具有任意方向的扇形滤波器及其在图像方向检测中的应用

扇形滤波器在图像的方向检测中是非常重要的.本文基于极傅里叶变换和一个楔形滤波器,设计出具有任意方向的二维扇形滤波器.首先,楔形滤波器被变换到极傅里叶域,再利用极傅里叶变换的旋转特性,楔形滤波器可以通过将其极傅里叶变换沿着水平方向进行移动来实现旋转,得到一系列具有任意方向导向的扇形滤波器,它们能够检测图像中所包含的任意方...     

基于方向-频率分解的旋转不变性纹理分类

提出了一种用于纹理分类的旋转不变性特征提取的新算法.该算法是将一定大小的图像进行二维傅里叶变换;其次在变换后的图像中央选择一个圆盘区域,并在方向[0°,180°]内进行等间隔角度频率抽样,实现方向分解,使用一组复Morlet小波对每个方向上的映射切片进行小波变换,从而实现多通道频率分解;在各个频率通道中计算均值和方差作为特征,并利用线性回归模型计算频率通道之间的关系特征;将特征沿方向进行一维傅里叶变换并取其幅值,从而得到旋转不变性特征.实验结果表明所提取的特征具有较好的旋转不变性,与其它算法相比具有更好的分类性能,并且对无旋转纹理分类也能产生较好的分类结果.     

一种新的方向滤波器组及其在图像去噪中的应用

通过将二维棋盘形滤波器对和二维余弦调制滤波器组相结合,构造了具有方向和频率选择性的新的方向滤波器组。本文中,我们首先设计了具有45度和135度方向的二维棋盘形滤波器对。通过棋盘形滤波器对,输入图像首先被分解为两幅图像。然后,二维余弦调制滤波器组被分别应用到每幅图像。这种结构等效于一个冗余比为2的方向滤波器组。新的滤波器组具有良好的方向和频率选择性。作为新滤波器组的一个应用,我们把双重局部维纳滤波算法和新的方向滤波器组相结合,提出了一种新的图像去噪算法。实验结果表明：对于具有丰富纹理的图像,提出的算法获得了明显的去噪性能改善。     

新的方向滤波器组及其在图像去噪中的应用

设计了具有45°和135°方向的二维棋盘形滤波器对.通过棋盘形滤波器对,输入图像被分解为两幅子图像,且二维余弦调制滤波器组被分别应用到每幅子图像.这种结构等效于一个冗余比为2的新的方向滤波器组.新的方向滤波器组具有良好的方向和频率选择性.将双重局部维纳滤波算法和新的方向滤波器组相结合,提出了一种新的图像去噪算法.实验结果表明:对于含有丰富纹理的图像,本文提出的算法获得了明显的去噪性能改善.     

基于LabVIEW的阿贝4F系统成像实验的仿真设计

利用LabVIEW软件设计了在阿贝4F系统成像过程中的各个环节,利用生成的二维光栅作为物面图像,之后利用傅里叶变换得到频谱,再通过滤波器对频谱信息进行处理,最后通过傅里叶逆变换得到像面图像。在生成的二维光栅与二维滤波器的设计与仿真中,可方便操作和控制光栅与滤波器的类型和大小,对理解频域与空域图像的对应关系起到了积极作用。     

基于频谱分析的匀速运动模糊图像模糊方向识别

点扩展函数的设置是图像复原中的关键问题，对于匀速直线运动模糊图像，运动方向和模糊长度决定了点扩展函数。根据运动模糊图像和原始图像在频谱上存在的对应关系，即运动模糊图像频谱存在着对应于传递函数零点的暗条纹，提出对运动模糊图像，通过二维傅里叶变换、二值化以及Radon变换检测运动模糊图像频谱图上暗条纹的方向和位置，来实现运动方向测量的方法。用该方法对模糊图像进行检测，模糊方向的识别精度小于1°。实验证明该方法可以实现平面内任意方向运动的测量。     

基于多方向多尺度变换的图像压缩算法

针对小波变换方向选择性差的局限,提出了一种多方向多尺度的的图像变换。圆对称滤波器组首先将图像分解为高频子带和低频子带,然后利用方向滤波器组将高频子带分解为多个方向子带,而对低频子带进行小波变换。多方向多尺度变换能以更稀疏的方式表示图像的边缘和纹理等几何特征,有利于图像压缩。在该变换基础上,结合迭代量化、嵌入式块截断编码(EBCOT)和集合分裂嵌入式块编码(SPECK)构建一种压缩算法。实验结果表明,对于纹理和边缘丰富的图像,压缩算法的性能相对于JPEG2000有明显地提高。     

