改进边缘质量和运动估计的超分辨率图像重构

凸集投影算法（POCS）是一种广泛使用的超分辨率图像重构方法。针对传统的POCS超分辨率图像重构算法出现的边缘模糊及匹配时的局限性问题,首先利用二阶梯度检测出像素周围0°、45°、90°、135°四个边缘。在构造参考帧时采用基于梯度的插值算法,沿边缘方向进行线性插值,沿非边缘方向进行基于一阶梯度的带权插值。在运动估计时,采用SURF匹配算法,提高匹配的鲁棒性和实时性。在修正参考帧时,分别定义中心在四个边缘方向的点扩散函数（PSF）。利用完全参考图像质量评价和无参考图像质量评价分别对仿真实验和实物实验进行了评价,评价结果表明提出的算法较传统POCS算法有明显的改善。      

基于轮廓先验约束的复杂异形工件CT成像方法研究

在X射线CT成像检测系统中,复杂异形工件由于结构复杂,散射、硬化等现象较严重,而且在部分投影角度上,由于在射线透照方向上的有效厚度差异大,固定能量的射线剂量与厚度不匹配,投影数据质量较差,传统的CT重建算法得到的重建图像质量低,边缘模糊,无法获取工件的完整轮廓信息.为此文章研究了一种基于轮廓先验约束的复杂异形工件CT成像方法.首先利用双目立体视觉技术获取工件的轮廓信息,根据双目坐标系与CT坐标系间的空间位置关系完成先验图像的配准;然后将轮廓先验纳入到CT重建过程中并结合TV正则化进行轮廓约束重建.实验结果表明,该方法能够有效地抑制伪影和噪声,保留重建图像的边缘,改善重建图像质量,有助于提高复杂异形工件缺陷检测的可靠性.     

基于视觉感知模型修正的彩色图像边缘检测

在彩色图像边缘检测过程中,准确检测低照度区域的细节边缘是一个难题.提出了一种基于视觉感知模型修正的彩色图像边缘检测方法.由于视觉感知彩色空间中的色调分量具有圆形特性,导致伪边缘问题,利用色调距离计算色调图像的梯度以去除伪边缘,并根据饱和度的非线性函数控制色调分量的梯度,最后利用修正的视觉感知模型计算矢量梯度以获取彩色图像的边缘.实验结果表明,新的彩色边缘检测方法较现有方法能提高彩色图像的边缘检测精度,尤其对于低照度区域的细节边缘具有较好的响应.     

低剂量CT重建中的双边滤波权值优化新方法

为了提升低剂量CT重建图像质量,本文提出了一种基于投影数据恢复导引的双边滤波权值优化方法.新方法有效地将CT投影数据恢复和图像数据恢复的两种低剂量成像策略进行结合,实现优质的低剂量CT重建.具体而言,本文中投影数据恢复采用三维块匹配滤波进行,图像数据恢复采用双边滤波进行,其中双边滤波权值进行优化设计.在双边滤波权值设计...     

基于红外图像特征与K-means的边缘检测

为解决红外图像边缘模糊导致边缘提取困难的问题,提出一种基于边缘特征与K-means结合的红外图像边缘检测方法。首先将人眼视觉特性与红外图像边缘点处的灰度分布特点结合,构造出反映其结构特征的数据集;再利用K-means将数据集分为边缘点和非边缘点,提取出图像边缘;最后利用二步法将边缘进行细化,以便实现红外图像边缘检测。实验结果表明:该方法能够通过自适应阈值提取出红外图像的完整外部轮廓,并保留内部边缘信息,对弱边缘起到良好的提取效果,并有效抑制噪声干扰。     

折/反射全方位视觉空间移变点扩散函数建模与图像复原

在分析折/反射全方位视觉(COV)图像模糊尺度分布规律的基础上提出了一种基于径向边缘检测的空间移变点扩散函数(SV-PSF)建模方法,并且研究了基于一种可以方便地加入空域可变模糊模型等先验知识的凸集投影(POCS)图像复原方法。本文提出的建模方法在有限的径向边界区域建立COV的全局SV-PSF模型,避免了一般建模方法需要大量的边缘检测和图像分割计算;采用基于POCS算法空域图像复原方法,很好容纳了SV-PSF这一先验知识,在实验中与MATLAB的盲反卷积工具箱函数的处理结果进行了对比,结果表明本文方法的灵活性和有效性。     

