一种基于假彩色的像素级多传感器图像融合算法

本文给出了一种基于假彩色的像素级多传感器图像融合算法,并将其用于两幅灰度图像的融合中.这种算法将现有的图像融合技术和彩色显示技术相结合,在灰度融合图像的基础上利用色差来表现原图像与灰度融合图像的细节差异.该算法分为三步:首先,用选择与平均相结合的方法得到两幅原图像的灰度融合图像;接着,求出灰度融合图像与两幅原图像的差异图像;最后,将两幅差异图像和灰度融合图像分别送至R、G、B颜色通道进行显示.比起灰度融合图像,最终得到的彩色融合图像在色彩上更丰富,包含更多的细节,直觉上更容易辨认.     

一种基于映射阈值的图像增强方法

针对灰度变换中映射范围难以确定的问题,提出了一种利用映射阈值进行图像增强的新方法.在直方图灰度的映射变换过程中,自动搜索目标区和背景区峰值间的最低点,并将其作为阈值进行关于图像灰度范围的映射,以调整图像的灰度覆盖范围,从而实现图像的亮度和对比度增强.实验分析表明,该方法能够调整图像增强后的灰度动态范围,取得比常规直方图灰度变换更好的增强效果.新方法解决了映射变换中的灰度范围确定问题,是一种有效的图像增强方法.     

基于直方图信息和灰度偏移网格的数字减影图像灰度矫正算法

本文首次提出一种利用数字减影图像直方图信息和灰度偏移网格进行灰度矫正的新的矫正算法.该算法能够有效去除灰度失真带来的蒙盈片灰度级差异,使减影图像丢失的细节信息得以重现.通过分析减影图像直方图,提取其中的灰度偏移量信息,有效消除了减影图像中的灰度级噪声.首次提出灰度偏移网格,较好地解决了数字减影图像灰度矫正过程中减影质量与矫正速度的矛盾.     

基于灰度冗余的红外图像实时均衡技术

文章提出一种新型的基于灰度冗余的红外图像实时均衡算法,通过图像灰度直方图的统计,将灰度像素分布为零的灰度级进行完全压缩,将灰度分布很少的灰度级映射到最近的大于阈值的灰度级,然后将有效图像灰度级进行等间距排列,最后完成排列后的直方图映射.该算法在压缩灰度冗余的同时,进行直方图的实时无损均衡,提高了图像质量,并实现实时处理,避免了常规直方图均衡带来的灰度断层现象.该算法在红外热像仪的应用中取得了较好效果.     

基于GLCM算法的图像纹理特征分析

深入研究灰度共生矩阵(GLCM,Gray Level Co-occurrence Matrix)算法,说明基于灰度共生矩阵的14个纹理特征具体意义,指出纹理特征之间存在冗余性。通过对纹理图像的灰度共生矩阵的计算分析和纹理特征提取实验,表明灰度共生矩阵能够反应图像的特点,与纹理特征描述图像的特点相对应,同时,图像的14个纹理特征之间存在一定程度的冗余,实际中可以根据图像纹理特征的差异,选择几个显著的纹理特征对图像进行分类。纹理特征分析和实验结果对图像纹理特征的应用具有普遍的指导意义。     

一种改进的QR码图像二值化算法

对光照不均的QR码图像进行全局二值化处理后,会出现全白或全黑的误差区域,在局部二值化过程中会出现伪边界情况,并且计算时间也会变长。针对光照不均的QR码图像提出了一种改进的基于背景灰度的二值化算法。首先,根据二维码源图像大小进行分块处理,使用灰度估算公式对分块的灰度值进行计算。其次,使用联合插值算法产生背景灰度水平图像,然后用背景灰度水平图像替代源图像得到校正图像。最后,采用Ostu算法对校正图像进行二值化。实验结果表明,该算法能有效的校正光照不均的QR图像,并得到一个良好的二值化图像。     

基于改进直方图均衡的红外图像对比度增强算法

针对红外图像灰度分布集中、对比度低的特征,提出了一种基于改进直方图均衡的对比度增强算法。首先采用线性对比度增强将原始16位红外图像映射到8位图像A;然后采用改进的平台直方图均衡将原始16位红外图像映射到8位图像B;再根据输入图像的灰度级范围动态确定映射图像A和B的权值;最后以确定的权值将映射图像A和B合并,得到最终对比度增强的图像。该方法克服了传统平台直方图均衡算法噪声过大及亮度突变的缺点,动态结合了传统的灰度变换增强算法,能根据全图目标与背景灰度的分布情况自适应调整对比度。实验表明,该算法在增强目标对比度的同时有效保留了图像的整体信息,改善了视觉效果。     

