一种运动自适应3D去隔行算法及其硬件实现

去隔行算法是电视扫描格式转换和数字视频处理的一项关键技术，近年来得到了广泛而深入的研究。文章提出一种基于运动检测的自适应时空滤波去隔行算法，运用数学形态学的方法对于运动检测进行后处理，提高了运动检测的准确度，很好的消除了运动图像的“锯齿”效应。同时与基于块匹配的运动补偿去隔行算法相比，易于硬件实现。     

一种提升电视图像质量的自适应去隔行算法

提出了一种用于提升电视图像质量的自适应去隔行算法。该算法中,待重建像素先分别在时域与空域进行重建,然后综合处理得到最终的重建值。空域滤波部分采用基于边缘自适应的方法,能较理想地解决运动图像的边缘锯齿等效应;时域滤波部分则基于运动自适应的方法,有效地提高了运动检测的准确性。测试结果表明,该算法实现较简单,且能有效改善去隔行后的电视图像质量。     

一种改进的自适应分数阶积分图像去噪算法

为了改善图像去噪的效果,提出一种基于分数阶积分和中值滤波的改进自适应图像去噪算法,首先利用自适应中值滤波算法(Ranked-order Based Adaptive Median Filter,RAMF)中的噪声判别条件来检测噪声点,然后利用"噪声边缘"判别函数对其中的可疑噪声点进行二次检测,同时根据图像的局部统计信息和结构特征构造自适应的分数阶阶次,最后将检测出的噪声点进行自适应的分数阶积分滤波去噪。与传统的分数阶积分去噪算法相比,该自适应算法有效地保留了被错误误去除的图像边缘点,并且实现了分数阶积分的阶次自适应化,在去除噪声的同时很好地保留了图像的边缘及纹理细节信息。     

一种新的运动自适应去隔行算法

针对隔行视频,提出了一种新的运动自适应去隔行算法,该算法能充分利用图像序列时间和空间上的相关信息,采用新颖的运动检测方法区分视频序列中的运动和静止部分。对于运动部分,使用新的自适应插值算法;对于静止部分,采用行复制算法进行处理。实验表明,该算法与一些传统的去隔行算法相比,在画面视觉效果上,能达到更好的效果。     

Novel De-interlacing Algorithm Based on Motion-adaptive Detection

针对隔行视频,提出了一种新的运动自适应去隔行算法,该算法能充分利用图像序列时间和空间上的相关信息,采用新颖的运动检测方法区分视频序列中的运动和静止部分.对于运动部分,使用新的自适应插值算法;对于静止部分,采用行复制算法进行处理.实验表明,该算法与一些传统的去隔行算法相比,在画面视觉效果上,能达到更好的效果.     

基于噪声检测的自适应中值滤波算法

针对现有中值滤波算法对于高密度噪声图像以及纹理细腻图像的边缘处理能力欠佳的缺陷,提出一种基于噪声检测的自适应中值滤波算法.新算法根据噪声点与周围信息的关联程度将噪声点滤波值进行调整,从而更好的处理图像的细节部份.新算法中的自适应策略加强了滤波算法的去噪性能,使其对于含有任意噪声密度的图像也能很好的进行噪声滤除.通过仿真分析,新算法对于细节丰富的图像以及高密度噪声的图像滤波效果良好,有效的提高图像的峰值信噪比,其去噪效果相比其他方法更加优秀.     

基于PCNN噪声检测的两级脉冲噪声滤波算法

为有效滤除图像中严重脉冲噪声干扰,提出了一种基于改进型脉冲耦合神经网络(PCNN)噪声检测的两级脉冲噪声滤除算法。该算法首先利用PCNN同步脉冲发放特性区分定位噪声点和信号点位置,其次根据噪声点局部邻域信息对噪声进行第1级自适应滤波,然后再利用具有保护边缘细节特点的多方向信息中值滤波器(MF进行第2级辅助滤波。实验结果表明,该算法在噪声检测中无需设定检测阈值,噪声检测精度较高;在去噪过程中不但有效滤除噪声干扰,而且能很好地保护图像边缘细节等信息,具有较好的主观视觉效果和客观评价指标,比传统MF及其它相关算法有更优的滤波性能,去噪能力强、信噪比高和适应性好,特别是对受严重噪声污染的图像,显示了更大的优越性。     