考虑圆形特征边缘模糊和偏心误差修正的高精度相机标定方法

针对立体视觉系统采用圆形特征点标定时存在的空间圆形投影边缘模糊和偏心现象问题,利用改进Zernike矩和偏心误差修正进行圆心的高精度定位,以此提高相机参数的标定精度。首先考虑了由于立体视觉成像系统的标定场景光照强度不均匀引起的圆形特征投影图像边缘模糊的问题,引入高斯误差函数对边缘过渡段的灰度分布进行描述,建立了高斯边缘模型,并基于该模型计算投影图像的Zernike矩,然后利用改进Zernike矩实现高精度的圆形特征投影边缘像素坐标定位。此外,分析了影响圆形特征中心投影点和拟合圆心间偏差大小的因素,基于该分析对迭代拟合圆心进行偏差补偿使之逼近真实的圆心投影,最后通过所提算法对99圆形标志点进行圆心坐标提取并用于相机参数的标定。仿真实验表明,文中算法对投影图像边缘定位的精度以及圆心拟合的精度均高于传统的算法;实测实验中,基于圆心高精度坐标得到的相机标定参数对标准杆进行三维重建,长度测量精度比传统算法提高了30%。     

Low-Bit-Rate VQ: A Projection Based Approach

In this paper, we present an image decompression scheme that emphasizes visual perception rather than objective signal integrity between the orginal and the reconstructed images. The decoding scheme is developed in the context of vector quantization (VQ). The technique includes a projection based image recovery algorithm as part of the decoding process. The quantized blocks are classified into edge and shade blocks. The edge blocks are filtered using optimized templates while the shade blocks are left unaltered. The method can be considered as a constrained smoothing of edges, which does not cause any degradation to the shade (background) regions in the decoded image. The result is a reconstruction that is subjectively better than a simple look-up table decoding.     

Comments on the filtered backprojection algorithm, rangeconditions, and the pseudoinverse solution

The filtered backprojection (FBP) algorithm is widely used in computed tomography for inverting the two-dimensional Radon transform. In this paper, we analyze the processing of an inconsistent data function by the FBP algorithm (in its continuous form). Specifically, we demonstrate that an image reconstructed using the FBP algorithm can be represented as the sum of a pseudoinverse solution and a residual image generated from an inconsistent component of the measured data. This reveals that, when the original data function is in the range of the Radon transform, the image reconstructed using the FBP algorithm corresponds to the pseudoinverse solution. When the data function is inconsistent, we demonstrate that the FBP algorithm makes use of a nonorthogonal projection of the data function to the range of the Radon transform.     

Automatic Contour Detection Using a "Fixed-Point Hachimura-Kuwahara Filter" for SPECT Attenuation Correction

Attenuation correction for single-photon emission computed tomography (SPECT) usually assumes a uniform attenuation distribution within the body surface contour. Previous methods to estimate this contour have used thresholding of a reconstructed section image. This method is often very sensitive to the selection of a threshold value, especially for nonuniform activity distributions within the body. We have proposed the "fixed-point Hachimura-Kuwahara filter" to extract contour primitives from SPECT images. The Hachimura-Kuwahara filter, which preserves edges but smoothes nonedge regions, is applied repeatedly to identify the invariant set-the fixed-point image-which is unchanged by this nonlinear, two-dimensional filtering operation. This image usually becomes a piecewise constant array. In order to detect the contour, the tracing algorithm based on the minimum distance connection criterion is applied to the extracted contour primitives. This procedure does not require choice of a threshold value in determining the contour. SPECT data from a water-filled elliptical phantom containing three sources was obtained and scattered projections were reconstructed. The automatic edge detection procedure was applied to the scattered window reconstruction, resulting in a reasonable outline of the phantom.     

基于提升小波的自适应阈值边缘检测新算法

针对传统的单一边缘检测算法抗噪能力差、边缘不连续等缺点,本文提出采用两种算法相结合的方式来进行边缘检测。首先,对原始图像进行多层小波分解;对分解后的图像低频部分用提出的改进提升算法进行边缘检测,对高频部分用小波变换的局部模极大值算法检测边缘;通过将各层边缘信息按一定的融合规则融合起来得到一个组合边缘,最后细化图像边缘。实验证明,这种方法相对于传统小波分析有着计算量小,计算速度快和要求存储空间小等诸多优势,同时,也能做到不丢失图像信息,保证了边缘的连续性和封闭性,检测效果较好。     

一种对比度增强的边缘检测改进算法及FPGA实现

传统的Sobel实时图像边缘检测算法对光照条件敏感,因此在进行边缘检测时常会有虚假边缘以及遗漏边缘的现象。将采集到的实时灰度图像进行中值滤波,再进行对比度增强来调整图像灰度值,将进行对比度增强后的图像进行Sobel边缘检测并在FPGA上实现。实验结果表明,采用对比度增强改进的边缘算法检测的边缘更加准确,抑制背景噪声能力更强。     

Signal detection theory and reconstruction algorithms-performancefor images in noise

