一种有效抑制噪音的模糊C均值图像分割算法

提出一种能够有效抑制噪音的模糊C均值聚类算法,通过构造基于灰度-中值的空间信息和塔形结构减少噪音对聚类中心的影响,塔形结构的引入缩短了运算时间,通过自适应地选取隶属度阈值避免人为设定阈值的不灵活性,在图像分割时用中值图像代替源图像消除噪声点。仿真实验表明,该方法更加适合处理受噪音污染的图像,分割结果更加精确。     

基于模糊理论的图像分割方法

在图像信息隐藏研究领域,图像分割技术得到了广泛的应用.常用分割技术主观性较强,对图像的分割不够精确,利用模糊理论对图像进行分割可以有效解决这一问题.首先构造了图像的局部方差矩阵,然后根据模糊理论定义了平滑区、纹理区和边沿区3个模糊集,随后选取了适当的隶属函数并确定了其相关参数,科学、客观的确定了图像的分割阈值,进而实现了对图像的精确分割,最后通过实验数据验证了算法的有效性.     

一种基于多尺度噪声检测的图像中值滤波器

介绍了标准中值滤波与有效中值滤波的概念,提出了一种基于自适应多尺度噪声检测的中值滤波器,可用于恢复被椒盐脉冲噪声污染了的图像。滤波器将输入图像像素分为有效信号类、脉冲噪声类和恒定区域类,对各类像素采用不同的方法进行滤波处理。实验结果证明,本文算法的性能比现存的其它许多算法有了显著的提高,而且便于实现。     

Neuro fuzzy and punctual kriging based filter for image restoration

In this paper, we present a hybrid, image restoration approach. The proposed approach combines the geostatistical interpolation of punctual kriging, artificial neural networks (ANNs), and fuzzy logic based approaches. Images degraded with Gaussian white noise are restored by first utilizing fuzzy logic for selecting pixels that needs kriging. Three fuzzy systems are employed. Both type-I and type-II fuzzy sets in addition with neuro fuzzy classifier (NFC) have been used for the detection of noisy pixels. To avoid edge pixels, a post processing technique is used to check the edge pixel connectivity up to lag 5. If the pixel under consideration is an edge pixel, it is excluded from the fuzzy map and thus not estimated. The concept of punctual kriging is then used to estimate the intensity of a noisy pixel. ANN is employed to minimize the cost function of the kriging based pixel intensity estimation procedure. ANN, in contrast to analytical methodologies, avoids both matrix inversion failure and negative weights problems. Image restoration performance based comparison has been made against adaptive Weiner filter and existing fuzzy kriging approaches. Experimental results using 450 images are used to validate the effectiveness of the proposed approach. Different image quality measures are used to compare the efficacy of the proposed NFC and fuzzy type-II approaches for detecting noisy pixels in conjunction with ANN and kriging based estimation.     

Histogram-based fuzzy filter for image restoration

In this paper, we present a novel approach to the restoration of noise-corrupted image, which is particularly effective at removing highly impulsive noise while preserving image details. This is accomplished through a fuzzy smoothing filter constructed from a set of fuzzy membership functions for which the initial parameters are derived in accordance with input histogram. A principle of conservation in histogram potential is incorporated with input statistics to adjust the initial parameters so as to minimize the discrepancy between a reference intensity and the output of defuzzification process. Similar to median filters (MF), the proposed filter has the benefits that it is simple and it assumes no a priori knowledge of specific input image, yet it shows superior performance over conventional filters (including MF) for the full range of impulsive noise probability. Unlike in many neuro-fuzzy or fuzzy-neuro filters where random strategy is employed to choose initial membership functions for subsequent lengthy training, the proposed filter can achieve satisfactory performance without any training.     