基于小波重构和灰度分段的红外图像放大增强

传统的内插算法在放大红外图像时都存在着一定的缺陷,提出了一种基于小波重构和灰度分段变换的图像放大新算法.该算法先对原始图像进行小波变换获得高频系数,运用牛顿插值算法放大高频系数作为放大图像的高频成份,再将原始图像作为低频成份,进行小波重构,可得放大图像.为了增强放大图像,将图像按双灰度闽值分割成对应目标的灰度值高段、对应背景的灰度值低段和对应过渡区域的灰度值中段等3个部分,对各部分采用不同的线性变换,获得最佳的视觉效果.实验证明该方法在图像细节方面具有很好的放大效果.     

Data compression for check processing machines

A technique of compressing image data derived from personal checks which possess several gray levels is described. Check images consist of both essential information such as printed and handwritten characters and nonessential background pattern or picture. Only the character plane is to be coded. Our proposed technique is divided into two phases: character plane extraction and character plane coding. In the first phase, a character plane which is composed of character pels on a uniform background is extracted from an original digital check image by using a combination of fundamental techniques of image segmentation. In the second phase, the extracted character plane is separated into a bit plane and a gray-level plane. The bit plane which preserves the position information of character pels on the character plane is conditional entropy coded. An adaptive two- or one-dimensional predictive coding scheme is applied to the gray-level plane which consists of only the character pels on the character plane. The check data are stored for further use as a combination of the codes derived from the bit plane encoder and the gray-level encoder in a check processing machine. A comparative study shows that the proposed coding scheme performs much better than conventional predictive coding schemes. For 8 gray-level image data, a compression factor of about 8:1 has been achieved.     

A new optimal digital halftoning technique based on the discrete cosine transform

Digital halftoning is a technique to display a gray-level image with a bilevel device. Conventionally, most halftoning techniques are done in the spatial domain. A new halftoning technique based on the discrete cosine transform is proposed. The method chooses an optimal bilevel image to display the original gray-level image and minimize the weighted mean square error based on the discrete cosine transform domain. The simulation results indicate that our algorithm can produce very good halftoned images without false contours.     

Inverse halftoning via MAP estimation

There has been a tremendous amount of research in the area of image halftoning, where the goal has been to find the most visually accurate representation given a limited palette of gray levels (often just two, black and white). This paper focuses on the inverse problem, that of finding efficient techniques for reconstructing high-quality continuous-tone images from their halftoned versions. The proposed algorithms are based on a maximum a posteriori (MAP) estimation criteria using a Markov random field (MRF) model for the prior image distribution. Image estimates obtained with the proposed model accurately reconstruct both the smooth regions of the image and the discontinuities along image edges. Algorithms are developed and example gray-level reconstructions are presented generated from both dithered and error-diffused halftone originals. Application of the technique to the problems of rescreening and the processing of halftone images are shown.     

一种新的基于边缘拟合的图像配准方法

为了克服罔像配准中计算效率与稳定性的两难问题,提出一种新的基于边缘特征的图像自动配准算法.利用小波变换对图像进行多尺度边缘检测,提高边缘榆测的稳定性;用直线拟合边缘,并根据几何特件、灰度特性为直线分配可信度;以直线对作为最小配准单位,利用马氏距离计算直线对之间的匹配程度;配准过程采用加权投票算法确定变换参数,每次投票根据直线对之间的匹配程度及各直线可信度拥有不同的权重.实验表明木文算法快速稳定,在没有任何先验知识的情况下,能够有效的配准存在相似变换关系(平移、旋转和尺度)的图像.     

一种基于曲线拟合提取干涉条纹中心点的新方法

介绍一种提取干涉条纹中心点的边界法向曲线拟合法。根据干涉条纹灰度分布规律，首先对干涉图像依次进行二值化处理和边缘提取，对所得到的条纹边缘进行曲线拟合，计算边缘上各点法线方向；再根据条纹灰度余弦分布特点，对法线方向上的条纹灰度数据进行最小二乘法拟合，求出极值点位置；进而获得条纹中心点坐标。该方法具有精度高、抗干扰的特点，不仅适用于平行直线型条纹图像，而且对含闭合条纹的复杂干涉图像同样适用。     

SAR图像的自动分割方法研究

由于存在相干斑噪声的影响,给SAR图像分割造成很大的困难,该文提出了一种SAR图像的自动分割方法。首先在特征提取阶段,通过计算小波能量提取纹理信息,用邻域统计量提取灰度信息,用保边缘平均灰度提取边缘信息,以确保边缘准确。然后提出一种改进的完全无监督的聚类算法进行图像分割,该算法可以自动确定分割的类型数目。由于该方法充分考虑了SAR图像的纹理、灰度和边缘信息,因而极大地提高了其最终分割性能。实验结果证明了该方法的有效性。     

