区域指导的自适应图像插值算法

提出了一种以图像区域的一致性为指导的自适应选择插值公式(简称ADA)的图像插值算法.首先将图像分割成不同区域,然后结合近邻法和众数法确定待插值点所属区域,将待插值点分类处理:对于区域内的点,采用线性插值;对于区域间的过渡点,设计非线性插值公式,分配较大的权值给与待插值点属于同一区域的邻域像素,分配较小的权值给其它邻域像素.实验表明,ADA图像插值算法得到的插值图像有较好的主观图像质量,其峰值信噪比(PSNR)比传统方法平均提高了3.11～4.39 dB.     

改进的邻域滤波图像修复方法

在分析了几种重要的基于滤波图像修复算法基础上,提出了一种改进的邻域滤波图像修复算法.为了减少平滑处理中的模糊,该算法根据待修复点与窗口中已知点的欧氏距离来决定相应位置的权值,然后采用邻域中已知像素的加权平均来估算待修复点的值.大量实验结果表明,改进算法修复的视觉效果和归一化均方误差(NMSE)的计算结果均明显优于先前的...     

一种水下退化图像的增强方法

针对水下退化图像的特点,提出了一种以邻域像素的增量特征为指导的插值增强方法,根据不同的增量特征判断待插值点的插值类型,再对不同插值类型分别设计插值算法,最后对插值图像进行修正增强.实验结果表明了方法的有效性.     

基于互信息的亚像素级立体视觉点匹配方法研究

在立体视觉测量中,为获得更高精度,往往需要将视差计算精确到亚像素级。将互信息理论引入双目图像配准并结合多分辨率技术实现亚像素级点匹配。采用Bouguet立体校正算法对左右图像进行极线校正,利用Harris角点探测器检测目标并将获取角点作为待匹配点,采用最大互相关法进行搜索确定像素级匹配点。然后对以左右匹配点为中心的20×20邻域图像进行插值并分别放大10倍和100倍,采用互信息方法先对低分辨率图像进行配准,再在高分辨率图像上进一步细化求精,结合像素级匹配的整数视差可得最终亚像素级视差。实验结果表明,该方法能将视差精度提高到0.01像素。     

基于Otsu阈值分割的边缘快速图像插值算法

为了满足视频监控、目标检测与识别过程中较高图像质量和较低算法复杂度要求,以及改善传统图像插值中细节模糊和边缘锯齿效应,文中提出一种基于Otsu阈值分割的边缘快速图像插值算法。利用Otsu算法,根据目标区域和背景区域的类方差最大,确定分割阈值,对非边缘区域进行双线性插值,边缘区域利用与待插值点周围6个或8个相邻降采样像素局部结构的多方向特点,自适应估计高分辨率像素值。实验表明,该算法运算复杂度低,很好保持了图像的边缘,获得了视觉质量较好的高分辨率图像。     

利用颜色滤波矩阵估计原图的拼接定位盲取证方法

图像拼接是伪造数字图像最基本的操作,对其的被动盲检测和定位也是数字图像取证亟待解决的关键问题之一。该文通过对图像进行颜色滤波矩阵插值,对图像进行了原图估计,重建其像素邻域一致性。利用待取证图像的边缘点在像素邻域一致性上偏离估计的原始图像的程度,对图像的拼接进行准确定位。实验表明,该方法能够有效地检测图像拼接伪造区域的位置、大小和形状。     

基于采样抠图和自适应颜色的图像合成算法

针对图像合成结果易受颜色取样和权值选取影响等问题,本文借鉴梯度域和线性alpha合成算法的优点,提出了一种基于采样抠图和自适应颜色权值的图像合成算法。算法首先通过增加数据项约束和邻域限制对原始抠图方法得到的alpha matte值进行局部迭代修正,根据得到的alpha matte值对合成区域进行划分,不同的合成区域采用不同的合成策略,边缘区域采用梯度域合成方法对边界进行无缝对接和颜色保真,同时利用均值插值重建方程求解,降低求解复杂度;内部区域通过亮度对比系数计算自适应颜色权值,并用其进行内部区域颜色再平衡,保证源图像和目标图像的完美融合。仿真实验表明,该算法与其他合成算法相比,在无缝边界的同时,颜色保证处理更为有效。     