基于频域纹理消除的结构性纹理缺陷检测方法

针对金属加工表面等结构纹理表面图像缺陷检测问题，结构纹理的存在会对缺陷（比如划痕）检测带来干扰，该文开展在频率域中消除背景纹理的方法来进行缺陷检测的研究。首先基于傅里叶变换的图像复原技术，空间域图像中的结构性纹理对应傅里叶域中高能频率分量，使用最小二乘法直线拟合操作去除，并将这些能量设置为零，经傅里叶逆变换为空间域图像。在复原的图像中，原始图像中的结构纹理区域将变为近似的均匀灰度级，但其中缺陷部分将被保留下来。再使用统计过程控制来设置阈值的方法就能从复原图像中分离出缺陷。最后在一系列的结构性纹理图像上的实验证实了所提方法可行且有效。     

傅里叶-梅林变换(FMT)在畸变-不变图像识别中的应用

针对尺度缩放和角度旋转变化目标相关识别率低的问题,在联合相关识别中加入傅里叶-梅林变换方法.采用傅里叶-梅林变换(FMT)中的对数极坐标变换、梅林变换、傅里叶变换具有的旋转、尺度、平移(RST)不变性,可以提高JTC图像识别的性能,实现畸变-不变图像的识别.利用联合变换相关器对角度旋转0°～40°、尺度变化0～20%的目标进行计算机仿真实验.实验结果表明:在JTC中采用FMT可以实现畸变-不变图像的识别.     

分数傅里叶变换滤波优化算法及滤波器设计

分数傅里叶变换（FrFT, fractional Fourier transform）是经典傅里叶变换的一种表现形式，可理解为在时 频平面中坐标轴系以原点为轴逆时针旋转一定的角度。通过数学推导，对能否利用分数傅里叶变换进行信号滤波，滤波的优化算法如何，以及滤波器有哪些设计结构等问题进行深入的研究，指出分数傅里叶变换适用于非平稳信号滤波。采用Matlab进行了数值仿真实验。实验结果表明：在信号滤波方面，由于傅里叶变换在处理某些数据时有局限性，因此分数傅里叶变换与傅里叶变换相比具有显著的优势。最后给出FrFT滤波器的设计思想。     

基于Gabor小波变换的ICA火灾图像纹理识别算法

针对火灾图像纹理识别问题,提出了基于Gabor小波变换的ICA火灾图像纹理识别算法,并根据火灾图像纹理识别特点进行了优化。首先用不同尺度和方向的Gabor滤波器对待识别图像滤波,得到其特征图像,然后将特征图像转化成特征向量作为ICA的输入,得到基矢量子空间,再将测试图像经过Gabor滤波器的特征向量投影到ICA子空间中得到系数向量作为目标识别特征,最后用支持向量机进行识别。通过与Gabor滤波器法和ICA方法的对比实验,表明该算法可以在火灾纹理图像的识别率上比传统方法提高5%以上,为火灾图像识别提供了一种新思路。     

基于光谱空间变换的遥感图像目标探测方法研究

针对遥感图像在频率域中的表征,提出了一种基于光谱空间变换的遥感图像目标探测方法。该方法首先利用傅里叶变换,将遥感图像从空域转变到频率域;然后利用频谱能量楔状采样和谐波叠置等手段,将不同频谱能量所表征的目标特征信息分解到不同的高、低频段中,由此获取对应目标特征在频率域中的探测标志;最后结合在频谱能量上具有方向和频带选择性的匹配Gabor滤波器,实现了居民楼地物目标的有效探测。试验结果表明,文章所提出的方法能够较好地探测遥感图像的目标信息,并且具有特定方向上目标检测的能力。     

工业CT切片序列任意方向剖面的绘制方法

为获得工业CT切片序列在三维空间任意方向的剖面,提出了一种基于三维纹理映射技术的任意剖面绘制方法。该方法首先把工业CT图片序列存储为体数据,然后使用传递函数对体数据进行分类,并将分类处理后的体数据定义为三维纹理,结合人机交互给出的平面参数计算出剖切多边形各顶点及其对应的纹理坐标,最后进行纹理映射得到剖面图像。该方法的特点是利用图形处理器支持的纹理映射功能完成剖面像素的采样和插值。实验结果表明,该方法成像质量较高,显示速度较快,能够满足人机交互实际应用需要。     

