一种基于椒盐噪声新型组合滤波算法仿真研究

提出了一种新型组合滤波算法。该算法首先在噪声方差估计、滤波模板类型和尺寸大小等方面对自适应维纳滤波进行改进,对图像噪声进行预处理;其次将预处理后的图像进行二维多尺度小波分解,由于低频子图像基本不受噪声污染,故不作处理;然后对开关中值滤波分别从噪声检测、噪声分类、噪声滤波等方面进行改进,并给出具体实现步骤,用于小波域高频子图像滤波;最后将滤波后高频子图像和低频子图像进行小波系数重构。实验结果表明,两类改进滤波算法在滤波性能上均优于原始算法,在抗噪性和细节保持等方面具有一定优势。     

声呐图像中的混合噪声抑制方法

针对含有混合噪声的声呐图像,提出了中值滤波与形态学滤波的组合算法,并对常用的图像滤波算法以及算法组合进行了定量分析比较,仿真结果表明,中值滤波和形态学滤波的组合算法对于声呐图像中混合噪声的滤除是较为理想的.     

一种高分辨率3维图像的自适应降噪算法

为了获得高保真3维图像,采用了一种针对高分辨率3维图像的自适应均值降噪算法。首先使用一种由激光器、高分辨率3维相机、直线电机和计算机等设备组成的线激光高精度3维测量实验系统对自然纹理皮革进行测量。然后针对系统测量所得的高分辨率3维自然纹理图像(每英寸点数大于1000),进行了理论分析和实验验证,取得了降噪后的高保真3维图像数据,并与传统的均值滤波、小波变换滤波的降噪效果进行对比。结果表明,该算法能自动选取最优的降噪窗口,有效地去除3维图像的噪声信息,并保留高分辨率图像丰富的边缘、细节信息,最终得到高保真的高分辨率3维自然纹理图像。该实验结果对于高分辨率图像的降噪问题是十分有帮助的。     

基于脊向指纹滤波算法

滤波技术是指纹预处理中的关键步骤.本文提出了一种基于脊向指纹滤波预处理方法.首先利用改进后的极值滤波器对指纹图像进行滤波,再用本文所提出的脊向滤波器对结果进行处理.实验结果验证了此方法的有效性.     

基于改进双边滤波的多尺度运动目标检测方法

提出一种基于改进双边滤波的运动多尺度目标检测方法,以提高对弱小目标的检测能力.首先对视频或序列红外图像进行改进双边滤波处理,提高目标的对比度,同时抑制背景的边缘噪声及随机噪声.然后对目标进行三维匹配滤波,获得若干组速度匹配叠加强度图像.最后,在这些图像中进行基于NNLoG(归一化负LoG算子)的多尺度目标检测,得到序列图像或视频段的最佳匹配速度及增强后的图像.可最终计算出目标在序列图像或视频中的运动方程.通过大量的实验及对比实验可知,改进双边滤波、三维匹配滤波及NNLoG算子综合处理效果都较好,可有效检测序列图像或视频中的目标.     

基于CxImage库的SAR图像Speckle噪声抑制

合成孔径雷达图像的相干斑噪声抑制是SAR信息处理中的一个重要环节。经典的Speckle噪声抑制通常作为一个处理模块集成在软件中,但现有软件代码封装无法二次开发。针对文中提出了一种基于开源图像库CxImage的空间域自适应Speckle噪声抑制算法的应用与集成。将CxImage图像库链接入MFC应用程序框架中,利用其图像管理、维护、处理功能对SAR图像进行维护管理,集成多种经典的空间域自适应滤波方法,并以ERS-2卫星的PRI SAR数据为例,进行Speckle噪声滤波处理,选取适当的滤波效果评价参数,对滤波结果进行比较,最终得出各滤波算法应用于SAR图像滤波的优劣。     

基于发光二极管阵列的光学中值滤波的实现

提出一种铸影的非相干光学系统，由可编程的发光二极管阵列实时产生二值中值滤波函数。用电的方法完成取阈和求和运算来实现中值滤波。同时利用非线性阈值分解技术实现灰值中值滤波，该系统结构简单紧凑，取阈方便。     

