共查询到19条相似文献，搜索用时 140 毫秒
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非负矩阵分解是一种提取图像原始信息局部特征的新方法,第二代Curvelet变换是一种效果较好的多尺度变换分析方法。结合两者特征提出一种基于NMF和Curvelet的遥感图像的融合方法,首先对已配准的多光谱图像和全色图像进行Curevelet分解,得到各层系数（Coarse、Detail和Fine尺度层）。然后对Coarse尺度层（低频系数）进行NMF分解,提取出包含特征基的低频系数;对Detail和Fine尺度层（高频系数）采用方差为测度参数进行邻域融合。最后进行Curevelet逆变换得到融合图像。实验结果表明,该方法的融合图像能较好地保留光谱信息,并在空间细节信息上得到改善,优于小波方法、Curvelet等方法。 相似文献
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提出了一种基于小波变换的图像加水印及检测的新方法,即对图像文件进行二维多尺度离散小波变换,得到图像数据高频和低频部分。在高频部分数据中,加入随机数水印并进行图像重构,检测时以同样方法进行小波分解,检测高频部分与随机数水印的相关性。此方法可用于图像防伪。 相似文献
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介绍了小波图像的分解和重构方法以及小波融合过程。采用Symlet小波变换融合方法对西安地区鲸鱼沟水库的TM4、TM5子图进行单尺度二维离散小波变换融合，并进行水库的边缘检测。对图像通过TM4分解的低频图与TM5分解的高频图像融合再与TM4影像原图比较，融合图像提取细节效果明显优于TM4原图像。 相似文献
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《物探化探计算技术》2018,(6)
常规的随机噪声压制方法面临着噪声频带与有效信号频带重叠,在压制噪声的同时对有效信号造成损害的局限性,基于小波变换和奇异值分解的思路,提出了一种小波变换与奇异值分解相结合的去噪方法,以单道信号作为处理单元,通过小波变换得到小波系数矩阵,并对此矩阵进行奇异值分解,进而求得能够反应信号与噪声变化的奇异熵,根据奇异熵确定阀值,进行SVD重构小波系数矩阵,最后小波逆变换重构信号,达到去除随机噪声的目的。此种方法对满足高斯白噪和不满足高斯白噪条件的随机干扰,均有去除效果。经理论信号与相关实际资料的处理证明,这种小波变换与奇异值分解相结合的去噪方法有效而实用。 相似文献
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《物探化探计算技术》2015,(4)
针对第一类、第二类Mallat系列小波的滤波器公式为频率域的表达式,它们无法直接求得对应系列小波的滤波器系数,从而造成了Mallat系列小波在时间域内多尺度计算的困难。这里基于傅里叶分析原理,借用MATLAB工作平台编制了由频率域到时间域的Mallat系列小波滤波器组的求解代码。该段代码解决了Mallat系列小波在时间域内的滤波器系数求解。经实际数字计算表明,采用该方法求得的滤波器组系数进行多尺度分析,能够实现小波的完全分解与重构,这对选用Mallat系列小波中的具体小波进行异常检测分析,具有十分重要的意义。 相似文献
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利用连续小波变换方法对潜水位进行气压和潮汐改正 总被引：1，自引：0，他引：1
以河北省农林科学院衡水市旱作节水农业实验站(旱作所)H5观测资料为例,利用连续小波变换(CWT)对气压、理论固体潮和浅层地下水位进行了多时间尺度分析,结合小波方差分析确定了气压、固体潮和地下水位等水文时间序列的主要周期(或频率)成分和能量分布;再运用连续小波逆变换在主要周期尺度上重构了气压、固体潮和地下水位时间序列,用最小二乘法求出了水位对不同频率气压和固体潮的敏感程度(即响应系数),依此消除气压和固体潮对观测水位的影响;最后通过极差和标准差评判改正效果。结果表明:旱作所H5观测孔水位受气压影响较大,气压系数为0.799~0.881cm/HPa;对固体潮的响应较小,固体潮系数均接近于0cm/μGal;响应系数的频率效应不明显;与采用一般线性回归方法和快速Mallat离散小波变换算法相比,CWT方法所得气压系数和固体潮系数的地球物理意义更明确;3种计算方法得到的水位改正结果显示,CWT方法与Mallat算法接近,优于一般线性回归方法。 相似文献
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中巴资源卫星（CBERS）多光谱CCD数据与全色HR数据空间分辨率相差较大,给图像融合带来一定困难。利用锐化、高通滤波（HPF）、Brovey变换、IHS变换、主成分变换、GS变换和小波变换等方法对CCD与HR数据进行融合试验。通过定性与定量分析,探讨了不同方法的融合效果。 相似文献
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针对高光谱遥感图像波段多,各波段受云层影响程度不一致,云层本身不均匀等特点,提出了一种频域自适应同态滤波方法.该方法首先检测某个图像云层的范围和对应厚度,根据云层处于低频特点,利用变差函数确定滤波窗口,把云层按照空间邻域大小转换为傅立叶空间,根据云层厚薄调整高通滤波截止频率,然后反变换到空间域.该方法可解决频域空间同态滤波方法破坏无云区的信息,克服空间域滤波方法效果不佳,难以控制和解释的缺陷.结果表明,该方法在保持无云区信息不破坏前提下,较好地改善了不同厚薄云层的质量,计算机内存开销很小,对大数据处理速度较快. 相似文献
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在地震记录中,随机噪声严重影响了有效信号的提取,为此必须进行消噪处理。这里首先使用小波包变换对不同频段的信号进行精细分离,有效信号和噪声经小波包分解后,其小波包系数将表现出不同特性,然后根据这种不同特性进行去噪处理,对小波包分析法处理后的剩余地震信号再进行KL(Karhunen-Loeve)变换,提取相关有效信号,最后对提取的有效信号进行中值滤波处理,进一步去除剩余噪声。经合成地震剖面和实际地震剖面处理实验证明,小波包分析、KL变换和中值滤波联合去噪方法,能有效地消除较强的随机噪声,提高地震剖面信噪比和分辨率。 相似文献
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In this paper, we propose an iterative algorithm for removing the effect of thin cloud cover from LANDSAT imagery. It is seen that the noise in such images is multiplicative as well as additive. The recorded image is first processed to update different parameters of the image formation model to known values. Processing algorithm and knowledge of parameter values are developed by considering the physics of the situation. A low-pass filter is then applied to the processed image to remove the effect of the cloud. The filter is of a tapered shape, and its parameters are adjusted to minimize the estimation error. 相似文献
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The experimental variogram computed in the usual way by the method of moments and the Haar wavelet transform are similar in that they filter data and yield informative summaries that may be interpreted. The variogram filters out constant values; wavelets can filter variation at several spatial scales and thereby provide a richer repertoire for analysis and demand no assumptions other than that of finite variance. This paper compares the two functions, identifying that part of the Haar wavelet transform that gives it its advantages. It goes on to show that the generalized variogram of order k=1, 2, and 3 filters linear, quadratic, and cubic polynomials from the data, respectively, which correspond with more complex wavelets in Daubechies's family. The additional filter coefficients of the latter can reveal features of the data