全方位多结构元形态滤波器

形态滤波器是在脉冲噪声背景下恢复根信号的一种有效方法。此文定义了一类全方位多结构元，并由此提出了一种新的形态滤波器－全方位多结构元形态滤波器。在标准试图象噪声环境下，仿真实验结果表明在噪声抑制和细节保持上，该滤波器有较好的性能。     

一类自适应顺序形态滤波器

提出了一类自适应顺序形态滤波器,建立了排序运算的隐含表达式,并在最小均方误差（ＭＳＥ）和最小平均绝对误差（ＭＡＥ）准则下,实现了结构元素和百分位值的自适应处理,该滤波器不仅可以有效地抑制信号中的各种噪声,而且较好地保持了信号的几何特征,计算机仿真结果证实了滤波算法在噪声图象恢复方面有较好的效果。     

堆栈滤波的递归实现

堆栈滤波器是一类能够有效滤除脉冲类噪声,同时能较好地保护图象边缘与细节的非线笥滤波器,它所具有的 阈值分解和堆栈两个重要性质保证了滤波过程可以在二进制域并行实现,虽然ＭＡＥ（平均绝对误差）准则下最优堆栈滤波器可由ＬＭＡ（最小平均绝对误差）算法给出,但直接推导得到的滤波有属于非递归型滤波器,基于此,提出了ＭＡＥ准则下最优堆栈滤波器的递归实现方法,该方法不仅采取直接递归和最递归两种方案实现递滤波器的噪     

基于层叠处理的多级自适应WOS滤波器

基于层叠处理的ＬＰ算法求解最优非线性滤波 缺陷在于：⑴必须掌握信号和噪声的先验统计知识,⑵计算量随滤波窗增大呈超指数增加,根据层叠滤波器理论,所有ＷＯＳ滤波器正布尔函数都是线性可分离的,在此基础上,提出了一种多级自适应ＷＯＳ层叠滤波器,从有效地克服了传统ＬＰ算法的缺点,它尤其适合于图象处理领域。     

新型红外图像脉冲噪声滤波算法

根据脉冲噪声与其邻域中图像灰度之间的明显差异性,提出一种新的基于交叉视觉皮质模型(ICM)的神经网络脉冲噪声滤波器算法.这种交叉视觉皮质模型(ICM)迭代运算次数少,执行速度快.并与中值滤波器、全方位结构元约束层叠滤波器、全方位结构元形态闭-开最小、开-闭最大滤波器等现有的非线性滤波器进行实验比较, 证明该方案有更好的降噪性能, 更重要的是比这些方案更有效地保持了红外图像的高频细节信息,对于红外图像的后期处理和识别打下了很好的基础.     

自适应邻域加权模拟退火层叠滤波器

图像处理问题的研究中,层叠滤波器的优化设计实际上是寻找最优正布尔函数的过程,如何有效地获得最优正布尔函数是优化设计的难点问题,为了改进层叠滤波器的优化方法,提出在自适应邻域加权平均绝对值(ANWMAE)准则的基础上建立数学优化模型,设计层叠滤波器,算法相对遗传算法优化时间较短,同时将邻域像素点的影响考虑在内,通过在迭代过程中得到最优正布尔函数.在传统阈值分解的基础上,构建自适应邻域加权模拟退火层叠滤波器(ANWSA)和自适应邻域加权递归模拟退火层叠滤波器(RANWSA).对最优ANWSA和RANWSA进行了性能分析,结果表明,层叠滤波器在有效地滤除噪声的同时,能更好地保持图像的细节信息.     

自适应加权镜像阈值层叠滤波器

与传统的阈值层叠滤波器相比,镜像阈值层叠滤波器不仅具有低通滤波的特性,还具有带通和高通的特性。但由于镜像阈值层叠滤波器比传统的阈值层叠滤波器的正布尔函数长度有显著增加,从而使计算量增加,为解决这一问题,提出了一种镜像自适应加权（MAW）算法。该方法充分考虑了镜像阈值分解的特点,并通过引入自适应领域加权误差准则建立了代价向量,在迭代过程中,对代价向量的层叠性进行快速约束,并判断其收敛性,最终获得了基于最优正布尔函数的自适应加权镜像阈值层叠滤波器（AWMSF）。为了验证该滤波器的滤噪性能,对最优AWMSF进行了性能分析,结果表明,AWMSF在滤除噪声的同时,能更好地保持图像的细节信息,并可减少迭代次数,从而使计算复杂度大大降低。     

层叠加权中值滤波器

利用层叠滤波器的阈值分解特性，提出了一种基于阈值分解结构的滤波器－层叠加权中值滤波器。该滤波器结构简单，易于并行处理和通过ＶＬＳＩ实现。图象处理仿真实验表明，其具有良好的滤波效果。     

