广义多结构元素并行复合形态滤波器

本文基于数学形态学中的广义形态开－闭和闭－开运算，采用多结构元素，构造了一类并行复合形态滤波器，这类滤波器具有平移不变性、递增性、对偶性和幂等性等重要性质，并遵半阈值叠加准则，不仅可以有效地抑制图象中的噪声，而且较好地保持了图象的几何结构特征。计算机模拟结果证实了滤波算法的有效性。     

多结构元受控形态滤波器

基于受控形态学中的形态开-闭和闭-开运算,采用多结构元素,构造了一类多结构元受控形态滤波器.该滤波器具有平移不变性、递增性、对偶性和幂等性等重要性质.多结构元受控形态滤波器不仅可以有效地抑制图像噪声,而且较好地保持了图像的几何结构特征,通过与传统形态滤波器的对比实验,进一步验证了该滤波器的优越性能.     

二值图象的多结构元数学形态滤波

与单结构元形态滤波相比，多结构元形态滤波能够保护或提取更多的图象几何特征．本文提出了二值图象的多结构元腐蚀、膨胀、开、闭四种多结构元形态滤波器，讨论了四种滤波器的基本性质和滤波性能，并将他们纳入完备网格空间一般形态变换的框架，最后讨论了多结构元形态滤波器的设计．     

一类形态带通滤波器及其特性分析

本文利用数学形态学中的开运算构成了一类形态带通滤波器，并且入地分析了它们的性质，这类滤波器只允许图像中具有特定形状的物体通过，因而能够有效地用于抽取图象的形状特征。     

一种全方位多角度自适应形态滤波器及其算法

根据形态滤波器的结构元特性,结合形态开—闭(闭—开)组合运算及自适应算法,建立了一种能有效去除复杂纹理图像中的各种噪声,并较好的保持图像细节的方法。实验证明,这种全方位多角度自适应形态滤波器与现有的中值滤波器、全方位结构元形态闭—开最小、开—闭最大滤波器等非线性滤波器相比较有更好的降噪性能及细节保护能力,扩展了形态滤波器在滤除高斯噪声方面的应用,提高了形态滤波器的适应性。     

一种广义多结构元形态滤波器

 形态滤波器是一种非线性滤波器.基于基本的形态变换—腐蚀和膨胀,采用多结构元素的方法,构造了广义形态开最大和广义形态闭最小滤波器.这类滤波器具有平移不变性、递增性、对偶性等重要性质.为了验证广义形态开最大、闭最小滤波器的有效性,针对含有不同幅值椒盐噪声和混合噪声的测试图像,使用这类滤波器对其进行滤波.滤波结果表明广义形态开最大、闭最小滤波器在抑制噪声的同时能保持更多的几何信息.随着噪声增强它的抗噪性也增强,而且它运行的时间将小于形态开闭最大、闭开最小、广义形态开闭最大、闭开最小滤波器运行的时间.     

一种形态开、闭自适应加权组合滤波器

本文利用数学形态学中的开、闭运算，结合自适应方法，提出了一种形态开、闭自适应加权组合滤波器．该滤波器不仅可以有效地抑制信号中各种加性白噪声和正、负脉冲噪声，而且不模糊信号中的陡峭阶跃变化．     

多种噪声图象的自适应韧性滤波

本文提出了一种用于噪声图象的自适应韧性滤波器。它不需要先验的图象统计分布知识,具有良好的滤波韧性。它能同时滤除图象中的独立加性噪声、信号有关噪声和脉冲噪声。这种滤波器有很好的边缘信号保护作用并易于实现。     

形态学滤波在压电陀螺信号处理中的应用

基于数学形态学滤波中的开-闭和闭-开滤波器的基本原理,针对压电陀螺信号噪声的特点和结构元素的选择原则,选用半圆形、三角形和余弦形的结构元素,构造一种多结构元素并行复合形态滤波器,对某型号压电陀螺的静态漂移和动态测量信号进行仿真分析,并与小波变换的处理结果进行了比较。结果表明,形态学滤波器可有效提高输出信号的信噪比,提高处理过程的速度,易于工程实现。     

