基于小波变换的图像混合噪声自适应滤除算法

为同时滤除图像中的椒盐噪声和高斯噪声,提出了一种基于小波变换的混合噪声自适应滤除算法,该算法首先采用中值滤波去除椒盐噪声,然后借助边缘检测算子区将图像为分边缘与非边缘区域,进一步对非边缘区域引入改进的均值滤波器,有效削弱高斯噪声的同时保护图像边缘细节,既初步削弱高斯噪声又保护了边缘,最后采用改进的小波阈值滤波算法,对不同的小波系数采用不同的阈值函数,通过线性回归得到各最优阈值关系式。实验结果表明,该混合噪声自适应滤除算法能有效滤除椒盐噪声和高斯噪声,在图像主观质量和客观质量上均取得了较好的效果,能提高去噪图像峰值信噪比0.5～2．0 dB。     

应用于增量式光电编码器的相关自适应滤波方法

为了改善增量式光电编码器的莫尔条纹光电信号质量,降低主计数信号中高次谐频的影响,提高信噪比达到输出稳定的正弦波形,本文深入地研究了莫尔条纹光电信号中的高频信号和噪音信号产生的原因及其相关自适应滤除方法.从宏观和微观两个方案分析光栅副的屏函数,研究莫尔条纹光电信号输出波形的不确定原因,并且提出采用相关自适应滤波方法将高频信号滤除保留原有基频信号.从相关自适应滤波的数学模型论证和推导该相关自适应滤波过程并讨论其自适应性.指出在设计该自适应滤波电路时需要重视的几个关键指标及其影响效果,完成相关自适应滤波的有效性分析.相关自适应滤波效果等价于一个中心频率为基频的窄带带通滤波器,当基频随机变化时其通带也会随之改变.通过相关自适应滤波技术较好地滤除计数信号中除基频外所有高频分量和放大电路所引入的加性高斯白噪音.实验证明：莫尔条纹光电信号的失真度被降低8倍相关自适应滤波方法能使莫尔条纹光电输出波形趋近于标准正弦波,获得更高的信噪比.     

复杂背景下红外弱小目标检测算法研究

复杂背景下低信噪比弱小目标的检测是红外预警系统中的重点和难点。为解决红外图像中杂波干扰多、目标信噪比低等问题,提出一种非线性空间滤波的目标检测方法。该算法在传统线性空间滤波算法的基础上,通过对预测点周围4个象限的背景灰度值进行计算,并动态地调节阈值,以达到突出小目标的目的。试验结果表明:当背景包含较多复杂因素时,采用非线性空间滤波的检测方法可有效地抑制杂波,实现弱小目标的提取,与线性滤波算法结果相比较,虚警数降低了3/4,且易于工程实现。     

一种改进的球栅阵列封装焊点射线图像阈值分割算法

球栅阵列封装焊点的射线图像具有信噪比差、背景不均匀等特点,故传统的阈值分割方法无法将目标焊点与背景图像很好的分割.本文通过对球栅阵列封装焊点射线图像直方图的分析,利用了自适应维纳滤波对阈值分割前的图像进行了预处理.根据图像的差异来调整该滤波器的参量,对局部差异大的地方进行小的平滑操作,对局部差异小的地方进行大的平滑操作.在最大类间方差法的基础上,对分割后的图像进行了进一步的分析并提出了改进的二次分割方法.改进的方法为并不直接通过OTSU法进行二值化处理来去除背景,而是在阈值分割得到的两个灰度级内通过计算中值和统计最大灰度像素的方法得到了更优化的阈值,使得去除背景后的焊点图像整体更加清晰和均匀.在背景灰度级内寻找了一个合适的灰度级作为处理后的灰度图像新背景,实验证明该方法明显改进了传统最大类间方差法对球栅阵列封装焊点射线图像的阈值分割效果.     