Edge directional 2D LMS filter for infrared small target detection

In this paper, we introduce an edge directional 2D least mean squares (LMSs) filter for small target detection in infrared (IR) images. Generally, the 2D LMS filter functions as a background prediction to apply to IR small target detection field. In order to accurately predict background objects as well as regions covered by small targets, the proposed 2D LMS filter take full advantage of edge information of prediction pixels corresponding to surrounding blocks around current filter window. And, to adjust adaptively its step size in the background and small target region, the adaptive region-dependent nonlinear step size is calculated by using the variance of the prediction pixels of the surrounding blocks. This prediction structure and adaptive step size of the proposed 2D LMS filter is applied to the background region including objects such as cloud edge and small target region differently. Through this way, the proposed 2D LMS filter predicts the background excluding small targets. Then, by subtracting the predicted background from the original IR image, small targets can be extracted. Experimental results show that the proposed 2D LMS filter has stronger target extraction and better background suppression ability compared to the existing 2D LMS filters.     

3D passive photon counting automatic target recognition using advanced correlation filters

In this Letter, we present results for detecting and recognizing 3D objects in photon counting images using integral imaging with maximum average correlation height filters. We show that even under photon starved conditions objects may be automatically recognized in passively sensed 3D images using advanced correlation filters. We show that the proposed filter synthesized with ideal training images can detect and recognize a 3D object in photon counting images, even in the presence of occlusions and obscuration.     

Channelized detection filters

The problem of detecting objects in noisy backgrounds is addressed. We derive detection filters by training a linear classifier, using features obtained from subimages corresponding to circular channels in the Fourier domain. The classifier weights approach the prewhitening matched filter when the classifier is trained for the detection of known objects in stationary noise. A simple form of rotation invariance is attained for considerably less computation than by the direct application of multiple matched filters. The method is demonstrated for the task of detecting simulated tumors in simulated nuclear medical images.     

Denoising 3D MR images by the enhanced non-local means filter for Rician noise

The non-local means (NLM) filter removes noise by calculating the weighted average of the pixels in the global area and shows superiority over existing local filter methods that only consider local neighbor pixels. This filter has been successfully extended from 2D images to 3D images and has been applied to denoising 3D magnetic resonance (MR) images. In this article, a novel filter based on the NLM filter is proposed to improve the denoising effect. Considering the characteristics of Rician noise in the MR images, denoising by the NLM filter is first performed on the squared magnitude images. Then, unbiased correcting is carried out to eliminate the biased deviation. When performing the NLM filter, the weight is calculated based on the Gaussian-filtered image to reduce the disturbance of the noise. The performance of this filter is evaluated by carrying out a qualitative and quantitative comparison of this method with three other filters, namely, the original NLM filter, the unbiased NLM (UNLM) filter and the Rician NLM (RNLM) filter. Experimental results demonstrate that the proposed filter achieves better denoising performance over the other filters being compared.     

Bilateral two-dimensional least mean square filter for infrared small target detection

The basic TDLMS (Two-Dimensional Least Mean Square) filter fails to detect infrared small targets consistently, especially under conditions of heavy noise and distinct cloud edges. This paper proposes a robust and efficient small-target detection method based on the basic TDLMS filter. The method first smooths the input image with a Gaussian filter of adaptive variance, and then employs TDLMS with a selected step size to filter the image with rightward and leftward iterations. Two prediction error images are obtained by subtracting the prediction images of the bilateral filtering from the original input image. Each prediction error image is separated into positive and negative prediction error images. That is, four images are generated in the bilateral filtering. The final image is obtained by fusing these four images. Experimental results show that the proposed method achieves significant improvement in background suppression and detection performance over the basic TDLMS filter and other improved TDLMS filters.     

A median-Gaussian filtering framework for Moiré pattern noise removal from X-ray microscopy image

Moiré pattern noise in Scanning Transmission X-ray Microscopy (STXM) imaging introduces significant errors in qualitative and quantitative image analysis. Due to the complex origin of the noise, it is difficult to avoid Moiré pattern noise during the image data acquisition stage. In this paper, we introduce a post-processing method for filtering Moiré pattern noise from STXM images. This method includes a semi-automatic detection of the spectral peaks in the Fourier amplitude spectrum by using a local median filter, and elimination of the spectral noise peaks using a Gaussian notch filter. The proposed median-Gaussian filtering framework shows good results for STXM images with the size of power of two, if such parameters as threshold, sizes of the median and Gaussian filters, and size of the low frequency window, have been properly selected.