一种形态开,闭自适应加权组滤波器

本文利用数学形态学听开，闭运算，结合自适应方法，提出一种形态开，闭自适应加要组合滤波器，该滤波器不仅可以有效地抑掉信号中各种加性白噪怕和正，负脉冲噪声，而且不模信号中的嵴阶跃变化。     

图像边缘提取的自适应Volterra滤波器设计

Ｖｏｌｔｅｒｒａ滤波是研究信号高阶统计冗余性的一种有效途径。我们提出了一种用于提取图像边缘的自适应二次Ｖｏｌｔｅｒｒａ滤波器设计方法。这种滤波器是推广型Ｔｅａｇｅｒ基滤子的线性组合，其系数用基于极小化最小均方能量函数的共轭梯度法研究；它兼有局域平均和高通特性，因而可均衡去除噪声和增强图像边缘。文章还给出了计算实例。     

Equivariant holomorphic filters for contour denoising and rapid object detection.

It is well known that linear filters are not powerful enough for many low-level image processing tasks. However, it is also very difficult to design robust nonlinear filters that respond exclusively to features of interest and that are, at the same time, equivariant with respect to translation and rotation. This paper proposes a new class of rotation-equivariant nonlinear filters that is based on the principle of group integration. These filters become efficiently computable by an iterative scheme based on repeated differentiation of products and summations of intermediate results. The relations of the proposed approach to Volterra filters and steerable filters are shown. In the context of detection problems, the filter may be interpreted as some kind of generalized Hough transform. The experiments show that the new filter can be used for enhancing noisy contours and rapid object detection in microscopical images. In the detection context, our experiments show that the proposed filter is definitely superior to alternative approaches, when high localization accuracy is required.     

Speeding up the generalized adaptive neural filters

A new class of adaptive filters called generalized adaptive neural filters (GANFs) has emerged. They share many things in common with stack filters and include all stack filters as a subset. The GANFs allow a very efficient hardware implementation once they are trained. However, the training process can be slow. This paper discusses structural modifications to allow for faster training. In addition, these modifications can lead to an increase in the filter's robustness, given a limited amount of training data. This paper does not attempt to justify use of a GANF; it only presents an alternative implementation of the filter. To verify the results, several simulations were performed by corrupting two images with varying amounts of mixture noise and Gaussian noise.     

A new class of nonlinear filters-neural filters

A class of nonlinear filters based on threshold decomposition and neural networks is defined. It is shown that these neural filters include all filters defined either by continuous functions, such as linear finite impulse response (FIR) filters, or by Boolean functions, such as generalized stack filters. Adaptive least-mean-absolute-error and adaptive least-mean-square-error algorithms are derived for determining optimal neural filters. As special cases, adaptive generalized stack and adaptive generalized weighted order statistic filtering algorithms under both error criteria are derived. Experimental results in 1D and 2D signal processing are presented to compare the performances of the adaptive neural filters and other widely used filters     

Isotropic and anisotropic filtering norm-minimization: A generalization of the TV and TGV minimizations using NESTA

Compressive sensing (CS) allows for the reconstruction of sparse signals based on measurements acquired at sub-Nyquist sampling rates. Amongst the common CS methods, total variation (TV) minimization is a common approach used to reconstruct images that are approximately piece-wise constant. The discrete gradient operation in TV can be described as a filtering operation using the horizontal and vertical finite differences filters. In this paper, we generalize the TV minimization procedure for any set of digital filters. The proposed method allows one to reconstruct signals that are sparse when filtered with some set of filters, other than the finite difference operators. Our implementation is based on a fast and accurate first-order optimization algorithm which is called NESTA, in a reference to the Nesterov’s algorithm. We incorporate isotropic and anisotropic filtering combinations to the original TV minimization method implemented in the original NESTA algorithm. We also propose 3 forms of the second order total generalized variation (TGV) when using first and second order filters. In order to evaluate the method, we perform a systematic set of experiments using synthetic and real magnetic resonance images, with several sets of filters and cost functions and under different undersampling factors and noise levels. A statistical analysis of the results shows that the best configurations of our method provide a significantly better image quality when compared to the TV and TGV for MRI reconstruction.     

