改进型抗混叠轮廓波的图像超分辨率重建

针对Contourlet变换中存在的频谱混叠现象,采用了基于抗混叠轮廓波变换的算法进行图像超分辨率重建.该算法首先用抗混叠塔式滤波器组替换掉Contourlet变换中的拉普拉斯塔式变换,对图像进行尺度变换;然后,根据尺度变换后不同尺度的高频子带之间的相似性,对相关高频分量用双三次插值做高频外推相似变换,再通过方向滤波器...     

基于图像自相似性及字典学习的超分辨率重建算法

图像超分辨率重建技术在重构图像细节,改善图像视觉效果等方面起着重要作用.为了提高超分辨率图像的重构质量,本文结合图像自身和自然图像库信息进行超分辨率重建.先利用图像在不同尺度的自相似性,形成图像金字塔,只用单幅低分辨率图像进行超分辨率重建;然后利用自然图像库进行字典学习并以初步得到的重建图像作为输入再次处理;在图像后处理时,利用图像非局部相似性和迭代反投影,进一步提高重建效果.实验结果表明,本文的方法与其它几种基于学习的超分辨率算法比较,无论主观视觉效果上还是峰值信噪比上都有明显提高.     

基于分块的自适应超分辨率算法

本文主要研究序列图像超分辨率重建技术.本文提出了一种新的基于分块策略的超分辨率算法,由于采用分块策略,矩阵规模极大减小,且其规模与分块大小相关,而与原始序列大小无关,故能有效降低系统内存开销,提高系统处理效率.实验表明,传统多帧算法对运动估计的精度要求很高,而本文算法对此要求不高,在采用低精度运动估计技术的条件下,本文...     

基于图像处理的外螺纹三维模型重构

提出了一种基于多角度序列图像特征实现外螺纹的三维模型重建的方法。首先在旋转平台上采集多角度序列螺纹件图像,然后对每帧图像进行特征点提取,将序列图像的特征点进行三维变换和插值,最终生成三维模型。实验结果表明,此算法能精确高精度地实现外螺纹三维模型重构。     

形态学和小波域杂波抑制的微弱目标检测

本文提出了一种基于形态学和小波域杂波抑制的微弱目标检测方法,该方法将图像序列进行形态擘tophat滤波,然后小波变换,再分别对各小波子带作平滑滤波,按各子带对滤波前后小波系数作差分运算,最后经过小波逆变换得到具有微弱目标的残差图像序列.用残差图像tophat结果估计目标潜在区域,在目标潜在域的约束下,对残差图像序列进行时空域数据融合,实现微弱运动目标的检测.仿真实验表明,该方法杂波抑制后残差图像具有很好的白高斯特性,且目标邻域信杂比(scNR)的平均增益比图像空域平滑滤波和图像频域低通滤波等典型运算的SCNR平均增益有明显改善,目标检测算法在5帧图像集成时能稳定检测出微弱运动目标轨迹.     

摄像中的超分辨率图像重建算法研究

超分辨率图像重建技术就是利用信号处理的方法,从多幅低分辨率图像中提取更多细节信息,重建出一幅或多幅高分辨率图像的技术。文章介绍了超分辨率图像重建的概念,并且探讨超分辨率图像重建的意义和需求,然后着重研究了目前的几种主要超分辨率图像重建算法,并讨论了关于目前超分辨率图像重建算法的一些思考。     

基于视觉对比度特性的红外图像超分辨率重建

针对现有超分辨率算法重建后的红外图像存在对比度差、信噪比低、视觉效果模糊的缺点,提出一种基于视觉对比度特性的红外图像超分辨率重建算法。该算法首先利用人眼在不同灰度级的分辨能力不同,通过引入红外图像对比度这一先验信息重建红外图像,其次构建一噪声度量因子以区分图像目标与噪声,然后对目标边缘进行增强,噪声进行滤除。实验结果表明:经过改进算法重建的超分辨率红外图像对比度提高了2倍,噪声得到了有效抑制,视觉效果明显改善。     

稀疏表示图像超分辨率重建的自适应方法

提出了一种参数自适应的图像超分辨率重建方法.在基于稀疏表示的图像超分辨率重建的经典算法模型框架下,正则化参数可以根据每个图像补丁本身情况自适应地确定,从而克服了人为选择参数且所有补丁参数需一致的缺点,因此使图像重建效果得到提升.实验结果表明,我们所提方法在不同尺寸扩大因子和噪声环境下都优于人工确定参数的情形,三种评价指标均表明所提方法是有效的.     