基于改进脊波变换的红外视频图像高质量复原

为了实现红外视频降质图像高质量复原,采用一种基于改进脊波变换的图像复原算法来进行处理。该算法首先对降质图像进行改进脊波变换,然后提出一种具有加权改进自适应伪中值滤波算法对脊波系数进行处理,最后针对滤波后图像中时常出现的"环绕"现象,引入自适应Wiener滤波算法来抑制。进行了理论分析和实验验证,取得了相关模拟降质图像、真实降质图像以及峰值信噪比的测试数据。结果表明,该算法性能优于伪中值滤波以及两类脊波变换去噪算法。这对于实现红外视频降质图像复原研究是有帮助的。     

一种用于抑制椒盐噪声的多窗口中值滤波器

该文提出了一种用于抑制椒盐噪声的多窗口中值滤波算法。算法在招待过程中根据具体情况采用不同大小的滤波窗口。仿真结果表明，与标准中值滤波算法相比，该方法不仅可以有效去除图像中的椒盐噪声，特别是在噪声密度非常大的情况下，表现了很好的性。     

X光序列图像三维动态滤波技术

对于静态X光图像序列,帧间滤波技术是抑制噪声、提高信噪比最为有效的方法之一。然而对于活动图像序列,该技术会使运动目标模糊,从而使图像质量更差。本文提出了一种X光序列图像的三维动态滤波技术,把图像序列作为二维空间数据流,在时间上采用先进先出的方式在整个积分周期内进行多帧平均,实现了沿着运动轨迹进行帧间滤波,从而使多帧平均技术对于活动X光图像序列同样具有较好的处理效果。     

一种改进型图像降噪方法

针对图像获取中易受到噪声干扰的问题,介绍了图像去噪处理的几种常用算法（邻域平均法、中值滤波法、图像间的平均滤波）,对比阐述了各自的优缺点及适用范围。提出了一种基于平滑滤波的小波阈值图像去噪算法,该算法采用中值滤波和小波阈值相结合的方式对图像进行平滑处理。实验结果表明,该方法不仅有利于图像噪声的去除,而且边缘信息也得到了较好的保留,使图像具有更好的视觉效果,还原出图像的本来面目。     

Impulsive noise filtering based on noise detection in corrupted digital color images

This paper is an enhancement to our earlier research with grey-scale images. In this paper, we propose two new detection-estimation based image filtering algorithms that effectively remove corrupted pixels with impulsive noise in digital color images. The existing methods for enhancing corrupted color images typically possess inherent problems in computation time and smoothing out edges because all pixels are filtered. Our proposed algorithms first classify corrupted pixels in each channel or in each pixel. Because marginal or vector median filtering is only performed for the classified pixels, the process is computationally efficient, and edges are preserved well. In addition, because there is no appropriate criterion to evaluate the performance of impulsive noise detectors for color images, the objective comparison of noise detectors is difficult. Thus, we introduce a new efficiency factor for comparing the performance of noise detectors in digital color images. Simulation results show that the proposed algorithms perform better than existing methods, in both objective and subjective evaluations.This work was supported by the Korea Science & Engineering Foundation (KOSEF) under grant no. 981-0912-057-2.     

Switching degenerate diffusion PDE filter based on impulselike probability for universal impulse noise removal

In this paper, a switching degenerate diffusion partial differential equation filter (SDDPDE) is developed by introducing the switching operators for reducing all kinds of impulse noise, and especially for images having a mixture of salt-and-pepper impulse noise and random-valued impulse noise which is a shortage for most of the existing filtering models. Our SDDPDE consists of the coarse and fine filtering stages. In the coarse filtering stages, the switching operator depends on a simple noise detector. In the fine filtering stages, we introduce the notion of impulselike probability, and the switching operator depends on both a simple noise detector and impulselike probability. Our SDDPDE will denoise noise pixels detected by the coarse detector while further modify the so-called noise-free pixels according to impulselike probability. The main advantages of our SDDPDE over published approaches are its simplicity and universality. In addition, we demonstrate the performance of our SDDPDE via application to three standard test images, corrupted by salt-and-pepper impulse noise, random-valued impulse noise and mixed impulse noise with high-noise levels, and the comparison with the other well-known filters. Experimental results show that our SDDPDE achieves high peak signal-to-noise ratio and better visual effect.     