A Realistic Computer-Simulated Brain Phantom for Evaluation of PET Charactenstics

To evaluate accurately the imaging characteristics of positron emission tomography (PET), a realistic computer-simulated brain phantom was developed. A cross-sectional slice from a human cadaver brain was chosen for its combination of gray matter, white matter, and cerebrospinal fluid (CSF) regions. The slice was photographed and digitized into a gray-level image with a video digitizer, boundary edges were located around cerebral structures in the digitized image, and each structural region was assigned a uniform pixel value dependent on both the cerebral parameter (e.g., blood flow, oxygen uptake, metabolic rate) under investigation and the type of structure (gray matter, white matter, CSF). Line integrals through the regions were generated at various angular and transverse positions according to specific physical characteristics (such as detector line-spread function) of a tomographic scanner configuration to create a set of simulated but realistic projection measurements. The set of projection measurements can be processed with any standard reconstruction program to create a tomographic image to reveal the effects of various PET characteristics. Investigations with this computer-simulated brain phantom have demonstrated its usefulness for examining the interrelations among neuroanatomical structure volume, tomographic spatial resolution, partial volume effect, and nonlinear parameter estimation. Transportability of the simulated phantom and the procedure to other medical imaging environments is described, and limitations of this simulation procedure are discussed.     

Improvement of Chest Region CT Images through Automated Gray-Level Remapping

A software system, CTIP, written in Fortran is discussed which remaps the computerized tomography (CT) image gray level so that both the lung and heart regions are clearly visible with a "natural anatomical appearance." The system is adaptive to image statistics derived from the gray-level histogram of the entire image. Unprocessed, conventional remap, and processed images were compared by 22 radiologists, with 82 percent preferring the processed images. All of the respondents agreed that the processed images possessed a "more anatomical" appearance than the conventional remapping approach.     

一种像素级多传感器图像融合算法的研究

该文给出了一种像素级多传感器图像融合算法,并将其应用于不同波段SAR图像的融合。该算法先用基于卡尔曼滤波的图像融合技术得到一幅灰度融合图像,再用不同的RGB颜色通道分别表示灰度融合图像以及原图像中增强后的细节,最终得到一幅彩色融合图像。仿真结果表明,用此算法得到的彩色融合图像包含更多的细节,可辨识性强。     

Adaptive image matching via spatial varying gray-level correction

This paper deals with the adaptive image matching and displacement estimation problems of dissimilar sequential images. Adaptive matching may be referred to a kind of functional optimization. Based on the additive measuring model for sequential images with gray-level deviation and the finite element technique, a class of gray-level correction model and robust matching criterion function are proposed, and an adaptive hierarchical searching algorithm is studied, which can automatically evaluate the temporal and spatial gray-level deviation existed in sequential images and filter out the interference thereof to increase the probability of correct estimation of displacement. Experimental results for the estimation of displacement fields with translation and/or small angle rotation in medical digital subtraction angiography (DSA) imaging and aerial remote sensing pictures are presented     

联合多通道均衡与多尺度融合的水下图像增强方法

针对不同场景水下退化图像出现的颜色失真和低 对比度等问题,提出了一种联合多通 道均衡与多尺度融合的水下图像增强方法。首先利用绿通道补偿红通道并通过区域红通道 均值二次加强红通道,其次利用自适应平台直方图均衡扩展R、G、B 3通道灰度级范围并 重新分配灰度值,最后将经过Gamma 、GUM 和CLAHE 算法处理的亮度V(Value )分量进 行多尺度融合。实验结果表明,本文算法简单而有效,能够明显改善图像雾化现象和蓝绿 外观,增强图像清晰度,且在多种场景下均表现出较强的泛化适应能力。     

实拍运动模糊图像的退化参数估计与复原

针对实拍运动模糊图像的复原问题,分析了实拍图像与仿真模糊图像的特征差异,提出一种适用于实拍图像退化参数估计的方法.首先计算退化图像的倒谱,选用倒谱灰度极小值的绝对值作为阈值把倒谱变为二值图像,再去除中心的十字亮线;然后用点到直线的距离公式计算出二值图像的亮条纹方向,即得运动模糊方向;最后以退化图像中心为旋转轴,将运动模糊方向旋转至水平方向,用差分自相关法计算模糊尺度.把估计的点扩散函数(PSF)代入维纳滤波算法复原实拍图像,复原效果证明参数估计结果正确.