基于自相似性与稀疏表示的超分辨率算法

目的为了解决当前稀疏表示的超分辨率算法效果依赖参与训练的数据的问题,结合图像的自相似性,提出一种基于自相似性与稀疏表示相结合的超分辨率算法。方法算法利用图像的多维自相似性,构建多维图像金字塔,采用改进的相似块搜索策略,得到对应的高低分辨率图像块作为训练样本,然后对样本进行字典训练,最后根据稀疏表示得到超分辨率图像。结果实验结果显示,文中算法在峰值信噪比(PSNR)和结构相似度(SSIM)上优于其他算法,对于实验图像而言,PSNR平均提升了0.5 dB。结论提出的超分辨率算法未引入外部数据库,具有较好的效果,能够用于超分辨率重建。     

一种基于自样本学习的人脸图像超分辨率复原算法

针对人脸图像超分辨率复原问题,提出了一种新的基于自样本学习的超分辨率复原算法.该算法从输入图像本身提取训练样本库,并采用矢量量化的方法对训练样本进行分类.再利用并行设计的多类预测器对每类样本进行学习训练,指导高频信息的估计重建.对来自输入图像本身的自样本训练集合和来自特定训练图像库的特定训练样本集合进行了对比研究.实验结果表明提出算法在图像重建质量和实现速度上都有很好的表现.     

3-D ultrasound volume reconstruction using the direct frame interpolation method

A new method for 3-D ultrasound volume reconstruction using tracked freehand 3-D ultrasound is proposed. The method is based on solving the forward volume reconstruction problem using direct interpolation of high-resolution ultrasound B-mode image frames. A series of ultrasound B-mode image frames (an image series) is acquired using the freehand scanning technique and position sensing via optical tracking equipment. The proposed algorithm creates additional intermediate image frames by directly interpolating between two or more adjacent image frames of the original image series. The target volume is filled using the original frames in combination with the additionally constructed frames. Compared with conventional volume reconstruction methods, no additional filling of empty voxels or holes within the volume is required, because the whole extent of the volume is defined by the arrangement of the original and the additionally constructed B-mode image frames. The proposed direct frame interpolation (DFI) method was tested on two different data sets acquired while scanning the head and neck region of different patients. The first data set consisted of eight B-mode 2-D frame sets acquired under optimal laboratory conditions. The second data set consisted of 73 image series acquired during a clinical study. Sample volumes were reconstructed for all 81 image series using the proposed DFI method with four different interpolation orders, as well as with the pixel nearest-neighbor method using three different interpolation neighborhoods. In addition, volumes based on a reduced number of image frames were reconstructed for comparison of the different methods' accuracy and robustness in reconstructing image data that lies between the original image frames. The DFI method is based on a forward approach making use of a priori information about the position and shape of the B-mode image frames (e.g., masking information) to optimize the reconstruction procedure and to reduce computation times and memory requirements. The method is straightforward, independent of additional input or parameters, and uses the high-resolution B-mode image frames instead of usually lower-resolution voxel information for interpolation. The DFI method can be considered as a valuable alternative to conventional 3-D ultrasound reconstruction methods based on pixel or voxel nearest-neighbor approaches, offering better quality and competitive reconstruction time.     

基于不同分色原理的图像传感器类别及其应用特点

首先本文分析了以拜尔传感器为代表的滤色片阵传感器的分色原理和性能特点,介绍了基于像素插补的图像重建方式,然后介绍了无需像素重建的条形滤色片分色和利用硅晶体透射特性分色的两种图像传感器。最后介绍的是利用光学棱镜进行分色处理的3CCD及3CMOS传感器的主要性能特点。     

High-resolution restoration of dynamic image sequences

We investigated an approach to reconstructing high-resolution images from dynamic image sequences using local spectral analysis. High-resolution reconstruction from linearly shifted multiple static image frames has previously been studied, in which the aliasing relationship between the spectrum of the original signal and the DFTs of shifted and sampled signals is exploited. In the high-resolution reconstruction of dynamic image sequences, difficulties occur as a result of nonuniform shifts in the frames. We use loca spectral analysis to achieve high-resolution reconstruction by overlapped block decomposition and motion compensation. For each block image in a reference frame in the sequence, motion estimation and subpixel registrations are performed against adjacent frames. High resolution reconstruction is performed on such motion-compensated block-image sequences. For some blocks containing motion boundaries, high resolution reconstruction is difficult; a new scene emerges or disappears producing inconsistent information. An interpolation technique is used in such blocks to generate the number of pixels consistent with other high-resolution blocks. The flower-garden image sequence is used for the computer simulations. The quality of the restored images are very encouraging; the aliasing effects in the original frames are significantly reduced and sharper edges are produced. The overall procedure to generate such higher-resolution images from a dynamic image sequence is described in detail. The result can be applied to various image sequence restoration applications including up-conversion of conventional video signals.©1994 John Wiley & Sons Inc     