一种快速去除高密度椒盐噪声的滤波算法

为了更好地恢复被高密度椒盐噪声污染的图像,在传统的自适应中值滤波算法的基础上提出了一种改进的自适应滤波算法。该算法将3×3矩形滤波窗口内极值点视为可疑噪声点,对可疑噪声点自适应调节滤波窗口大小进一步判断是否为噪声点;将噪声点区分为低密度噪声区噪声点和高密度噪声区噪声点,并分别用改进后的中值滤波算法、自适应修正后均值滤波算法处理,信号点保持不变。仿真结果表明,该算法处理速度快并且能够有效恢复被椒盐噪声(密度达80%)污染的图像,在去噪的同时能够很好地保护图像的细节。     

一种用于消除红外图像噪声的自适应加权滤波算法

针对带有高斯噪声和椒盐噪声两种混合噪声的红外图像,提出了一种自适应加权混合去噪算法。该算法首先通过邻域像素的灰度差值来判断像素噪声的类别,然后对高斯噪声采用自适应加权均值滤波法滤除,对椒盐噪声采用自适应加权中值滤波算法滤除。实验表明,该方法优于传统均值滤波算法和中值滤波算法,能同时消除混合噪声,并具有较好的保护图像细节的能力。     

一种运动自适应去隔行算法及硬件实现

去隔行算法是电视扫描格式转换和数字视频处理的一项关键技术.本文分析了现有的各种去隔行算法,在此基础上,采用了一种运动自适应去隔行算法.该算法通过运动检测将图像中的像素进行分类,针对不同类型的像素点自适应的采取不同的插值算法,提高了图像的质量.同时与基于块匹配的运动补偿去隔行算法相比,易于硬件实现.     

基于目标匹配的视频对象跟踪算法

视频对象分割算法的性能好坏将直接影响MPEG-4编码产品的质量。连续两次差分后自适应处理，对差分图像取交集获得运动对象的边界，形态学处理后获得二值分割掩模进而提取运动目标。基于改进的Hausdorff距离度量法对后续帧中视频对象进行跟踪。实验结果证明，该方法能够从背景不变的图像序列中较好的提取出运动对象，具有较强的鲁棒性。     

岛岸背景下的红外运动多目标跟踪

提出了一种基于粒子滤波和FCM的岛岸背景条件下红外运动多目标跟踪方法.通过对红外序列图像进行中值滤波、边缘提取、海天线检测和形态滤波等预处理,提取出若干个候选目标.然后利用基于粒子滤波和FCM的算法实现对候选目标的数据关联和连续跟踪.实验结果表明,该方法具有良好的可行性和效果.     

Moving window-based double Haar wavelet transform for image processing.

Image denoising is a lively research field. The classical nonlinear filters used for image denoising, such as median filter, are based on a local analysis of the pixels within a moving window. Recently, the research of image denoising has been focused on the wavelet domain. Compared to the classical nonlinear filters, it is based on a global multiscale analysis of images. Apparently, the wavelet transform can be embedded in a moving window. Thus, a moving window-based local multiscale analysis is obtained. In this paper, based on the Haar wavelet, a class of nonorthogonal multi-channel filter bank with its corresponding wavelet shrinkage called Lee shrinkage is derived. As a special case of this filter bank, the double Haar wavelet transform is introduced. Examples show that it is suitable for a moving window-based local multiscale analysis used for image denoising, edge detection, and edge enhancement.     

An Edge‐Based Adaptive Method for Removing High‐Density Impulsive Noise from an Image While Preserving Edges

This paper presents an algorithm for removing high‐density impulsive noise that generates some serious distortions in edge regions of an image. Although many works have been presented to reduce edge distortions, these existing methods cannot sufficiently restore distorted edges in images with large amounts of impulsive noise. To solve this problem, this paper proposes a method using connected lines extracted from a binarized image, which segments an image into uniform and edge regions. For uniform regions, the existing simple adaptive median filter is applied to remove impulsive noise, and, for edge regions, a prediction filter and a line‐weighted median filter using the connected lines are proposed. Simulation results show that the proposed method provides much better performance in restoring distorted edges than existing methods provide. When noise content is more than 20 percent, existing algorithms result in severe edge distortions, while the proposed algorithm can reconstruct edge regions similar to those of the original image.     

Detecting wide lines using isotropic nonlinear filtering.