层次卷积滤波红外弱小目标检测方法

针对单帧复杂背景红外图像点目标检测算法存在复杂背景下处理效果不理想、处理时间长的问题,提出了一种层次卷积滤波检测算法。主要分为两个部分：第一,根据红外小目标特性,设计一种层次卷积滤波的算子,对图像进行滤波处理,实现图像中小目标的增效和背景抑制的效果;第二,采用基于最大值的自适应阈值方法,对图像进行二值化操作,过滤背景杂波,最终提取到待检测的目标。在大量不同背景红外图像中进行实验,论文算法在背景抑制因子和信噪比增益的性能量化结果上优于现有5种典型红外弱小目标检测算法的性能结果,且平均处理时间仅为高斯拉普拉斯（Laplacian of Gaussian,LoG）滤波算法的30.42%。通过实验对比,表明该层次卷积滤波算法可以有效解决在不同复杂背景下的红外图像中对小目标检测的问题。     

基于数学形态学的像增强器缺陷的图像检测方法

介绍了一种利用图像阈值分割和数学形态学噪声处理的像增强器缺陷的检测方法,通过对阈值分割二值化后的缺陷图像进行形态滤波,达到了自动检测缺陷的目的。该方法不受缺陷形状和位置的影响,能够快速准确的识别出其缺陷,自动判别缺陷的准确性和实际用人眼观察缺陷的结果相一致。实验结果证明了该方法在实际应用中的可行性。     

空间信息自适应融合的高光谱图像分类方法

针对单一的滤波器提取高光谱图像空间纹理信息时不能获得完整的图像特征的不足,提出一种结合双边滤波和域转换标准卷积滤波的高光谱图像分类算法.该方法采用空间信息自适应融合的分类寻优,先对高光谱波段进行抽样分组,再用双边滤波和域转换标准卷积滤波对分组后的波段进行滤波,两种空间信息进行线性融合后交由支持向量机完成分类.实验表明,相比使用光谱信息、高光谱降维、空谱结合的支持向量机分类方法和边缘保持滤波以及递归滤波的方法,本文所提算法对高光谱图像的分类精度有较大提高,在训练样本仅为5%和3%的情况下,对印第安农林和帕维亚大学图像的总体分类精度分别达到了96.95%和97.89%,比其他算法高出213个百分点,验证了该方法在高光谱图像分类的有效性.     

小波域局部维纳滤波器估计误差分析及图像去噪

对小波域局部维纳滤波算法的估计误差进行了理论分析,推导了估计误差平方期望表达式,得到了一种观测系数局部方差估计的阈值.以此为基础,提出了一种小波域图像去噪算法.先对观测系数做阈值化处理,再进行局部自适应维纳滤波.实验结果表明,该方法提高了真实信号系数方差估计的准确度,在去噪性能上优于Mihcak等提出的LAWML算法.     

基于改进遗传算法优化的形态学修正TOP-HAT滤波器设计方法

针对红外序列图像中运动弱小点目标的检测问题,设计了一种基于改进遗传算法优化的修正Top-Hat形态学滤波器算子。其中,优化的修正Top-Hat形态学滤波器可以很好地抑制背景和噪声的影响;改进遗传算法采用新的区间离散化编码和自适应的主次式交叉与变异算子,通过优化搜索全局空间得到的形态学滤波器参量具有较好的滤波性及时效性。并且针对不同信噪比的点目标检测建立了自适应门限。实测数据的处理结果表明:在虚警概率小于5%情况下,优化的修正Top-Hat形态学滤波器算子对信噪比约为2的复杂图像检测概率大于等于70%,与固定结构元素的Top-Hat形态学滤波器相比检测概率提高了近10%,与用经典遗传算法训练的传统Top-Hat形态学滤波器相比检测概率提高了4%。     

Speech enhancement based on the discrete Gabor transform and multi-notch adaptive digital filters

This paper presents a new method to speech enhancement based on time-frequency analysis and adaptive digital filtering. The proposed method for dual-channel speech enhancement was developed by tracking frequencies of corrupting signal by the discrete Gabor transform (DGT) and implementing multi-notch adaptive digital filter (MNADF) at those frequencies. Since no a priori knowledge of the noise source statistics is required this method differs from traditional speech enhancement methods. Specifically, the proposed method was applied to the case where speech quality and intelligibility deteriorate in the presence of background noise. Speech coders and automatic speech recognition (ASR) systems are designed to act on clean speech signals. Therefore, corrupted speech signals by the noise must be enhanced before their processing. The method uses a primary input containing the corrupted speech signal while a reference input containing the noise only. In this paper, we designed MNADF instead of single-notch adaptive digital filter and used DGT to track frequencies of corrupting signal because fast filtering process and fast measure of the time-dependent noise frequency are of great importance in speech enhancement process. Therefore, MNADF was implemented to take advantage of fast filtering process. Different types of noises from Noisex-92 database were used to degrade real speech signals. Objective measures, the study of the speech spectrograms and global signal-to-noise ratio (SNR), segmental SNR (segSNR), Itakura-Saito distance measure as well as subjective listing test demonstrated consistently superior enhancement performance of the proposed method over traditional speech enhancement method such as spectral subtraction. Combining MNADF and DGT, excellent speech enhancement was obtained.     