利用二维插值核的高分辨力图像重构

利用连续图像采样及其内插公式,提出了一种基于二维插值核重建高分辨力图像的新算法。该算法先使一般低分辨力序列图像具有标准位移关系,再根据高、低分辨力图像之间的关系重构高分辨力图像。同时利用正则化技术导出了一套重复迭代算法,以提高算法的处理效果和克服病态问题。实验表明,该算法使图像的细节和清晰度大大增强,分辨力大幅度提高,明显优于零阶保持插值算法的处理结果,其峰值信噪比提高8-9dB。     

基于背景图像的视频图像压缩感知重构关键技术研究

目的针对视频序列相邻帧图像之间的相关性,提出了基于帧间差分的背景图像的视频图像重构算法。方法利用前2帧的重构图像求得背景图像,作为下一帧图像重构的先验知识。结果明显提高了重构图像的质量,同时也减少了重构图像所需时间。结论将帧间差分法与压缩感知重构算法相结合得到的视频重构算法,在视频图像重构的质量和速度上都有明显优势。     

基于颜色矢量角度直方图与DCT压缩的鲁棒哈希算法

目的为了改善哈希序列对篡改内容的正确识别率和鲁棒性,提出基于颜色矢量角度直方图耦合离散余弦变换压缩的鲁棒哈希算法。方法结合插值运算和Gaussian滤波器,对图像实施预处理,使其对于任意的认证目标均可产生一个固定长度的哈希序列;引入极坐标变换LPT(Log-polar transform),对规范尺寸图像完成坐标变换,输出二次图像;借助SVD(SingularValueDecompostion)方法来处理二次图像,输出全局鲁棒特征;提取规范尺寸图像中所有像素分量的颜色矢量角度,并计算其对应的直方图,形成色彩感知特征;将全局与色彩感知特征组合,将其视为1D哈希序列;基于离散余弦变换DCT(Discrete Cosine Transform),对1D哈希序列实施压缩,输出一组交流系数;引入Logistic映射,根据其输出的混沌序列来设计差异加密方法,对交流系数实施扩散,从而形成目标哈希序列。基于l2范数距离和优化的识别阈值,对待检测图像内容的真伪实施判别。结果试验结果显示,相对于已有的哈希机制而言,所提方案具备更强的鲁棒性,对各类几何攻击均有更高的检测准确度。结论所提哈希方法具有较高的鲁棒性与识别正确率,对商标检索、信息防伪等领域具有较好的参考价值。     

A Method of Obtaining Catchment Basins with Contour Lines for Foam Image Segmentation

Foam image segmentation, represented by watershed algorithm, is wildly used in the extraction of bubble morphology features. H-minima transformation was proved to be effective in locating the catchment basins in the traditional watershed segmentation method. To further improve the accuracy of watershed segmentation, method of top-bottom-cap filters and method of morphological reconstruction were implied to marking the catchment basins. In this paper, instead of H-minima transformation, a method of contour lines is specially proposed to obtain the catchment basins for foam image segmentation by using top-bottom-cap filters and less morphological reconstruction. Experimental results in foam segmentation show that the proposed method is equally accurate but more efficient than the method of H-minima plus morphological reconstruction, and equally efficient but more accurate than the method of H-minima plus top-bottom-cap filters.     

基于卷积神经网络的超分辨率失真控制图像重构研究

目的 解决超分辨率图像重构模型中存在的功能单元之间关联性差,图像色度特征提取完整性不强、超分辨率重构失真控制和采样过程残差控制偏弱等问题。方法 通过在卷积神经网络模型引入双激活函数,提高模型中各功能单元之间的兼容连接性;引用密集连接卷积神经网络构建超分辨率失真控制单元,分别实现对4个色度分量进行卷积补偿运算;将残差插值函数应用于上采样单元中,使用深度反投影网络规则实现超分辨率色度特征插值运算。结果 设计的模型集联了内部多个卷积核,实现了超分辨率色度失真补偿,使用了统一的处理权值,确保了整个模型内部组成单元的有机融合。结论 相关实验结果验证了本文图像重构模型具有良好可靠性、稳定性和高效性。     

基于小波的改进加权抛物线插值的图像超分辨率算法

为了尽可能地保持图像的基本信息,提高图像的视觉效果和空间分辨率,提出一种基于小波的改进加权抛物线插值算法,即在传统的加权抛物线算法上增加插值的误差补偿项。利用sobel算子设定插值点的边缘方向,得到初始放大图像。利用小波变换提取高频成份,原始图像幅值增强充当低频部分,再经过小波逆变换得到高分辨率图像。实验结果表明,相对于传统的图像放大算法,该算法考虑到全局相关性,得到更加清晰的边缘信息。