矢量自对偶形态学滤波算子

自对偶形态学算子不依赖形态学腐蚀、膨胀算子的先后次序, 是一种等同处理图像背景和前景的形态学算子. 而将自对偶形态学算子拓展到多通道图像处理是一个难题. 为了解决该问题, 提出了基于极值约束的矢量自对偶形态学滤波算子(EC-VSDMF). 首先根据对称矢量排序算法构建满足对偶性的矢量形态学算子, 然后依据形态学算子中的极值原理优化矢量集合, 从而有效抑制矢量集合中包含单通道极值的矢量作为输出结果, 最终实现了具有约束功能的矢量自对偶形态学滤波算子(VSDMF). 实验结果表明, EC-VSDMF继承了传统自对偶形态学滤波算子的性质, 将其应用于彩色图像滤波可以改善现有矢量形态学滤波算子导致滤波后图像亮度和色度发生偏移的问题. 滤波后的图像在有效抑制噪声的同时较好地保留了图像细节, 滤波性能甚至超过了多种现有的矢量中值滤波算子.     

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.     

边缘检测中的改进型均值滤波算法

针对传统高斯滤波器算法存在边缘模糊及局部伪影的问题,提出一种改进型均值滤波算法,以有效去除图像中的高密度脉冲噪声。通过求解图像直方图,检测图像直方图的左右2个峰值点,以查找出可疑的噪声点,进而使用灰度值确定其坐标。利用图像像素点周围灰度值的相似性,在局部窗口选择递归加权平均值代替被污染的图像像素点灰度值。结合自动选择Canny算子的双阈值方法,对灰度图像边缘进行检测。实验结果表明,该算法的算子滤波效果优于LOG算子和传统灰度Canny算子,且具有较好的稳定性。     

改进型中值滤波和形态学组合降噪方法

根据改进型中值滤波和开-闭灰度形态学滤波算子设计了一种基于门限递归中值滤波和灰度形态学滤波组合优化算法,该算法弥补了中值滤波在噪声强度较高时去噪能力下降快的缺点.仿真结果表明,在噪声强度较高时,该算法明显优于单独使用中值滤波和灰度形态学滤波,具有较高的实用性.     

一种改进的中值滤波算法

通常 ,大部分自然图像中同时存在颗粒噪声和高斯噪声 ,而单纯地用中值滤波算法或均值滤波难以同时尽可能地消除混合噪声。针对这一问题 ,L ee和 Kassam提出了一种改进的均值滤波算法 Modified Trim med Mean(MTM) ,虽然 MTM算法的滤波效果相对于传统的平滑算法已有了很大的改善 ,但是 MTM的滤噪能力在很大程度上受到了阈值的限制。在分析 MTM算法和传统平滑算法结构特点的基础上提出了一种改进的自适应中值滤波算法。该算法对含有混合噪声的图像上每一点的 N× N区域应用自适应算子。对于不同的图像区域 ,算子也相应地有所不同 ,其中算子中的权值选取依赖于区域的灰度中值 ,且当某点的灰度越接近灰度中值 ,其权值就相应地越大。实践证明 ,新算法的处理结果优于传统的滤波算法和 MTM滤波 ,且没有阈值限制     

图像降噪组合滤波优化算法

论文利用中值滤波算子和灰度数学形态学中的开、闭算子设计了一种用于灰度图像降噪处理的优化组合滤波算法。提出了一种简单、实用的评价函数用于算法的优化过程。该算法能根据输入噪声图像自动调整算子的组合结构获得最佳的滤波输出,并克服了中值滤波算法在处理灰度图像时对噪声强度的敏感问题。该优化算法简洁、耗时少。算法的实用性用计算机进行了仿真验证。     

基于HSV彩色空间的矢量形态学算子

在HSV彩色空间中,现有的矢量形态学算子对彩色像素的排序依据V、S、H顺序分层进行,从而违背了彩色图像中三个分量的平等原则,导致矢量形态学滤波算子难以去除彩色图像中由色调和饱和度分量引起的噪声,因此滤波算子性能较差.文中提出了一种基于HSV三分量混合运算的矢量形态学排序规则,并根据该规则定义了新的矢量形态学腐蚀、膨胀算子以及常用的矢量形态学滤波算子.实验结果表明,新的矢量形态学滤波算子较现有的矢量形态学滤波算子具有更强的噪声抑制性能,在保证图像不增加新的彩色像素的同时,去除了噪声并保留了图像细节,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.     