极坐标图像的机场目标特征提取方法

提出了一种机场场面监视雷达图像的机场目标特征提取方法。首先,为了解决雷达图像的亮点噪声问题,采用类形态学的图像预处理方法;然后,采用邻域链路法标记目标的轮廓像素;最后,单元面积作为权值,估计极坐标图像下机场目标的特征,并对权值的计算方法进行了讨论。实验表明,所提方法能够消除亮点噪声,得到比较准确的特征信息,为目标分类识别和多目标跟踪提供正确的特征。     

改进邻域平均法在MRI图像去噪处理中的研究

对磁共振成像图像的去噪声处理是医学图像预处理中的重要环节。为了克服均值滤波算法不能兼顾去噪和保持图像边缘细节的不足,提出了改进邻域平均法。先根据相似者相容的原理得到待像素与各模板的关系,从而判断该像素是否为孤立噪声点。然后对孤立噪声点用灰度相近T邻域平均法进行处理,并对非孤立噪声点采用加权平滑模板进行处理。实验结果表明,该算法在去除噪声的同时,还保留了图像的边缘和细节。     

一种快速的亚像素图像配准算法

图像超分辨率重建是在现有红外探测器基础上提升空间分辨率的一种有效方法。超分辨率图像重建是利用一组相互之间存在亚像素位移的低分辨率图像构造出一幅高分辨率的图像,快速、高精度估计图像间的位移是其关键技术之一。提出了一种用于超分辨率重建的亚像素配准算法,算法由特征检测、像素级配准和亚像素级配准三个处理过程组成。在特征检测过程,首先采用梯度算子对图像进行边缘检测,然后对边缘点进行角点预检测,排除非角点像素点,之后再进行Harris角点检测,大大减少了计算量;在像素级配准过程,用NCC算法进行像素级配准,用统计方法去除误匹配点对;在亚像素级配准过程,先对像素级匹配点的邻域进行插值放大,再进行亚像素匹配,误匹配点剔除,相对偏移量计算。对提出的算法进行了仿真实验,结果显示本算法的速度较类似算法速度有较大的提高。     

EDGE-DIRECTED INTERPOLATION BASED ON SHIFTED-LINEAR IMAGE MODEL

This paper presents a hybrid image interpolation algorithm to keep details and edges simultaneously. The basic idea is to separate the unknown pixels into two classes and estimate them in different way. One class of unknown pixels is obtained via shifted linear interpolation and the other class through statistical signal processing method. The merit of this hybrid algorithm is that each unknown pixel can be estimated through original pixels simultaneously. Simulation results demonstrate that this hybrid interpolation algorithm improves the quality of the interpolated images over conventional interpolation methods.     

Reversible data embedding for high quality images using interpolation and reference pixel distribution mechanism

This paper proposes a reversible data hiding method based on image interpolation and the detection of smooth and complex regions in the cover images. A binary image that represents the locations of reference pixels is constructed according the local image activity. In complex regions, more reference pixels are chosen and, thus, fewer pixels are used for embedding, which reduces the image degradation. On the other hand, in smooth regions, less reference pixels are chosen, which increases the embedding capacity without introducing significant distortion. Pixels are interpolated according to the constructed binary image, and the interpolation errors are then used to embed data through histogram shifting. The pixel values in the cover image are modified one grayscale unit at most to ensure that a high quality stego image can be produced. The experimental results show that the proposed method provides better image quality and embedding capacity compared with prior works.     

Morphological Image Enlargements

This paper presents a method based in mathematical morphology to enlarge images. It does not make the low pass assumption which is common to all linear interpolation methods and which does not often hold for images. Pixels in smooth areas are properly interpolated by linear methods while those at the edges are not. The method begins with a linear interpolation and a gradient computation. The gradient serves as a measure of confidence about the linear interpolation. Then, the proposed algorithm processes the pixels in a certain order: first pixels with high confidence (smooth zones) of the image and those with a low one (edges) at the end. By doing so, it preserves both slow variations and sharp edges. The method can be applied to other image processing problems, such as edge enhancement or motion vector estimation, where there is an image and confidence information about each pixel.     