IMC系统的优化滤波器研究

推荐两种滤波器作为ＩＭＣ系统的滤波器。一种是ＩＴＡＥ滤波器，它符合时间乘绝对误差积分值最小的优化原则，即Ｊ＝│ｅ│ｔｄｔ→ｍｉｎ。另一种是Ｂｕｔｔｅｒｗｏｒｔｈ滤波器，它具有很好的理想低通滤波器特性。由于这两种滤波器的全部结构极点均处于Ｓ复平面的高稳定性区域，又是按最优原理确定其参数，因此，采用这两种滤波器可以抑制模型失配导致的不良影响，使得ＩＭＣ系统的鲁棒性更加有保证。本文列举一个工业过程控制例     

单通道最优和自校正去卷平滑器及其应用

基于白噪声估计理论，本文提出了单通道ＡＲＭＡ信号的一种新的最优和自校正去卷滤波器和平滑器，可处理非平稳ＡＲＭＡ输入信号及不稳定和非最小相位系统，并给出了在雷达跟踪系统中的仿真应用例子。     

Soft morphological filtering

Stack filters are widely used nonlinear filters based on threshold decomposition and positive Boolean functions. They have shown to form a very large class of filters which includes rank-order operations as well as standard morphological operations. The stack filter representation of an order statistic filter provides an efficient tool for the theoretical analysis of the filter.Soft morphological filters form a large subclass of stack filters. They were introduced to improve the behavior of standard morphological filters in noisy conditions. In this paper, different properties of soft morphological filters are analysed and illustrated. Their connection to stack filters is established, and that connection is used in the statistical analysis of soft morphological filters. Soft morphological filters are less sensitive to additive noise than standard morphological filters. The deterministic properties of soft morphological filters are also analysed and it is shown that soft morphological filters form a class of filters with many desirable properties. For example, they preserve well details of images.     

SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.     

Structure and properties of generalized adaptive neural filters forsignal enhancement

This article addresses the structure and properties of a new class of nonlinear adaptive filters called generalized adaptive neural filters (GANFs). Various properties, such as an upper bound of the mean absolute error of the filters, are analytically derived. Experimental results are presented to demonstrate the performance of the filters for signal and image enhancement. It is shown that GANFs not only extend the class of stack filters, but also have better performance in noise suppression.     

双算子形态学滤波器

传统的形态学滤波算子交替性差、耗时长且抑制噪声能力弱. 基于中心互补结构元素与交替对偶算子, 提出了双算子形态学滤波器. 该滤波器继承了经典形态学滤波器的递增性、对偶性和幂等性, 但不满足扩展性和非扩展性. 双算子形态学滤波器具有离散的邻域运算特性, 采用交替小结构元素能去除较结构元素大的噪声块, 且在抑制噪声的同时有效保留了图像细节. 实验结果表明, 与基本的形态学滤波器及目前已改进的形态学滤波器相比, 双算子形态学滤波器具有更强的噪声抑制性能, 且在同等滤波效果下, 其计算量更小, 最终滤波后的图像具有较高的峰值信噪比和较小的均方根误差.     

一类多结构元自适应广义形态滤波器

基于广义形态开—闭和闭—开运算，采用多种结构元和自适应加权平均技术，定义了一类新型形态滤波器。这类滤波器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特征。滤波器计算简单，便于实时并行处理。     

Omnidirectional edge detection

In this paper we propose a new method for extending 1-D step edge detection filters to two dimensions via complex-valued filtering. Complex-valued filtering allows us to obtain edge magnitude and direction simultaneously. Our method can be viewed either as an extension of n-directional complex filtering of Paplinski to infinite directions or as a variant of Canny’s gradient-based approach. In the second view, the real part of our filter computes the gradient in the x direction and the imaginary part computes the gradient in the y direction. Paplinski claimed that n-directional filtering is an improvement over the gradient-based method, which computes gradient only in two directions. We show that our omnidirectional and Canny’s gradient-based extensions of the 1-D DoG coincide. In contrast to Paplinski’s claim, this coincidence shows that both approaches suffer from being confined to the subspace of two 2-D filters, even though n-directional filtering hides these filters in a single complex-valued filter. Aside from these theoretical results, the omnidirectional method has practical advantages over both n-directional and gradient-based approaches. Our experiments on synthetic and real-world images show the superiority of omnidirectional and gradient-based methods over n-directional approach. In comparison with the gradient-based method, the advantage of omnidirectional method lies mostly in freeing the user from specifying the smoothing window and its parameter. Since the omnidirectional and Canny’s gradient-based extensions of the 1-D DoG coincide, we have based our experiments on extending the 1-D Demigny filter. This filter has been proposed by Demigny as the optimal edge detection filter in sampled images.