An orthogonal wavelet representation of multivalued images

A new orthogonal wavelet representation of multivalued images is presented. The idea for this representation is based on the concept of maximal gradient of multivalued images. This concept is generalized from gradients toward linear vector operators in the image plane with equal components along rows and columns. Using this generalization, the pyramidal dyadic wavelet transform algorithm using quadrature mirror filters is modified to be applied to multivalued images. This results in a representation of a single image, containing multiscale detail information from all component images involved. This representation leads to multiple applications ranging from multispectral image fusion to color and multivalued image enhancement, denoising and segmentation. In this paper, the representation is applied for fusion of images. More in particular, we introduce a scheme to merge high spatial resolution greylevel images with low spatial resolution multivalued images to improve spatial resolution of the latter while preserving spectral resolution. Two applications are studied: demosaicing of color images and merging of multispectral remote sensing images.     

自适应去噪滤波器组合的训练与设计方法

该文通过建立噪声信道模型提出一种对噪声自适应的去噪滤波器组合进行训练和设计的方法。通过对训练图像及噪声信道输出图像的有监督训练,建立自适应于噪声的滤波器组合模型,并可在随后的应用中采用该模型对通过噪声信道的图像进行盲滤波达到滤除图像噪声和保留图像细节的目的。为验证设计方法的有效性,采用高斯调制的加权中值滤波器组对信道中常见的椒盐噪声和均匀分布脉冲噪声进行滤除,效果较现有方法有明显优势。该设计方法可以推广应用到其它具有适应性的去噪滤波器之上,使它们对不同类型、不同强度的信道噪声进行更柔性化的处理。     

Low-complexity subband encoding for HDTV images

The transmission of high-definition television (HDTV) signals on available digital networks and satellites requires the adoption of sophisticated compression techniques to limit the bit rate requirements and to provide high-quality and reliable service to customers. For processing and transmission of image signals, a low-complexity codec without visible degradation is desired. A low-complexity intraframe subband image coding algorithm is developed. The low band is DPCM encoded and the high bands are PCM encoded. An efficient entropy coder is designed which reduces the overall bit rate significantly. It is shown that high-quality HDTV images can be obtained at as low a bit rate as 45 Mb/s or less with a very low-complexity encoder. For dividing the image into subbands, a new class of quadrature mirror filters (QMFs) called generalized quadrature mirror filters (GQMFs) is used for filtering. Performance is also evaluated by using short kernel filters (SKFs), which are easy to implement and require very few computations     

广义切比雪夫型LC滤波器的设计*

提出一种广义切比雪夫型LC滤波器的设计方法,带外传输零点可以是任意设定,其带外特性既可以对称,也可以不对称.采用此方法设计出的LC滤波器,其带内波动可以与最大平坦型滤波器一样很小,而矩形系数可以与椭圆函数型滤波器一样好.每一个传输零点的位置可以由一个元器件的取值大小控制.与椭圆函数型LC滤波器相比,相同数量的元器件,它可以实现更陡的截止特性.     

基于高概率椒盐噪声的模糊滤波器在图像恢复中的算法设计

高概率椒盐噪声对数字图像的重度污染大量存在,如要消除信息少且噪点集中的噪声存在诸多困难;而低概率椒盐噪声对数字图像的轻度污染虽然可完全滤除,但在实际图像恢复中又缺少普遍意义.本文基于灰度值空间的模糊划分和描述灰度水平的模糊数,采用极值法对高概率噪声实施检测并建立恰当滤波窗口,应用广义重心去模糊化法和非噪声点对应的隶属函数设计一种新模糊滤波器.最后,通过仿真实例获得该滤波器可有效地过滤数字图像中高概率椒盐噪声,并说明它的去噪性能优于其他常见滤波器.