An adaptive clutter rejection method based on AR model in color flow imaging

The ultrasound Doppler signal scattered from blood is heavily corrupted by the clutter signal reflected from slowly moving muscular tissue. The Doppler frequency shift of blood flow and clutter in different parts of human body greatly changes. Therefore, if a fixed wall filter is selected, the optimal filtering effect can not be attained. An adaptive clutter rejection method is proposed in this paper, which consists of a weak clutter rejector and a 2-order AR estimator. The clutter and blood power thresholds were preliminary defined in the weak clutter rejector. The echo power is compared with the two pre-defined thresholds, and the result was used to select an appropriate wall filter. The output of the weak clutter rejector is estimated by a 2-order AR estimator and two poles are acquired. The low frequency pole denotes the clutter signal and the high frequency pole denotes the blood signal. Before the AR estimation, a static signal is added to avoid producing split spectral peaks. It is illustrated in the simulation that the proposed method can detect the slower blood flow with smaller variance compared with the traditional wall filtering method.     

核密度估计时-空域滤波红外背景抑制方法

提出一种基于核密度估计的时-空域滤波算法,用于红外搜索跟踪系统图像的背景抑制。算法分为空域滤波和时域滤波两部分。在空域滤波中,采用核密度估计算法对背景进行平滑;在时域滤波中,采用核密度估计算法对经过空域滤波后的图像灰度值进行概率计算,判别属于背景残差的灰度值,然后做进一步的滤除。核方法对背景有很好的光滑性且易于计算机实现,实验表明,这种非参方法设计的时-空域滤波算法对背景杂波有非常良好的抑制效果,信噪比也得到明显提高。     

核密度估计时-空域滤波红外背景抑制方法

提出一种基于核密度估计的时-空域滤波算法,用于红外搜索跟踪系统图像的背景抑制。算法分为空域滤波和时域滤波两部分。在空域滤波中,采用核密度估计算法对背景进行平滑;在时域滤波中,采用核密度估计算法对经过空域滤波后的图像灰度值进行概率计算,判别属于背景残差的灰度值,然后做进一步的滤除。核方法对背景有很好的光滑性且易于计算机实现,实验表明,这种非参方法设计的时-空域滤波算法对背景杂波有非常良好的抑制效果,信噪比也得到明显提高。     

Efficient array beam forming by spatial filtering for ultrasound B-mode imaging

This paper proposes an efficient array beam-forming method using spatial matched filtering (SMF) for ultrasonic imaging. In the proposed method, ultrasonic waves are transmitted from an array subaperture with fixed transmit focus as in conventional array imaging. At receive, radio frequency echo signals from each receive channel are passed through a spatial matched filter that is constructed based on the system transmit-receive spatial impulse response. The filtered echo signals are then summed without time delays. The filter concentrates and spatially registers the echo energy from each element so that the pulse-echo impulse response of the summed output is focused with acceptably low side lobes. Analytical beam pattern analysis and simulation results using a linear array show that this spatial filtering method can improve lateral resolution and contrast-to-noise ratio as compared with conventional dynamic receive focusing (DRF) methods. Experimental results with a linear array are consistent but point out the need to address additional practical issues. Spatial filtering is equivalent to synthetic aperture methods that dynamically focus on both transmit and receive throughout the field of view. In one common example of phase aberrations, the SMF method was degraded to a degree comparable to conventional DRF methods.     

