中将噪声的小波系数收缩至零，将此算法应用于ERS－1 SAR图象斑点噪声滤波，并与基于Mallat分解的滤波算法和另外3种典型的SAR图象滤波算法进行比较，结果表明，该方法不仅可以有效地去除斑点噪声,并且可以保持SAR图象的精细纹理结构。     

Image relaxation: restoration and feature extraction

The techniques of a posteriori image restoration and iterative image feature extraction are described and compared. Image feature extraction methods known as graduated nonconvexity (GNC); variable conductance diffusion (VCD), anisotropic diffusion, and biased anisotropic diffusion (BAD), which extract edges from noisy images, are compared with a restoration/feature extraction method known as mean field annealing (MFA). All are shown to be performing the same basic operation: image relaxation. This equivalence shows the relationship between energy minimization methods and spatial analysis methods and between their respective parameters of temperature and scale. As a result of the equivalence, VCD is demonstrated to minimize a cost function, and that cost is specified explicitly. Furthermore, operations over scale space are shown to be a method of avoiding local minima     

一种低信噪比图像的模拟退火恢复算法

本文根据马尔可夫(Markov)随机场模型和全局最大后验概率估计技术提出了一种模拟退火图像恢复算法.应用这种算法对混入可加性独立高斯噪声的试验图像进行恢复的实验结果表明,该算法对低信噪比图像数据的恢复处理非常有效.     

基于改进模拟退火的神经网络降质图像恢复

随着科学技术的发展，图像处理技术已经成为科学研究不可或缺的强有力工具，而图像恢复是图像处理中非常重要的一环。传统基于模拟退火算法的神经网络降质图像恢复方法，为了避免退火过程过早收敛，对温度的降低不得不慢慢进行，这样导致算法运行时间太长。采用改进的含回火过程模拟退火算法降温，实验表明该改进算法求解时间比传统的方法有了很大的提高，图像的恢复效果也较令人满意，比传统的逆滤波、维纳滤波方法具有更好的峰值信噪比。     

Figure-ground discrimination: a combinatorial optimization approach

The figure-ground discrimination problem is considered from a combinatorial optimization perspective. A mathematical model encoding the figure-ground discrimination problem that makes explicit a definition of shape based on cocircularity, smoothness, proximity, and contrast is presented. This model consists of building a cost function on the basis of image element interactions. This cost function fits the constraints of an interacting spin system that, in turn, is a well suited physical model that solves hard combinatorial optimization problems. Two combinatorial optimization methods for solving the figure-ground problem, namely mean field annealing, which combines mean field approximation theory and annealing, and microcanonical annealing, are discussed. Mean field annealing may be viewed as a deterministic approximation of stochastic methods such as simulated annealing. The theoretical bases of these methods are described, and the computational models are derived. The efficiencies of mean field annealing, simulated annealing, and microcanonical annealing algorithms are compared. Within the framework of such a comparison, the figure-ground problem may be viewed as a benchmark     

Depth and image recovery using a MRF model

This paper deals with the problem of depth recovery and image restoration from sparse and noisy image data. The image is modeled as a Markov random field and a new energy function is developed to effectively detect discontinuities in highly sparse and noisy images. The model provides an alternative to the use of a line process. Interpolation over missing data sites is first done using local characteristics to obtain initial estimates and then simulated annealing is used to compute the maximum a posteriori (MAP) estimate. A threshold on energy reduction per iteration is used to speed up simulated annealing by avoiding computation that contributes little to the energy minimization. Moreover, a minor modification of the posterior energy function gives improved results for random as well as structured sparsing problems. Results of simulations carried out on real range and intensity images along with details of the simulations are presented     

Evolutionary optimization with Markov random field prior

This work discusses an evolutionary algorithm in which the constituent variables of a solution are modeled by a Markov random field (MRF). We maintain a population of potential solutions at every generation and for each solution a fitness value is calculated. The evolution, however, is not achieved through genetic recombination. Instead, each variable in a solution will be updated by sampling from its probability distribution. According to the MRF prior, local exploitation is encoded in the conditional probabilities. For evolutionary exploration, we estimate the probabilities as fitness-weighted statistics. These two kinds of search are combined smoothly in our algorithm. We compare it with two representative algorithms [iterated conditional modes (ICM) and simulated annealing (SA)] on noisy and textured image segmentation. Remarkable performance is observed.     

Unsupervised texture segmentation in a deterministic annealingframework

We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a data clustering problem based on sparse proximity data. Dissimilarities of pairs of textured regions are computed from a multiscale Gabor filter image representation. We discuss and compare a class of clustering objective functions which is systematically derived from invariance principles. As a general optimization framework, we propose deterministic annealing based on a mean-field approximation. The canonical way to derive clustering algorithms within this framework as well as an efficient implementation of mean-field annealing and the closely related Gibbs sampler are presented. We apply both annealing variants to Brodatz-like microtexture mixtures and real-word images     

医学内窥镜图象的遗传恢复算法

遗传算法是一种软计算方法,它作为一种新的全局优化搜索算法用于图象处理,是一种具有发展潜力的智能信息处理方法,对于利用遗传算法处理灰度图象来说,由于其数据量和计算量很大,并且传统遗传算法存在“过早收敛”的问题,使得图象恢复质量不理想。该文针对医学内窥镜摄取的图象纹理和病灶特征,设计了一种新的二维染色体编码方法,并将模拟退火和遗传算法相结合的算法引入灰度图象恢复处理过程中的遗传操作,实验结果表明,该方     

Mean field annealing using compound Gauss-Markov random fields foredge detection and image estimation

The authors consider the problem of edge detection and image estimation in nonstationary images corrupted by additive Gaussian noise. The noise-free image is represented using the compound Gauss-Markov random field developed by F.C. Jeng and J.W. Woods (1990), and the problem of image estimation and edge detection is posed as a maximum a posteriori estimation problem. Since the a posteriori probability function is nonconvex, computationally intensive stochastic relaxation algorithms are normally required. A deterministic relaxation method based on mean field annealing with a compound Gauss-Markov random (CGMRF) field model is proposed. The authors present a set of iterative equations for the mean values of the intensity and both horizontal and vertical line processes with or without taking into account some interaction between them. The relationship between this technique and two other methods is considered. Edge detection and image estimation results on several noisy images are included.