基于模糊理论的低照度彩色图像增强算法

低照度彩色图像存在整体亮度低、对比度差、颜色偏暗和信噪比低等特点,传统图像增强算法对其增强效果非常有限。提出了一种基于模糊理论的低照度彩色图像增强算法,将三原色(red,green,blue,RGB)图像转换成色相饱和度(HSV)图像,以保证增强处理不引起图像的色彩失真。对亮度图像进行非线性变化,实现动态范围展宽;采用修正后的隶属度函数将图像映射到模糊平面,实现对比度增强。实验结果表明:该算法显著地提高了图像整体亮度和对比度,改善了低照度彩色图像的视觉效果。     

一种基于模糊集的灰度图像阈值分割算法

提出了一种新的基于模糊集理论的图像直方图阈值分割算法。该算法首先把图像的直方图预分成目标参考区,背景参考区和模糊区。然后对位于模糊区的灰度级,逐一比较其与目标和背景区的相似度,以决定该灰度级的归属,并最终确定灰度阈值。仿真结果表明：此方法不但能不有效地对灰度图像进行分割,且与传统方法相比更具灵活性。     

结合高维几何空间理论的COSM图像复原算法*

为了在不精确知道图像的点扩展函数的情况下,去除图像的离焦模糊,提出了基于高维空间几何理论的图像复原算法。依据同源连续性原理,通过分析高维空间中向量的方向和点的位置关系来研究模糊图像与原图像的空间关系,并且结合最近邻算法,将该算法应用于去除最近邻算法所得图像的本层模糊。实验表明该算法能够有效地去除离焦模糊。本算法不同于传统的图像复原算法,可广泛应用于模糊图像的复原中。     

基于模糊粗神经网络的图像脉冲噪声滤除

为了获得在图像噪声滤除和细节保留两方面更好的兼顾与平衡,提出了一种兼有模糊化神经元和不可微粗神经元的模糊粗神经网络。采用融合了爬山法的遗传学习算法,增强了网络在学习过程中,特别是进化后期阶段对最优解的局部搜索能力。仿真表明,融合了模糊信息和粗糙集信息处理能力的模糊粗神经网络在图像融合滤波方面比BP网络和粗集神经网络具有更好的性能,是一种性能很好的混合智能神经网络。     

A hybrid image restoration approach: fuzzy logic and directional weighted median based uniform impulse noise removal

In this paper, a hybrid image restoration technique based on fuzzy logic and directional weighted median is presented. The proposed technique consists of noise detection and fuzzy filtering processes to detect and remove uniform (random-valued) impulse noise while preserving the image details efficiently. In order to preserve image details such as edges and texture information, a two-stage robust noise detection is presented in this paper. Pixels detected as noisy by both the noise detection stages are considered for noise removal by the fuzzy filtering process, which utilizes the direction based weighted median to construct fuzzy membership function, which is the main contributing factor in noise removal and detail preservation. Extensive experimentation shows that the proposed technique performs significantly better than state-of-the-art filters based on peak signal-to-noise ratio, structural similarity index measure and subjective evaluation criteria.     

基于后悔理论和证据理论的犹豫模糊决策方法

针对属性值为犹豫模糊信息、属性权重和自然状态发生概率完全未知的多属性群决策问题,考虑决策者心理行为,提出一种基于后悔理论和证据理论的多属性群决策方法。首先,运用证据理论计算各自然状态发生的概率;然后,基于区间模糊矩阵、t-分布估计以及得分函数矩阵确定属性信息的效用值,进而依据后悔理论得到每个自然状态下的感知效用矩阵;通过加权算术平均得到综合感知效用矩阵,并依据方案综合感知效用的大小确定方案优劣排序;最后,将所提方法运用于对投资公司的选择实例中。实验结果表明,虽然所提方法与现有方法得到的决策结果相同,但是所提方法在决策过程中只需考虑较少数量的参数。对比分析实验表明,所提方法得到的决策结果合理、可靠,且能反映实际决策情况。     

基于D-S证据理论的多源图像分割算法

针对多源图像分割问题,提出了一种可靠的分割算法.采用被污染的高斯分布描述待分割图像,MRF模型描述分割图像的先验分布,并利用D-S证据理论融合多源数据的不确定性,依据置信度最大的原则,确定多源图像数据的分割标记类别.用仿真图像和多源遥感图像的分割结果,证明了该算法提供了更准确、可靠的分割结果.     

