基于局部显著性与梯度L_0范数的红外图像保边平滑算法

在红外图像预处理过程中,为有效保护图像的显著边缘信息,提出了一种融合全局梯度L_0范数和局部显著性的保边平滑算法。首先,对红外图像进行拉普拉斯变换,采用嵌套矩形框快速估计边缘局部显著性,并利用均值与方差设计权重函数;其次,依据权重调节逼近项中每个像素的重要性,约束全局梯度的L_0范数,建立模型;最后通过交替方向乘子法对模型求解,平滑红外图像,保护显著的边缘信息。实验结果表明,与其他保边平滑算法相比,所提算法能在平滑图像的同时,有效保护显著边缘,为进一步应用提供了理论支持。     

复杂纹理背景下的红外图像目标提取

纹理特征是红外图像中的重要因素之一.提出了一种复杂纹理背景下的红外目标提取算法.由于均值漂移算法是一种非参数密度估计算法,在图像分割中得到了广泛应用,所以首先通过均值漂移算法对红外图像进行平滑处理,以消除噪声对图像质量的影响;然后对平滑后的红外图像的像素进行均值漂移图像分割,并结合8邻域差值聚类法提取出红外目标前景信息.与传统的纹理处理方法相比,该算法可剔除纹理背景而保留非纹理目标,无监督性和适应性较好,在军事领域具有一定的应用价值.     

基于L曲线调参的FOTV-ADMM超分辨率图像重建

基于图像高频细节的重构问题,建立了全变分(Total Variation,TV)约束重建模型,选取交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)求解分析.TV-ADMM重建算法能够保持图像边缘信息,纹理细节的刻画却不够理想,图像平滑部分的重建出现阶梯效应和过平滑现象.为此,提出使用分数阶约束的模型算法FOTV-ADMM求解.该算法对图像纹理高频细节重建效果较好,能锐化图像边缘区域,同时为降低经验调节参数对图像重建的影响,减少调节参数的时间,引入L曲线调节参数,找出了正则化参数最优解.实验结果表明,基于L曲线调参的FOTV-ADMM算法能够更好地保留图像的纹理和平滑部分的细节特征,在峰值信噪比和结构相似度评价指标上,FOTV对高频细节的重建改善效果更佳.     

数字图像预处理算法在彩色年轮图像分割中的应用

本文以分水岭算法为切入点,根据彩色年轮图像的纹理特征,自适应的抑制由于年轮图像表面不平滑以及细微的纹理变化所产生的过分割问题,同时去除了图像中的背景信息,也就是年轮的早材部分。从处理的最终结果可以看出,应用本文图像预处理方法可以很好的保留原始图像中的纹理信息,也就是年轮的晚材部分,同时去除了图像中的大部分噪声,获得了清晰的边缘,为后续对年轮图像信息的进一步提取打下了良好的基础。     

一种基于正则化的边缘定向插值算法

针对传统的基于线性回归模型插值算法不能对变化剧烈的边缘进行有效插值的问题,该文提出一种基于正则化的边缘定向插值算法。算法主要分为两部分:参数估计部分与数据估计部分。在参数估计部分,为了更加准确地描述图像局部结构,把已估计的高分辨率像素作为训练像素的一部分,用以进行回归模型参数的估计。在数据估计部分,引入像素平滑方向作为正则化项,以降低参数的误估计引起的数据估计偏差。实验结果表明,该算法能很好地保持图像的边缘特征,尤其在变化比较剧烈的边缘区域;与双三次插值算法及基于正则化的局部线性回归插值算法(Regularized Local Linear Regression, RLLR)相比,该算法能取得更好的视觉效果及较高的PSNR值。     

一种基于多重分形的SAR图像边缘检测方法

分形维数只能刻画那些具有理想的自相似性的分形体,现实中的许多纹理并不满足这一条件,因此单一的分形维数并不足以描述和刻画SAR图像的纹理,多重分形维数更适合于描述图像的纹理.通过计算原始SAR图像离散点数据的奇异性指数,然后对应每一点奇异性指数计算全局多重分形奇异谱,根据判决准则区分边缘和纹理可以实现SAR图像的边缘检测,实验结果表明,基于多重分形特征的边缘检测算法能够检测到许多局部细节,同时又避免出现不重要的细节,突出了主要的边缘信息,很好地区分出SAR图像的纹理和边缘.     