that are not evident in its usual form. Three examples in which data recorded at regular intervals on transects are analyzed illustrate the extended form of the variogram. The apparent periodicity of gilgais in Australia seems to be accentuated as filter coefficients are added, but otherwise the analysis provides no new insight. Analysis of hyerpsectral data with a strong linear trend showed that the wavelet-based variograms filtered it out. Adding filter coefficients in the analysis of the topsoil across the Jurassic scarplands of England changed the upper bound of the variogram; it then resembled the within-class variogram computed by the method of moments. To elucidate these results, we simulated several series of data to represent a random process with values fluctuating about a mean, data with long-range linear trend, data with local trend, and data with stepped transitions. The results suggest that the wavelet variogram can filter out the effects of long-range trend, but not local trend, and of transitions from one class to another, as across boundaries. 相似文献
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地震勘探的有效信号常受到随机噪声的干扰而难以识别,需要进行随机噪声和有效信号的分离。传统Shearlet全局阈值不随方向与尺度变化,在去噪的同时也会损失许多有效信号。Shearlet变换作为一种新的多尺度多方向时频分析方法,具有最优的稀疏表示能力、局部化特征和方向敏感性。本文将含噪地震信号通过Shearlet分解后计算各尺度与方向上Shearlet域系数的L2范数,并对其进行数据重排后发现,随着方向改变L2范数不断减小,进而提出一种基于L2范数的尺度方向自适应阈值计算方法。将其与小波变换、曲波变换、Shearlet全局阈值去噪方法对比,模拟数据与实际地震记录去噪结果表明,本文方法在去除随机噪声的同时,深部弱信号也得到了很好的恢复,地震数据的信噪比比其他3种方法有所提高,在0.929 9 dB条件下提升至11.565 1 dB。 相似文献
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Factorial Kriging (FK) is a data- dependent spatial filtering method that can be used to remove both independent and correlated
noise on geological images as well as to enhance lineaments for subsequent geological interpretation. The spatial variability
of signal, noise, and lineaments, characterized by a variogram model, have been used explicitly in calculating FK filter coefficients
that are equivalent to the kriging weighting coefficients. This is in contrast to the conventional spatial filtering method
by predefined, data-independent filters, such as Gaussian and Sobel filters. The geostatistically optimal FK filter coefficients,
however, do not guarantee an optimal filtering effect, if filter geometry (size and shape) are not properly selected. The
selection of filter geometry has been investigated by examining the sensitivity of the FK filter coefficients to changes in
filter size as well as variogram characteristics, such as nugget effect, type, range of influence, and anisotropy. The efficiency
of data-dependent FK filtering relative to data-independent spatial filters has been evaluated through simulated stochastic
images by two examples. In the first example, both FK and data-independent filters are used to remove white noise in simulated
images. FK filtering results in a less blurring effect than the data-independent fillers, even for a filter size as large
as 9 × 9. In the second example, FK and data-independent filters are compared relative to the extraction of lineaments and
components showing anisotropic variability. It was determined that square windows of the filter mask are effective only for
removing Isotropie components or white noise. A nonsquare windows must be used if anisotropic components are to be filtered
out. FK filtering for lineament enhancement is shown to be resistant to image noise, whereas data-independent filters are
sensitive to the presence of noise. We also have applied the FK filtering to the GLORIA side-scan sonar image from the Gulf
of Mexico, illustrating that FK is superior to the data-independent filters in removing noise and enhancing lineaments. The
case study also demonstrate that variogram analysis and FK filtering can be used for large images if a spectral analysis and
optimal filter design in the frequency domain is prohibitive because of a large memory requirement. 相似文献
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In this paper, we propose a technique of random noise attenuation from seismic data using discrete and continuous wavelet transforms. Firstly, the discrete wavelet transform (DWT) is applied to denoise seismic data using the threshold method. After, we calculate the continuous wavelet transform of the denoised seismic seismogram, the final denoised seismic seismogram is the continuous wavelet transform coefficients at the lower scale. Application to a synthetic seismogram shows the robustness of the proposed tool for random noise attenuation. Application to real vertical seismic profile recorded in Algeria clearly shows the efficiency of the proposed tool for random noise attenuation. 相似文献