An optimum interpolation method applied to the resampling of NOAAAVHRR data

Two main problems must be solved in the geometric processing of satellite data: geometric registration and resampling. When the data must be geometrically registered over a reference map, and particularly when the output pixel size is not the same as the original pixel size, the quality of the resampling can determine the quality of the output, not only in the visual appearance of the image, but also in the numerically interpolated values when used in multitemporal or multisensor studies. The “optimum” interpolation algorithm for AVHRR data is defined over a 6×6 window in order to: consider overlapping effects among adjacent pixels. The response for each new pixel R(x, y) is determined as a linear combination of the response R _i(x_iy_i) of the surrounding pixels in the window (i=1,36). The weighting coefficients μ_i are calculated from the ground projection of the effective spatial response function for each AVHRR pixel, taking into account the particular viewing angle and geometry of the pixels on the ground. This method is intended to give an optimal interpolation of AVHRR scenes along all the scanline, in order to compensate for off-nadir radiometric alterations associated to the varying spatial resolution and the blurring introduced by the pixel overlaps. The optimum method, as mathematically defined, is highly expensive in CPU time. Then, a big effort is necessary to implement the algorithms so that they could be operationally applied. Two approaches are considered: a general numerical method and a pseudo-analytical approximation. A Landsat TM image corresponding to the same date of the AVHRR image is used to test the quality of the radiometric interpolation procedure     

Pixel feature-weighted scale-adaptive object tracking algorithm

An effective object tracking method using weighted pixel features was proposed to deal with all kinds of complicated tracking situations,such as target movement,rotation,background interference and scaling and so on.First,the color feature and location information of the pixels in the target area were used to build the object model.Then the average weight image was used to estimate the scale variation coefficient.The aim was to adapt to the scale changes of the target.Finally,an update model was proposed,which was able to renew the object model and background model.The experimental results show that the proposed algorithm could make full use of the differences between pixels in the target area,so it can track more quickly and more effectively with strong robustness.     

Shape-based interpolation of multidimensional grey-level images

Shape-based interpolation as applied to binary images causes the interpolation process to be influenced by the shape of the object. It accomplishes this by first applying a distance transform to the data. This results in the creation of a grey-level data set in which the value at each point represents the minimum distance from that point to the surface of the object. (By convention, points inside the object are assigned positive values; points outside are assigned negative values.) This distance transformed data set is then interpolated using linear or higher-order interpolation and is then thresholded at a distance value of zero to produce the interpolated binary data set. Here, the authors describe a new method that extends shape-based interpolation to grey-level input data sets. This generalization consists of first lifting the n-dimensional (n-D) image data to represent it as a surface, or equivalently as a binary image, in an (n+1)-dimensional [(n+1)-D] space. The binary shape-based method is then applied to this image to create an (n+1)-D binary interpolated image. Finally, this image is collapsed (inverse of lifting) to create the n-D interpolated grey-level data set. The authors have conducted several evaluation studies involving patient computed tomography (CT) and magnetic resonance (MR) data as well as mathematical phantoms. They all indicate that the new method produces more accurate results than commonly used grey-level linear interpolation methods, although at the cost of increased computation.     

基于差分进化的插值置换混叠图像盲分离

针对插值置换混叠图像提出了一套完整的盲分离方案。根据插值图像像素点之间存在的相关性,通过对置换混叠图像分块进行有限差分,检测插值图像与原图像经过有限差分后的值之间的差距,设定适当的阈值,将其分开。为获得最优阈值,用差分进化算法进行优化,获得最优阈值。根据阈值将图像二值化,进而将置换图像分离出来。实验结果表明,该方法比阈值法的鲁棒性强,对经过不同插值方式的置换混叠图像进行盲分离都有较好的效果。     

数字图像自适应插值法

文章分析了数字图像插值中最常用的双线形插值和立方卷积插值,并提出了一个根据 映射点邻域原图像的复杂程度,自适应地调节插值权值的数字图像插值方法。     

An edge-guided image interpolation algorithm via directional filtering and data fusion.

Preserving edge structures is a challenge to image interpolation algorithms that reconstruct a high-resolution image from a low-resolution counterpart. We propose a new edge-guided nonlinear interpolation technique through directional filtering and data fusion. For a pixel to be interpolated, two observation sets are defined in two orthogonal directions, and each set produces an estimate of the pixel value. These directional estimates, modeled as different noisy measurements of the missing pixel are fused by the linear minimum mean square-error estimation (LMMSE) technique into a more robust estimate, using the statistics of the two observation sets. We also present a simplified version of the LMMSE-based interpolation algorithm to reduce computational cost without sacrificing much the interpolation performance. Experiments show that the new interpolation techniques can preserve edge sharpness and reduce ringing artifacts.