基于熵正则L0梯度最小化模型的图像平滑方法

图像平滑是计算机图像和视觉领域中的一项基本任务,L0梯度最小化模型是该领域效果较好的图像平滑处理方法之一.但是该方法存在着严重的阶梯效应,且缺乏对噪声的鲁棒性.为了克服这些缺点,本文提出了一种基于熵正则的L0梯度最小化模型的图像平滑方法.首先,采用快速局部均值滤波算法预处理图像,并将处理后的图像运用到L0梯度最小化模型中,以此减少噪声点对图像平滑的影响;然后,为更好地刻画处理后图像与原始图像的相似度,保护其边缘信息,引入熵因子作为模型正则项,以减轻阶梯效应对图像平滑效果的影响;最后,运用交替迭代寻优方法,求解能量函数的最优解,继而得到最终平滑图像.为验证所提方法的有效性,利用大量图像进行实验,实验结果表明:与L0梯度最小化模型、RTV模型、DTV模型、Super-pixel L0模型相比,所提模型能够获得更好的平滑效果的同时,较好地克服阶梯效应,且对噪声的鲁棒性也有一定程度的提高.     

地形图像制备中的分形和小波变换应用

为了制备高分辨率的地形图，建立了地形的分形模型，然后进行插值处理。经多次插值后，平滑效应显著，地形的纹理特征逐渐丧失。文中提出一种小波变换分层处理的方法，有效地克服了单纯分形插值的平滑效应，在保持地形纹理特征的基础上，制得高分辨率的地形图。     

融合局部三值数量和色度信息的均值漂移跟踪

该文提出了局部三值数量(Local Ternary Number, LTN)这一新的局部显著性纹理算子,并将其与色度信息相结合得到一种新的目标跟踪方法。该方法充分利用目标像素与其八邻域像素灰度值的大小关系,将局部显著性算子 (Local Similarity Number, LSN)加以拓展,设计了局部三值数量这一新的局部显著性纹理算子,该算子能区分目标像素在同一局部显著度下的不同纹理结构;LTN掩膜提取边缘、线和角点上关键像素以提高纹理特征的区分能力,同时能够较完整地保留目标信息;在此基础上,将掩膜内目标像素的LTN特征与色度信息融合生成一种新的目标模型,并嵌入到均值漂移(Mean Shift, MS)框架完成目标的跟踪。实验结果表明,该文提出的目标跟踪方法在场景中存在相似颜色和光照变化干扰的情况下,仍能持续准确地实现目标的定位,提高了传统均值漂移跟踪算法的性能。     

基于改进的区域纹理谱描述符的图像检索

提出了区域纹理谱描述符的增强算法,新描述符在考虑局部邻域像素灰度差异符号变化的基础上,又融入了灰度差异的幅度变化以及局部邻域灰度均值与图像全局灰度均值的关系,从而更好地描述了局部区域纹理信息。为证明新描述符的分辨能力,文中采用不同图像库进行图像检索对比实验,实验结果证明新描述符的性能得到了较大程度的提高。     

Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation.

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.     

基于整体变分和变换域分析的复合去噪模型

本文在泛函空间理论基础上提出了一种整体变分与小波阈值萎缩复合的图像去噪模型。复合模型在小空间规整化约束下实现整体变分优化去噪,保持图像边缘特征减弱值阶跃现象;复合模型处理图像中不存在Gibbs现象且可应用于大噪声图像恢复缺失信息。DCT变换是一种表征图像纹理特征的变换域处理方式,本文最后将DCT变换引入复合模型,得到保持纹理特征的复合去噪模型。理论和实验证明了本文提出的模型在图像去噪中的有效性。     

图像的区域描述方法及运动估计

文中提出一种任意形状区域的图像描述方法进行图像序列的运动估计。首先,对初始帧图像采用保边界平滑,然后,对平滑帧进行分水岭分割。根据标记图像,将图像以区域邻接图的方式进行存储。为解决过度分割的问题,给出了区域的灰度特性和边界强度及区域尺寸相结合的融合准则。最后,每个区域采用仿射运动模型及非线性最小均方法进行参数优化。得到的运动矢量场同基于像素点的估计方法相比具有更高的一致性和抗干扰性,运动补偿差图像的峰值信噪比得以提高。     

Medical X-Ray Image Enhancement Based on Kramer''''s PDE Model

The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation (PDE) model, Kramer’s PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer’s PDE model. The study suggests that the Kramer’s PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.     