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This paper describes how the continuous wavelet transform is used to filter multiple waveforms in both time and frequency domains. It is well suited to process the stationary signals, and it shows the signal in both time and frequency scales. This new approach was tested first on synthetic data and then on real data. The results obtained on both cases were good. The method consists of identifying the multiples on which we apply a normal move out using the multiple velocity law. The multiples will be aligned and the primary reflections will not be aligned. This operation allows locating the multiples in the time-scale domain. We compute the continuous wavelet transform (CWT for short) in order to focus on the patterns relative to seismic events. To filter the multiples, we define a zone with frequency and time bounds. These bounds are deduced from the projection of the seismic trace. Then an automatic mask is applied to the pattern to be isolated. Filtering in time–frequency domain is done by keeping only the wavelet coefficients that are outside the mask and assigning zero to the coefficients larger than a threshold amplitude inside the defined zone. The mask shape does not matter, which is not the case in classical filtering, where both the window size and shape play a key role. The mask is defined from three parameters: time, frequency, and the wavelet coefficients. To go back to the time domain, one has to compute the wavelet transform inverse of the trace. This procedure is repeated for all traces. To reset the traces to their initial positions, we apply the dynamic correction inverse with the same velocity law as the multiples. It turns out that the attenuation of multiples by the CWT works fine, in particular, the two identified multiples were quasi eliminated (Fig. 10). 相似文献
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Ground penetrating radar (GPR) is widely used for non-invasive examination of man-made structures, especially to determine the depth of pipes buried underground. Unfortunately, shallower objects may obscure GPR raw data that is reflected from deeper ones. This study introduces a signal processing technique, called the discrete wavelet transform (DWT), to filter and enhance the GPR raw data in order to obtain higher quality profile images. Laboratory experiments were conducted and the locations of buried pipes under different conditions were analyzed. The buried pipes were made of plastic and metal, and both single and two parallel horizontal pipes are discussed. The experimental results indicate that the DWT profiles can provide more information than the traditional GPR profile. The images of the diameter and position of pipes, even two pipes of different materials and in horizontal alignment, can be enhanced by using the DWT profile. 相似文献
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Two-dimensional Conditional Simulations Based on the Wavelet Decomposition of Training Images 总被引：2，自引：1，他引：1
Scale dependency is a critical topic when modeling spatial phenomena of complex geological patterns that interact at different
spatial scales. A two-dimensional conditional simulation based on wavelet decomposition is proposed for simulating geological
patterns at different scales. The method utilizes the wavelet transform of a training image to decompose it into wavelet coefficients
at different scales, and then quantifies their spatial dependence. Joint simulation of the wavelet coefficients is used together
with available hard and or soft conditioning data. The conditionally co-simulated wavelet coefficients are back-transformed
generating a realization of the attribute under study. Realizations generated using the proposed method reproduce the conditioning
data, the wavelet coefficients and their spatial dependence. Two examples using geological images as training images elucidate
the different aspects of the method, including hard and soft conditioning, the ability to reproduce some non-linear features
and scale dependencies of the training images. 相似文献