靶场外弹道数据处理中的实时野值剔除算法

针对靶场外弹道数据处理过程中数据实时预处理的需要,在对最小二乘估计野值剔除算法分析的基础上,提出一种计算量小,满足实时性要求,且对雷达、光电经纬仪等大型测试设备数据预处理具有通用性的自适应野值剔除算法。该算法把连续5个实测数据的标准差的3倍作为阈值,以此判定下一个数据点是否为野值,最终完成整个弹道的实时野值剔除。通过Matlab仿真验证及实测数据分析表明,该算法对孤立野值点的剔除率可以达到100%,适用于靶场外弹道数据的实时预处理。     

基于局部端元光谱表征的地表水体遥感自适应提取

精确的光谱表征是遥感专题地物信息提取的重要前提。针对不同水体光谱之间特征差异性,给水体信息全局光谱表征带来不确定性,提出了基于局部端元光谱表征的各类水体自适应提取方法。首先通过全局光谱指数计算实现水体的初步识别;然后,建立局部专题地物目标区,进行PPI计算提取局部水体端元;第三,通过计算局部区域内水体端元光谱相似度,统计光谱相似度直方图;最后,分析直方图分布特征,选择初始分割阈值,通过逐步迭代自动调整阈值达到最佳阈值选择,实现局部水体单元的自适应高精度提取。通过不同类型湖泊水体提取实验表明,该方法要优于传统的全局水体光谱指数阈值分割法,能够准确、 自动地提取遥感影像上水体分布信息,几乎不受水体光谱特征差异的影响。     

A general strategy for anisotropic diffusion in MR image denoising and enhancement

Anisotropic diffusion (AD) has proven to be very effective in the denoising of magnetic resonance (MR) images. The result of AD filtering is highly dependent on several parameters, especially the conductance parameter. However, there is no automatic method to select the optimal parameter values. This paper presents a general strategy for AD filtering of MR images using an automatic parameter selection method. The basic idea is to estimate the parameters through an optimization step on a synthetic image model, which is different from traditional analytical methods. This approach can be easily applied to more sophisticated diffusion models for better denoising results. We conducted a systematic study of parameter selection for the AD filter, including the dynamic parameter decreasing rate, the parameter selection range for different noise levels and the influence of the image contrast on parameter selection. The proposed approach was validated using both simulated and real MR images. The model image generated using our approach was shown to be highly suitable for the purpose of parameter optimization. The results confirm that our method outperforms most state-of-the-art methods in both quantitative measurement and visual evaluation. By testing on real images with different noise levels, we demonstrated that our method is sufficiently general to be applied to a variety of MR images.     

闪光照相CCD图像的自适应中值滤波方法

中值滤波是一种在去除噪声的同时能较好地保护图像边缘细节的非线性图像处理方法。为了滤除闪光照相CCD图像中的脉冲噪声，同时能更好地保护图像边缘，提出了一种改进的自适应中值滤波方法。该方法采用局部中值和局部方差作为判断噪声点的阈值，实现了局部自适应的中值滤波，克服了传统自适应中值滤波方法的缺点，对椒盐噪声和随机脉冲噪声均有较好的滤波效果。实验结果表明，该方法消除图像脉冲噪声十分有效，对闪光照相CCD图像的处理结果也较好。     

Electro- and photo-controllable spatial filter based on?a?liquid crystal film with a photoconductive layer

This work presents an electro- and photo-controllable spatial filter that is based on a liquid crystal (LC) film with a photoconductive layer. The controllable spatial filter can be formed because of the controllability of the photoelectro-induced screen effect of the space charge in the LC cell. An applied dc voltage or incident pumped intensity can be controlled to enable different spatial distributions of the diffraction pattern of the target object to be selected for filtering by the LC cell, such that various reconstructed images can be obtained. A simulation using Fourier analysis is developed, and its results agree closely with experimental results. Additionally, the LC spatial filter has the extra advantage of controllable low or high filtering functions: they are controlled by switching the configuration between normally black and normally white modes.     

基于空间频率域滤波的漫反射光大气湍流传输数值仿真方法

在基于目标回光的激光瞄准系统和目标在回路(TIL)系统中都要使用目标照明漫反射回光作为系统工作的基础.数值仿真在研究光波传输特性的过程中一直发挥着重要作用.由于漫反射光场发散角很大,给数值计算带来困难.一般的处理方法是进行分段空间域滤波,但这会大大增加计算量.提出一种计算漫反射光传榆的频率域滤波方法.频域滤波基本不增加...