基于D-S证据理论的融合图像隐写分析

提出一种基于证据权的D-S证据理论的图像隐写分析方法,首先在空域,离散余弦变换（DOT）域和离散小波变换（DWT）域分别提取图像特征并各自进行预分类;然后对各域分类结果进行基本概率分配并进行证据权修正。利用D—S组合规则计算融合概率分配函数,形成最终的决策级融合分类结果．针对典型的隐写方法（如F5,JPHide,Jstego和YASS算法）进行检测,仿真结果显示,所提出的方法能显著提高单分类器的性能．     

基于模糊理论的证据航迹关联算法分析

针对模糊推理航迹关联算法隶属度函数确定较为繁琐、影响算法灵活性等现实问题,将证据理论引入模糊航迹关联算法,对隶属度函数进行加权处理以获取证据信息,并在此基础上进行基于证据理论的融合处理.仿真分析表明,基于模糊理论的证据航迹关联算法具有较强的环境适应性,能够在不大幅增加运算量的情况下进一步降低模糊航迹关联的不确定性,体现了证据理论在解决不确定问题上的优良特性.     

基于模糊证据理论的水质环境状态估计

从信息融合的角度，将湖泊水质环境多因子综合评价过程看成是一个多源数据融合处理与状态估计过程，提出基于相似度的模糊证据理论，评估湖泊水体富营养状况．通过引入模糊集合的相似度，一方面，确定模糊焦元的权值，并修改证据模型；另一方面，确定模糊焦元对类别焦元的信任函数的贡献因子，并建立状态估计与评价模型，使其能够处理湖泊水体富营养状况的突发性带来的某些焦点元素的显著变化问题．对太湖水体富营养状况的评估做了实证分析，并与营养状态指数评价方法进行比较，结果一致，说明基于相似度的模糊证据理论的估计方法可行．     

基于模糊散度理论的图像置乱程度评价研究

提出了基于模糊散度理论的图像置乱程度评价新方法。首先对置乱前后图像中任意像素所对应二阶邻域系统构造一个模糊集并定义其隶属度;其次计算图像中各像素所对应二阶邻域系统模糊集及其补集之间的模糊散度;最后根据置乱前后两图像的模糊散度来构造图像置乱程度评价函数。实验结果表明,提出的评价方法是能够较好地刻画图像的置乱程度,反映了加密次数与置乱程度之间的关系,与人的视觉基本相符。而且对于不同的图像,该评价方法能在一定程度上反映所用的置乱变换在各置乱阶段的效果。     

基于证据理论和前景理论的犹豫-直觉模糊语言多准则决策方法

在犹豫模糊语言和直觉模糊语言的基础上,提出一种新的犹豫-直觉模糊语言集,并定义得分函数、精确函数和Hamming距离公式.针对准则值为犹豫-直觉模糊语言的不确定多准则决策问题,构建基于证据理论和前景理论的拓展型VIKOR方法.该决策方法利用证据理论处理自然状态发生的概率未知的不确定状况,应用前景理论刻画人在做决策时的有限理性行为,使决策结果更能反映实际情况.最后,通过算例表明了所提出方法的有效性和可行性.     

基于证据理论的偏好型直觉模糊群决策方法

针对直觉模糊多属性群决策问题,研究属性和专家权重的确定以及信息的集结方法.利用直觉模糊熵确定属性客观权重,并根据偏好信息确定合理的属性综合权重;在属性层面区分专家权重,将直觉模糊评价值作为Mass函数,构建证据冲突度模型确定专家权重,并利用犹豫度加以修正,避免综合支持度低而对方案排序影响大的专家权重过分削弱;采用证据理论集结决策信息,根据得分值进行方案排序.最后通过算例分析,验证了所提出方法的合理性和有效性.     

Data mining based noise diagnosis and fuzzy filter design for image processing

In image processing, both diagnosis of noise types and filter design are critical. Conventional filtering techniques for image restoration such as median filter and mean filter are not effective in many cases, such as the case lacking the information of noise types or the case having mixed noise in images. This paper develops a data mining approach for noise type diagnosis, and proposes a fuzzy filter design for enhancing the quality of noise corrupted images. The experimental results demonstrate that the proposed technique outperforms the conventional filters, particularly for dealing with the images corrupted by mixed noise with additive Gaussian noise and impulse noise.