In many noisy image processing situations, decision making is the ultimate objective. Here, the authors show using signal detection theory how direct optimal processing of the projection data yields a considerable gain in the decision making performance over that obtained by first using image reconstruction. The problem is formulated in the framework of a two hypotheses detection problem. Optimal processors based on the likelihood ratio approach have been presented for two cases. The first considers direct processing of the projection data. The second applies optimal decision theory to the reconstructed data. Results based on computer simulation are presented in the form of receiver operating curves for different signal-to-noise ratios. Early results indicate that large performance gains can be achieved by direct optimal processing of the projection data compared with optimal processing of reconstructed data. Results for the latter case can be interpreted as providing an upper bound on all postreconstruction decision rules. The authors hope to extend this approach to a number of different aspects of the image decision making problem     

基于CP神经网络的边缘检测

边缘检测是图像分析识别必不可少的环节，是一种重要的图像预处理技术。虽然传统的算子算法对边缘的检测速度快，但其得到的往往是断续的，不完整的边缘信息，且这类检测方法对噪声比较敏感，在检测噪声污染图像时会得到许多虚假的边缘。利用CP神经网络对灰度图像的边缘进行检测，但考虑到神经网络训练量过大的问题，先利用传统算子对图像进行边缘处理，将处理后的图像做为神经网络的输入。实验结果表明，该方法得到的边缘图像边界封闭性好，具有较好的抗噪特点。     

应用图像方向和宽度谱检测机场跑道

为有效解决成像制导中的机场跑道检测问题,提出了一种基于方向和宽度谱分析的处理方法.首先归纳了机场跑道形态和灰度特性,进而根据局部灰度特征进行跑道区域预分割,然后以Sobel算子提取边缘信息,再利用局部PCA计算边缘方向,并对各方向值出现频次进行累积,形成方向谱.通过分析方向谱的显著峰信息得到跑道的主方向,在此基础上,确定正交宽度谱的扫描计算模式.结合宽度谱显著峰信息的分析和跑道主方向进行跑道区域的指示和几何宽度值的计算.对机场跑道图像的处理结果显示,文中方法具有较高的检测精度,同时对跑道形状畸变显示出良好的鲁棒性.     

Constrained Fourier space method for compensation of missing data in emission computed tomography

A method is introduced to compensate for missing projection data that can result from gas between detectors or from malfunctioning detectors. This method uses constraints in the Fourier domain to estimate the missing data, thus completing the data set so that the filtered backprojection algorithm can be used to reconstruct artifact-free images. The image reconstructed from estimates using this technique and a data set with gaps is nearly indistinguishable from an image reconstructed from a complete data set without gaps, using a simulated brain phantom.     

基于神经网络的图像弱边缘检测方法研究

主要立足于图像点处理与神经网络相结合的思想,提出了一种基于边缘点特征的BP神经网络图像边缘检测方法.利用提取出的图像边缘特征向量作为训练样本来训练BP神经网络,进一步完成图像边缘的检测.最后,通过实验与传统的边缘检测方法进行了对比,结果证明该方法检测的边缘轮廓清晰,检测速度较快,特别对含有弱边缘的图像能够更好避免漏检和...     

An adaptive color plane interpolation method based on edge detection

In this letter, an adaptive interpolation algorithm based on edge detection is proposed. With this algorithm, all the missing green values can be reconstructed in Bayer pattern image by using edge detection interpolation method. Reconstructed images composed of green pixels are classified according to the high frequency components in image, and the threshold T needed for all kinds of green images in the edge detection is determined through experiments. The edge detection is carried out based on the one Dimensional （1D） gradient operator. If the gradient value is greater than T, this pixel is located on the edge; otherwise the pixel is in the smooth area of the image. Finally, the simple bilinear interpolation is used for the smooth area while the Laplacian interpolation with the second-order correction term is adopted to reconstruct the other red/blue values on the edge. This algorithm resolves effectively the conflicts between reconstructing high quality color image and reducing computational complexity, and thus largely enhances the processing speed for the reconstructed color image.     

Fully 3-D PET reconstruction with system matrix derived from point source measurements

The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space through the system matrix The elements of the system matrix for the clinical Hi-Rez scanner were derived by processing the data measured for a point source at different positions in a portion of the field of view. These measured data included axial compression and azimuthal interleaving of adjacent projections. Measured data were corrected for crystal and geometrical efficiency. Then, a whole system matrix was derived by processing the responses in projection space. Such responses included both geometrical and detection physics components of the system matrix. The response was parameterized to correct for point source location and to smooth for projection noise. The model also accounts for axial compression (span) used on the scanner. The forward projector for iterative reconstruction was constructed using the estimated response parameters. This paper extends our previous work to fully three-dimensional. Experimental data were used to compare images reconstructed by the standard iterative reconstruction software and the one modeling the response function. The results showed that the modeling of the response function improves both spatial resolution and noise properties.     

使用小波变换的图象边缘检测算法

将小波变换理论应用于图象边缘提取,提出了一种新的图象边缘检测算法。一种相对于(c0,c3)=(0.05,0.05)的对称尺度函数被用于二进小波变换,得到原图象的一个多分辨率表达式,再提取图象的边缘特征。实验结果表明,本方法是解决图象边缘提取的一个十分有效的模型。