基于噪声检测的高密度椒盐噪声自适应滤波算法

为了减少图像中的椒盐噪声对后续图像处理的影响,针对高密度噪声污染图像,提出了基于噪声检测的高密度椒盐噪声滤波算法。噪声检测方法理论可靠,保证了较高的噪声检测率,根据噪声点邻域信号点分布的不同采用不同的策略,能最大限度的保护图像的细节信息,使得高密度噪声污染图像也能得到较好地恢复。实验结果表明,所提出的滤波算法具有较强的自适应性、较高的算法保真率及较好的滤波效果。     

Constant time joint bilateral filtering using joint integral histograms

In this brief, we present a constant time method for the joint bilateral filtering. First, we propose an image data structure, coined as joint integral histograms (JIHs). Extending the classic integral images and the integral histograms, it represents the global information of two correlated images. In a JIH, the value at each bin indicates an integral determined by the two images. Then, the joint bilateral filtering is transformed to computation and manipulation of histograms. Utilizing the JIHs, we are capable of joint bilateral filtering in constant time. Its performance is validated in a digital photography approach using Flash-noFlash image pairs. Compared with the brute-force method, the proposed method achieves a speedup factor of 2-3 orders of magnitude while producing similar filtering results.     

Clustering to improve matched filter detection of weak gas plumesin hyperspectral thermal imagery

The use of matched filters on hyperspectral data has made it possible to detect faint signatures. This study uses a modified k-means clustering to improve matched filter performance. Several simple bivariate cases are examined in detail, and the interaction of filtering and partitioning is discussed. The authors show that clustering can reduce within-class variance and group pixels with similar correlation structures. Both of these features improve filter performance. The traditional k-means algorithm is modified to work with a sample of the image at each iteration and is tested against two hyperspectral datasets. A new “extreme” centroid initialization technique is introduced and shown to speed convergence. Several matched filtering formulations (the simple matched filter, the clutter matched filter, and the saturated matched filter) are compared for a variety of number of classes and synthetic hyperspectral images. The performance of the various clutter matched filter formulations is similar, all are about an order of magnitude better than the simple matched filter. Clustering is found to improve the performance of all matched filter formulations by a factor of two to five. Clustering in conjunction with clutter matched filtering can improve fifty-fold over the simple case, enabling very weak signals to be detected in hyperspectral images     

基于中值滤波和ICA的军事目标特征提取方法

针对电子侦察中存在的干扰或多特征重叠问题,提出一种基于中值滤波和独立分量分析(ICA)的军事目标特征提取方法。首先借助中值滤波对含噪声侦察图像进行去噪处理,然后利用无预白化的快速不动点(FastICA)算法对去噪后的侦察图像做目标特征的提取,最后再次通过中值滤波对提取的目标特征图像进行残留噪声的去除。仿真实验结果表明,该方法对噪声的去除效果较好,满足了人们的视觉需求;同时,提取的目标特征图像能够自主地分辨出不同的目标特征,去除掉重叠的目标特征。该方法具有较强的实用性和有效性。     

数字图像中值滤波技术研究

中值滤波作为图像处理中的一种非线性滤波技术,在有效抑制脉冲噪声的同时能很好地保护图像信号的细节信息,尤其是在处理椒盐噪声方面效果较好,得到了广泛的研究和应用。文章通过对中值滤波及其改进方法的研究,比较了不同方法的运算效率及对不同图像的去噪效果,分析中值滤波技术的研究方向。     

紫外图像处理方法

针对紫外成像存在噪声干扰的问题,为获取高品质的紫外图像及对目标准确检测,对典型紫外成像系统进行了研究。分析了紫外成像噪声干扰原因,建立了紫外目标场景模型。并以此研究紫外图像复原方法,对单幅图像进行门限处理,去掉背景噪声。然后对连续多帧图像进行均值处理和时域递归滤波,阈值分割后得到较好的图像输出。从图像输出结果可以看出,多帧图像均值处理和时域递归滤波都能有效地去除紫外图像中的随机噪声,特别是时域递归滤波方法对于图像复原有更好的效果。     

A new similarity measure for non-local means filtering of MRI images

In this paper, the application of non-local means (NLM) filtering on MRI images is investigated. An essential component of any NLM-based algorithm is its similarity measure used to compare pixel intensities. Unfortunately, virtually all existing similarity measures used to denoise MRI images have been derived under the assumption of additive white Gaussian noise contamination. Since this assumption is known to fail at low values of signal-to-noise ratio (SNR), alternative formulations of these measures which take into account the correct (Rician) statistics of the noise are required. Accordingly, the main contribution of the present work is to introduce a new similarity measure for NLM filtering of MRI images, which is derived under bona fide statistical assumptions and proves to posses important theoretical advantages over alternative formulations. The utility and viability of the proposed method is demonstrated through a series of numerical experiments using both in silico and in vivo MRI data.