基于FPGA的灰度形态学滤波器实现

阐述了灰度形态学滤波的原理和基本操作,给出了3×3结构元素灰度形态学滤波器的硬件结构,详细描述了该滤波器在Xilinx公司的XC2S400E芯片上实现的过程和仿真的方法,并说明了需要注意的问题。     

利用非线性滤波进行多尺度拐角检测

对灰度图进行一般线性和非线性平滑会极大程度地模糊拐角,使得多尺度方法难以用于拐角检测.由于非线性多尺度CB(contour-based)形态滤波算子具有很好的拐角保持特性,而且能使拐角满足单调性,即当尺度增大时拐角数不增加.用CB形态滤波器对灰度图进行滤波,构成CB形态学尺度空间,以实现对拐角的多尺度检测.理论分析和实验结果证明了这种分析方法的有效性.     

基于Prewitt的指纹图像预处理算法研究

指纹识别能否取得预期的效果,取决于指纹图像预处理。传统图像预处理的步骤包含灰度滤波、二值化及二值滤波、细化及细化去噪。提出一种将优化的Prewitt算子用于灰度滤波的指纹图像预处理方法:先在灰度滤波时采用方向图滤波进行滤波处理,之后采用优化的Prewitt算子进行图像增强处理;再在局部快速傅氏变换时引入方向傅氏变换滤波;最后,采用八邻域查表细化算法进行细化去噪。该方法不仅使指纹图像的对比度更加清晰,同时还对指纹图像进行了边界提取与处理。整个方案具有较强的实用价值和应用前景。     

Denoising by using multineural networks for medical X-ray imaging applications

In this paper, a new type of multineural network filter (MNNF) is presented that is trained for restoration and enhancement of the digital radiological images. In medical radiographices, noise has been categorized as quantum mottle, which is related to the incident X-ray exposure and artificial noise, which is caused by the grid, etc. MNNF consists of several neural network filters (NNFs). A novel analysis method is proposed to make the characteristics of the trained MNNF clearly. In the proposed method, a characteristics judgement system is presented to decide which NNF will be executed through the standard deviation value of pixels in the input region. The new approach was tested on nine clinical medical X-ray images and five synthesized noisy X-ray images. In all cases, the proposed MNNF produced better results in terms of peak signal-to-noise ratio (PSNR), mean-to-standard-deviation ratio (MSR) and contrast to noise ratio (CNR) measures than the original NNF, linear inverse filter and nonlinear median filter.     

Adaptive image denoising using cuckoo algorithm

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.     

Multiplicative noise removal via combining total variation and wavelet frame

Total variation (TV) regularization has been proved effective for cartoon images restoration however it produces staircase effects, and properly wavelet frames were confirmed to provide a more smoothing approximation to the original image. In this paper, a new model for multiplicative noise removal was proposed, which combines wavelet frame-based regularization and TV regularization. A modified proximal linearized alternating direction method is developed to solve the proposed model, considering that adding a new regularization term to the TV model would yield more parameters, which will result in computational difficulties. For the new model, the existence of solution and the convergence property of the proposed algorithm are proved. Numerical experiments have proved that the proposed model has a superior performance in terms of the peak signal-to-noise ratio and the relative error values for non-piecewise constant images when compared with some state-of-the-art multiplicative noise removal models.     

改进的形态学肺部图像边缘检测

针对肺部图像边缘检测中存在的噪声问题,在数学形态学边缘检测的基础上做了3点改进:(1)结合结构元素3个基本选取原则,即形状的相似性、尺寸的覆盖性和不同结构元素的组合性,选取适合肺部图像的全方位结构元和多尺度结构元;(2)改进了普通的形态学边缘检测算子,将全方位结构元和多尺度结构元相结合,得到适用于肺部图像的新型复合形态学边缘检测算子;(3)将峰值信噪比(Peak signal-to-noise ratio, PSNR)加入权值计算方法中,改进了权值的计算方法。最后通过仿真实验,对PSNR为50684 9 dB的肺部噪声图像进行边缘检测,并与一般算法进行比较,结果表明改进算法在PSNR和均方误差(Mean square error, MSE)上均有明显改善,能够检测出更清晰、去噪效果更好的肺部图像边缘。应用于其他图像或加入不同噪声时,本文算法也能检测出更清晰的图像边缘,表明该算法具有很好的鲁棒性。     

