基于小波包分解和形态学融合的地基云图边缘检测

采用一种基于小波包分解和数学形态学融合的边缘提取方法用于地基云图的边缘检测．首先对图像进行基于小波包分解的边缘提取,然后对图像进行数学形态学的边缘检测,最后利用图像融合技术对两幅图像进行融合得出最优边缘图像．通过Matlab软件验证和比较,表明该方法实际使用效果较好     

基于Bubble小波函数的印鉴鉴别

利用Bubble小波函数，通过Mallat算法可对印鉴图像进行小波分解和边缘检测，在此基础上，提取由傅里叶系数所导出的形状特征，然后利用神经网络分类器进行训练，继而实现印鉴的自动鉴别。     

基于多尺度小波边缘检测的对流降水和闪电分析

为准确估测对流降水,提出了多尺度小波边缘检测算法,借鉴小波变换的多尺度分析和边缘检测原理,对雷达反射率数据进行处理,有效地识别出对流云回波区域,进而反演对流降水,精细化分析其和闪电的相关关系。选取了南京2009年7月6—7日的一次强雷暴降水过程中对流活动较剧烈的180 min为例,对采自南京龙王山雷达站的真实数据进行了算法识别分析,反演得到对流降水,统计6 min时间间隔内的闪电频数与同期降水进行相关分析,得到该时段中每次闪电所表征的降水量（RPF）的范围在7.47×106～4.46×107 kg,平     

3种阈值方法在闪电通道图像识别中的应用

该文实现了3种常见的阈值算法在闪电通道识别中的应用。针对某些闪电通道图像对比度较低的特点,首先对原始图像去除背景光照不均的影响并进行图像增强,再根据所处理的闪电通道图像的具体情况,选择全局自适应阈值法、局部自适应阈值法或基于Otsu自适应阈值的Canny算子对增强后的图像进行边缘检测,进而对闪电通道进行识别。对所提取到的闪电通道进行形态学和细化处理,最终提取到由连续的像素点序列表示的闪电通道。针对数字图像中闪电通道背景的不同特点,文中提到的3种阈值方法能够有针对性地对不同图像进行通道检测,在保证准确获得清晰边缘信息的同时,较好地兼顾了对弱边缘信息的提取,有效保证了对闪电通道的识别效果。     

基于树状小波分解的图像混合编码方法

提出了一种基于树状小波分解的图像混合编码技术，它能够在一定的能量准则下自适应地对图像进行分解，并根据不同尺度子带在小波图像中的地位和特点，选取相应的编码方法，模拟实验证明此方法在较好的图像质量情况下可获得较高的压缩比。     

基于小波域高斯尺度混合模型的天气雷达图像高分辨率插值

采用可精确刻画雷达回波强度数据统计特征的小波域高斯尺度混合（GSM）模型作为雷达图像先验模型,进行天气雷达图像插值,在提高图像分辨率的同时有效重建降水回波中局部强回波值、小尺度变化细节等一些重要空间分布统计特征。分析和总结雷达回波强度数据小波频率域统计特点,建立小波域GSM模型;匹配天气雷达图像小波系数和GSM模型,利用贝叶斯理论估计更小尺度的小波系数,进行小波逆变换,完成高分辨率天气雷达图像插值。试验表明,该算法能从低分辨率图像中估计出高分辨率高频系数,且所利用的先验模型充分考虑降水数据本身的特点,可有效捕获降水回波结构的非高斯特征和局部相关特性,重建雷达图像中的局部变化细节。      

小波变换在气象卫星云图压缩中的应用

该文分析了卫星云图和自然图像在小波系数分布上的特征差异,提出了基于小波变换的气象卫星云图压缩方案。方案选择对卫星云图压缩效果较好的、具有双正交性的Odegard 9/7滤波器组对卫星云图进行五级小波分解和重构;根据卫星云图小波分解系数相似性强、低频分量能量更为集中、分量系数层次衰减性明显的特点,使用改进后的零树编码算法对小波系数进行编码运算;最后,采用高效的自适应算术编码对输出数据流进行了进一步的压缩。该方法对卫星云图的压缩效果要优于经典的嵌入式零树小波编码,在失真允许情况下,对红外云图的最大压缩比可达40:1,水汽云图达60:1,可见光云图达35:1。     

小波频率估计方法及其在通信信号检测中的应用

小波变换作为新的信号分析工具,受到学者们的重视,然而如何将小波尺度与频率联系起来,是一个亟待解决的问题.为了求解小波的中心频率,提出了一种基于正弦波的模型,并将模型推广到任意采样间隔与任意小波尺度.由于模型的非凸性,传统的基于梯度的优化方法容易陷入局部最优,因此提出了一种基于Rossler吸引子的粒子群算法.实验给出了常见小波的频率值,并通过一个通信信号检测的例子证明了该方法的有效性.     