Lines provide important information in images, and line detection is crucial in many applications. However, most of the existing algorithms focus only on the extraction of line positions, ignoring line thickness. This paper presents a novel wide line detector using an isotropic nonlinear filter. Unlike most existing edge and line detectors which use directional derivatives, our proposed wide line detector applies a nonlinear filter to extract a line completely without any derivative. The detector is based on the isotropic responses via circular masks. A general scheme for the analysis of the robustness of the proposed wide line detector is introduced and the dynamic selection of parameters is developed. In addition, this paper investigates the relationship between the size of circular masks and the width of detected lines. A sequence of tests has been conducted on a variety of image samples and our experimental results demonstrate the feasibility and effectiveness of the proposed method.     

Directional low-pass filtering for improved accuracy and reproducibility of stenosis quantification in coronary arteriograms

Considers the quantification of percent diameter stenosis in digital coronary arteriograms of low spatial resolution. To improve accuracy and reproducibility an edge-preserving smoothing method, called the directional low-pass filter (DLF), was developed to suppress quantum noise by averaging image intensity in a direction parallel to the vessel border. Accuracy of stenosis quantification was assessed by using stenosis phantoms. The standard error of the estimate (SEE) was 0.76 pixel-length (p) without spatial filtering and further reduced to 0.50 p by DLF; the average deviation as a measure of the regularity of border definition was also reduced by DLF from 1.00 to 0.68 p (n=50, P<0.001). It was shown that the DLF outperformed the conventional moving average filter and median filter. Reproducibility in terms of intraframe variability was assessed by using coronary arteriograms obtained from 10 patients. Intraframe variability of the percent stenosis measurements was reduced from 3.5% to 2.9% by DLF (n=10, P<0.005). An analysis of variance showed, however, that the interframe variability cannot be reduced by any of the spatial filters under investigation. The result of this study has provided a guideline for angiographically based quantification of percent stenosis under limited imaging resolution and suggests a new method for improving accuracy and reproducibility by directional low-pass filtering.     

视频运动目标跟踪系统设计

文设计了一种可应用于静止背景下进行动态视频目标跟踪的系统。首先通过CCD摄像头采集动态图像序列,然后通过图像格式转换、中值滤波、边缘检测以及匹配跟踪对采集到的动态图像序列进行处理,最后得出运动目标的位置信息。本文所设计的系统与传统的基于PC机的视频运动目标跟踪系统相比,具有更快的图像处理速度和更高的实时性。     

Multilevel nonlinear filters for edge detection and noisesuppression

Two new classes of multilevel nonlinear filters are introduced for simultaneous edge detection and noise suppression, which the authors call a nested median filter/median averaging filter (NMF/MAF) pair and a delayed decision filter/embedded median trimmed filter (DDF/EMTF) pair. Median filters and median-related filter cause an edge shift in the presence of an impulse near the edge. The proposed filters reduce such edge shifting while suppressing impulsive as well as nonimpulsive noise. It is shown that at the noisy edge point the NMF and the DDF are substantially superior both theoretically and experimentally to the median filter, the α-TM filter, and the STM filter in two respects: (1) the output bias error and (2) the output mean square error. It is also shown that in the noisy homogeneous region (nonedge point), the bias errors of the MAF are zero and the output mean square errors of the MAF are substantially close to those of the optimized single-level filters: the averager, the median filter, and the min-max filter under Gaussian, Laplacian, and uniform noise, respectively. Test results confirm that the NMF/MAF pair and the DDF/EMTF structure are each robust in preserving sharp edges, inhibiting edge shifting, and suppressing a wide variety of noise     

An adaptive temporal-spatial filter for MPEG decoded video signals

A new adaptive post-processing algorithm for the MPEG decoded video sequences is proposed. We use a motion compensated averaging filter to reduce the noises in the temporal domain and an adaptive spatial filter to remove noise in the spatial domain and preserve the edge of different orientations in the image. A MPEG decoded video sequence called table tennis is processed by our proposed filter. The post-processed video sequence shows that its image quality is improved, especially of the moving objects.     

一种级联的激光主动成像图像融合降噪算法

针对激光主动成像图像混合噪声的特点,提出了一种投票中值滤波和整数提升小波级联的融合降噪算法。首先对激光图像进行噪声像素点检测,区分噪声点与非噪声点;而后采用投票中值滤波对噪声点进行处理,抑制脉冲噪声;然后采用整数提升小波变换对图像进行Bayes自适应阈值去噪,抑制散斑噪声;最后通过逆变换得到去噪图像。通过实验比对结果表明,该方法在具有良好的去噪、边缘保持性能的同时,还具有较为理想的实时性。