An efficient decision-based and edge-preserving method for salt-and-pepper noise removal

In this paper, we propose an improved two-stage scheme for the removal of salt-and-pepper noise based on an efficient impulse detector and the edge-preserving total variation inpainting model. We test the proposed algorithm on the four images corrupted by salt-and-pepper noise with a wide range of noise levels varied from 10% to 95%, and compare it with the six other well known filters. Further, we evaluate quantitatively the performance of these filters with the peak signal-to-noise ratio (PSNR), the measure of structural similarity (SSIM) and the image enhancement factor (IEF). The experimental results have demonstrated that the proposed filter performs impressively in noise suppression and edge preservation.     

小波与双边滤波的医学超声图像去噪

目的：医学超声图像中的斑点噪声降低了图像质量并且限制了超声图像自动化诊断技术的发展。针对斑点噪声问题,提出了一种新型的基于小波和双边滤波的去噪算法。方法：首先,根据医学超声图像在小波域内的统计特性,在通用小波阈值函数的基础之上,改进了小波阈值函数。其次,将无噪信号的小波系数和斑点噪声的小波系数分别建模为广义拉普拉斯分布模型和高斯分布模型,利用贝叶斯最大后验估计方法得到了新型的小波收缩算法,利用小波阈值法对小波域内的高频信号分量进行去噪。最后,对小波域内的低频信号分量进行双边滤波处理,然后利用小波逆变换便得到去噪后的图像。结果：在仿真实验中,通过与其它7种去噪算法作对比,观察峰值信噪比(PSNR)等图像质量评价指标,结果表明本文算法的去噪效果优于其他相关算法。临床超声图像的实验结果进一步验证了本文算法的去噪性能。结论：本文提出了一种新型的去噪算法,实验表明本文算法能够很好地抑制斑点噪声,并且能保留图像病灶边缘等细节。     

极化SAR相干斑抑制的非局部加权最小均方误差滤波算法

目的 相干斑的存在严重影响了极化合成孔径雷达(PolSAR)的影像质量.对相干斑的抑制是使用SAR数据的必不可少的预处理程序.提出一种基于非局部加权的线性最小均方误差(LMMSE)滤波器的极化SAR滤波的方法.方法 该方法的主要过程是利用非局部均值的理论来获取LMMSE估计器中像素样本的权重.同时,在样本像素的选取过程中,利用待处理像素的极化散射特性和邻域块的异质性来排除不相似像素以加速算法,同时达到保持点目标和自适应调节块窗口大小的目的.结果 模拟影像和真实影像上进行的实验结果表明,采用这种方法滤波后影像的质量得到明显改善.和传统的LMMSE算法相比,无论是单视的影像还是多视的影像,本文方法去噪结果的等效视数都高出8视以上;峰值信噪比也提升了5.8 dB.同时,去噪后影像分类的总体精度也达到了83％以上,该方法的运行效率也比非局部均值算法有了较大提升.结论 本文方法不仅能够有效抑制相干斑噪声,还能较好地保持边缘和细节信息以及极化散射特性.这将会为后续高效利用SAR数据提供保障.     

Scale-space using mathematical morphology

In this paper, we prove that the scale-space of a one-dimensional gray-scale signal based on morphological filterings satisfies causality (no new feature points are created as scale gets larger). For this we refine the standard definition of zero-crossing so as to allow signals with certain singularity, and use them to define feature points. This new definition of zero-crossing agrees with the standard one in the case of functions with second order derivative. In particular, the scale-space based on the Gaussian kernel G does not need this concept because a filtered signal G*f is always infinitely differentiable. Using this generalized concept of zero-crossing, we show that a morphological filtering based on opening (and, hence, also closing by duality) satisfies causality. We note that some previous works have mistakes which are corrected in this paper. Our causality results do not apply to more general two-dimensional gray scale images. Causality results on alternating sequential filter, obtained as byproduct, are also included