小波分析思想在中尺度对流活动研究中的应用

应用近些年在大气科学领域被广泛采用的小波分析方法,基于小波分析思想提取GMS红外卫星云图中的中-β尺度对流云信息,分析中-β尺度对流活动特征.构造了理想小波函数模板,对四种理想云模型进行了实验分析,建立了小波变换提取云的中心位置和水平尺度等信息的方法.并应用该方法对中-β尺度对流活动实例进行分析和追踪实验.此外,还应用小波分析方法对2001年夏季(6～8月)我国中、东部地区的中-β尺度对流活动进行了统计分析,揭示出了这一区域中-β尺度对流活动的分布规律、强度变化等特征.     

基于方向特征显著性计算的MRI脑图像肿瘤检测

本文提出了一种利用方向性特征进行显著性建模的MRI脑图像肿瘤自动检测算法.该模型首先将MRI脑图像进行预处理,去除掉图像中头骨区域的干扰;然后使用基于方向特征的显著性检测增加病变区域的对比度,实现更准确地提取肿瘤图像区域.本文算法在脑图像数据集上进行了大量的实验,并且与主流的肿瘤自动检测方法进行了对比,证明了本文算法的有效性,并为医生提供可靠的辅助诊断和临床参考.     

小波框架的彩色图像泊松去噪

噪声是影响图像质量的重要因素,噪声的存在导致图像的某些特征细节不能被辨识以及图像的信噪比下降,所以图像去噪是图像处理中的一个重要问题.本文提出了基于小波框架的彩色图像的泊松去噪模型.在该模型中,赋权的l²项作为保真项,包含小波框架的l¹项作为正则项.同时,又提出了解决该模型的重新赋权的split Bregman算法.最后,利用仿真实验以及PSNR（峰值信噪比）指标对该模型的去噪效果进行评估,评估结果表明该算法可行、有效.     

Statistical Modeling with a Hidden Markov Tree and High-resolution Interpolation for Spaceborne Radar Reflectivity in the Wavelet Domain

With the increasing availability of precipitation radar data from space, enhancement of the resolution of spaceborne precipitation observations is important, particularly for hazard prediction and climate modeling at local scales relevant to extreme precipitation intensities and gradients. In this paper, the statistical characteristics of radar precipitation reflectivity data are studied and modeled using a hidden Markov tree (HMT) in the wavelet domain. Then, a high-resolution interpolation algorithm is proposed for spaceborne radar reflectivity using the HMT model as prior information. Owing to the small and transient storm elements embedded in the larger and slowly varying elements, the radar precipitation data exhibit distinct multiscale statistical properties, including a non-Gaussian structure and scale-to-scale dependency. An HMT model can capture well the statistical properties of radar precipitation, where the wavelet coefficients in each sub-band are characterized as a Gaussian mixture model (GMM), and the wavelet coefficients from the coarse scale to fine scale are described using a multiscale Markov process. The state probabilities of the GMM are determined using the expectation maximization method, and other parameters, for instance, the variance decay parameters in the HMT model are learned and estimated from high-resolution ground radar reflectivity images. Using the prior model, the wavelet coefficients at finer scales are estimated using local Wiener filtering. The interpolation algorithm is validated using data from the precipitation radar onboard the Tropical Rainfall Measurement Mission satellite, and the reconstructed results are found to be able to enhance the spatial resolution while optimally reproducing the local extremes and gradients.     

一种高分辨率遥感影像的数学形态学滤波算法

应用数学形态学理论,结合高空间分辨率遥感影像的特点,选择包含全部方向的结构元素,设计一种基于全方位结构元素的加权多级滤波算法,用于高空间分辨率遥感影像的处理。算法中使用峰值信噪比方法自适应地确定各个方向结构元素的权重。实验结果表明：与结构元素尺度形状单一的形态学滤波算法相比,全方位结构元素的加权多级滤波算法在噪声抑制和细节保持上均有较好的性能,峰值信噪比有了更大的提高,而且更适合于对高空间分辨率遥感影像的噪声去除。     

An introduction to combined Fourier–wavelet transform and its application to convectively coupled equatorial waves