Medical X-Ray Image Enhancement Based on Kramer's PDE Model

The purpose of this study is to present an application of a novel enhancement technique for enhancing medical images generated from X-rays. The method presented in this study is based on a nonlinear partial differential equation (PDE) model, Kramer's PDE model. The usefulness of this method is investigated by experimental results. We apply this method to a medical X-ray image. For comparison, the X-ray image is also processed using classic Perona-Malik PDE model and Catte PDE model. Although the Perona-Malik model and Catte PDE model could also enhance the image, the quality of the enhanced images is considerably inferior compared with the enhanced image using Kramer's PDE model. The study suggests that the Kramer's PDE model is capable of enhancing medical X-ray images, which will make the X-ray images more reliable.     

基于平稳小波变换的图像去噪方法

针对传统正交小波变换在图像去噪时存在的边缘失真,提出了一种基于平稳小波变换的图像去噪方法。使用系数关联法将图像小波分解后的高频分量像素标记为噪声和边缘,如果小波系数被标记为边缘,则保持其系数不变,否则采用基于邻域的方法进行系数收缩。当噪声方差较大时,收缩后最小尺度的高频分量中会存在一些孤立的亮点或暗点,借助次大尺度高频分量将其去除,对处理后的小波系数进行平稳小波反变换得到去噪图像。实验结果表明,本文方法能够在去除噪声的同时较好地保持图像的边缘,是一种有效的图像去噪方法。     

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.     

基于Curvelet的纹理方向自适应图像插值

为提高纹理复杂图像的插值放大质量,提出一种纹理方向自适应的图像插值算法。首先利用Curvelet变换提取图像的4个方向因子矩阵,然后对不同类插值点选择相应的2个方向因子构造权重系数进行线性插值,从而自适应地重建各类待插值点。分析比较了本文与现有插值算法对平滑与纹理复杂区域的插值重建质量。实验结果表明,本文算法能有效抑制传统插值方法重建图像时出现的边缘模糊和锯齿现象,重构的图像效果优于传统方法,纹理丰富图像的重构质量可提高2dB以上。     

遥感图像中无水桥梁识别新算法

 给出了边缘密度和线段复杂度的定义,并提出一种遥感图像中无水桥梁的识别新算法.首先对图像进行边缘提取,计算像素点的边缘密度,根据边缘密度进行图像分割,接着采用Hough变换提取直线,利用线段复杂度等确定疑似桥梁区域,然后计算疑似桥梁区域像素点的纹理特征,并构成一个特征矢量,最后将此特征矢量送入BPNN进行分类,统计该区域所有像素点的分类结果以判决是否属于桥梁.实验结果表明,该算法能够较好地识别出遥感图像中的无水桥梁目标.     

Edge-based image restoration.

In this paper, we propose a new image inpainting algorithm that relies on explicit edge information. The edge information is used both for the reconstruction of a skeleton image structure in the missing areas, as well as for guiding the interpolation that follows. The structure reconstruction part exploits different properties of the edges, such as the colors of the objects they separate, an estimate of how well one edge continues into another one, and the spatial order of the edges with respect to each other. In order to preserve both sharp and smooth edges, the areas delimited by the recovered structure are interpolated independently, and the process is guided by the direction of the nearby edges. The novelty of our approach lies primarily in exploiting explicitly the constraint enforced by the numerical interpretation of the sequential order of edges, as well as in the pixel filling method which takes into account the proximity and direction of edges. Extensive experiments are carried out in order to validate and compare the algorithm both quantitatively and qualitatively. They show the advantages of our algorithm and its readily application to real world cases.