Convectively coupled equatorial waves (CCEWs) are major sources of tropical day-to-day variability. The majority of CCEWs-related studies for the past decade or so have based their analyses, in one form or another, on the Fourier-based space–time spectral analysis method developed by Wheeler and Kiladis (WK). Like other atmospheric and oceanic phenomena, however, CCEWs exhibit pronounced nonstationarity, which the conventional Fourier-based method has difficulty elucidating. The purpose of this study is to introduce an analysis method that is able to describe the time-varying spectral features of CCEWs. The method is based on a transform, referred to as the combined Fourier–wavelet transform (CFWT), defined as a combination of the Fourier transform in space (longitude) and wavelet transform in time, providing an instantaneous space–time spectrum at any given time. The elaboration made on how to display the CFWT spectrum in a manner analogous to the conventional method (i.e., as a function of zonal wavenumber and frequency) and how to estimate the background noise spectrum renders the method more practically feasible. As a practical example, this study analyzes 3-hourly cloud archive user service (CLAUS) cloudiness data for 23 years. The CFWT and WK methods exhibit a remarkable level of agreement in the distributions of climatological-mean space–time spectra over a wide range of space–time scales ranging in time from several hours to several tens of days, indicating the instantaneous CFWT spectrum provides a reasonable snapshot. The usefulness of the capability to localize space–time spectra in time is demonstrated through examinations of the annual cycle, interannual variability, and a case study.     

用连续子波变换提取城市冠层大气湍流的相干结构

切变湍流的相干结构是湍流研究中的重大发现,它表明湍流在表面上看来不规则运动中具有可检测的有序运动,这种相干结构在切变湍流的脉动生成和发展中起着主宰作用.因此识别和提取相干结构对于认识和研究湍流是非常重要的.用数字滤波法将包含相干结构的大尺度信号提取出来以后,再用子波分析,根据子波能量极大值的判别方法,分别确定出大气湍流三个方向上的速度脉动信号相干结构的频率或时间尺度,然后由确定尺度上的连续子波反演公式,提取出大气湍流三个方向上的速度脉动信号相干结构所对应的波形.     

A conceptual framework for time and space scale interactions in the climate system

 Interactions involving various time and space scales, both within the tropics and between the tropics and midlatitudes, are ubiquitous in the climate system. We propose a conceptual framework for understanding such interactions whereby longer time scales and larger space scales set the base state for processes on shorter time scales and smaller space scales, which in turn have an influence back on the longer time scales and larger space scales in a continuum of process-related interactions. Though not intended to be comprehensive, we do cite examples from the literature to provide evidence for the validity of this framework. Decadal time scale base states of the coupled climate system set the context for the manifestation of interannual time scales (El Nino/Southern Oscillation, ENSO and tropospheric biennial oscillation, TBO) which are influenced by and interact with the annual cycle and seasonal time scales. Those base states in turn influence the large-scale coupled processes involved with intraseasonal and submonthly time scales, tied to interactions within the tropics and extratropics, and tropical–midlatitude teleconnections. All of these set the base state for processes on the synoptic and mesoscale and regional/local space scales. Events at those relatively short time scales and small space scales may then affect the longer time scale and larger space scale processes in turn, reaching back out to submonthly, intraseasonal, seasonal, annual, TBO, ENSO and decadal. Global coupled models can capture some elements of the decadal, ENSO, TBO, annual and seasonal time scales with the associated global space scales. However, coupled models are less successful at simulating phenomena at subseasonal and shorter time scales with hemispheric and smaller space scales. In the context of the proposed conceptual framework, the synergistic interactions of the time and space scales suggest that a high priority must be placed on improved simulations of all of the time and space scales in the climate system. This is particularly important for the subseasonal time scales and hemispheric and smaller space scales, which are not well simulated at present, to improve the prospects of successfully forecasting phenomena beyond the synoptic scales. Received: 3 April 2000/ Accepted: 6 November 2000     

图像小波域维纳滤波去噪算法的改进

介绍了小波变换和维纳滤波相结合的图像小波域维纳滤波去噪算法，在分析该算法最小均方误差期望的基础上提出一种改进算法。指出在进行维纳滤波之前，阈值化处理可以有效提高维纳滤波的性能。用标准测试图像的处理结果来比较改进前后两种算法的效果。     

The spatial structure of turbulence at production wavenumbers using orthonormal wavelets

Orthonormal wavelet expansions are applied to surface-layer measurements of vertical wind speed under various atmospheric, stability conditions. The orthonormal wavelet transform allows for the unfolding of these measurements into space and scale simultaneously to reveal the large intermittent behavior in space for the turbulent production wavenumbers. Both Fourier and wavelet power spectra indicated the existence of a –1 power law for the vertical velocity measurements at the production wavenumbers. The –1 power law in the turbulent production range was derived from surface-layer similarity theory. A dimensionless skewness, structure function is applied to the wavelet decomposed vertical velocity field to trace the destruction of the shear-or buoyancy-induced anisotropy under various stability conditions. The structure skewness function revealed shear- or buoyancy-induced eddy asymmetry dependence on stability at each scale within the –1 power-law wavenumber range with more isotropy during propagation from smaller to larger wavenumbers. The asymmetry of these events at the turbulent production wavenumbers appeared very localized in space, as well as in scale, and could be described with a simple eddy-overturning model. It is demonstrated that the wavelet transform is suitable for such analysis.