遗传算法优化小波阈值对水质光谱数据的去噪研究

针对紫外-可见光谱法水质检测系统易受到仪器本身和外界环境的噪声干扰, 所测得的光谱数据存在大量系 统和杂散光噪声的问题, 在对紫外-可见光谱法水质检测系统的噪声源分析的基础上提出将遗传算法应用于小波阈值 优化的去噪方法, 并与小波软阈值、 SG 平滑和中值滤波方法进行了对比。为评价去噪效果, 对同一浓度的邻苯二甲 酸氢钾标液的紫外-可见光谱数据进行去噪实验。在采用遗传算法选取小波最优阈值对标液进行去噪处理的同时, 还 采用传统小波软阈值去噪、 SG 平滑和中值滤波去噪作为对比。为验证该算法的实际可行性, 进一步用这四种方法对 某地排水沟和某污水处理厂排水口的实际水样光谱进行去噪处理。实验结果表明: 基于遗传算法的小波阈值去噪效 果良好, 相较于传统的小波软阈值去噪、 SG 平滑和中值滤波的方法, 信噪比分别提高了 2.2994、 5.7066、 2.6155 dB, 均方根误差分别减小了 0.0028、 0.0087、 0.0033, 峰值信噪比分别提高了 2.0837、 5.2569、 2.7375 dB。基于遗传算法 的小波阈值去噪算法不仅抑制了光谱数据中的噪声, 同时也提高了系统精度, 为紫外-可见光谱法水质光谱去噪处理提 供了一种新的解决办法。     

Wavelets for computationally efficient hyperspectral derivativeanalysis

Smoothing followed by a derivative operation is often used in the analysis of hyperspectral signatures. The width of the smoothing and/or derivative operator can greatly affect the utility of the method. If one is unsure of the appropriate width or would like to conduct analysis for several widths, scale-space images can be used. This paper shows how the wavelet transform modulus-maxima method can be used to formalize and generalize the smoothing followed by derivative analysis and how the wavelet transform ran be used to greatly decrease computational costs of the analysis. The Mallat/Zhong wavelet algorithm is compared to the traditional method, convolution with Gaussian derivative filters, for computing scale-space images. Both methods are compared on two points: (1) computational expense and (2) resulting scalar decompositions. The results show that the wavelet algorithm can greatly reduce the computational expense while practically no differences exist in the subsequent scaler decompositions. The analysis is conducted on a database of hyperspectral signatures, namely, hyperspectral digital image collection experiment (HYDICE) signatures. The reduction in computational expense is by a factor of about 30, and the average Euclidean distance between resulting scale-space images is on the order of 0.02     

基于小波变换模极大值重构原理的HRTFs平滑逼近预处理

空间听觉的研究以及虚拟听觉空间的实现中,与头相关传递函数(Head-Related Transfer Functions, HRTFs)或与头相关冲激响应(Head-Related Impulse Responses, HRIRs)的高效建模对于隐含在HRTFs中的特征模式的识别有着极其重要的作用。作为建模前的一个重要环节,该文通过对HRTFs的时域奇异性特征分析和全部测量空间方位上HRIRs分布特点的统计判断,采用具有平移不变特性的多孔小波变换和相应的模极大值重构原理提出了一种HRTFs非线性平滑逼近预处理的方法。仿真实验结果表明,在设置的阈值门限一致的情况下,该文方法较PCA(Principal Component Analysis)和基于小波变换Mallat算法的逼近处理的性能分别提高了8.3dB和2.4dB。     

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。     

采用频域紧支集正交小波基消除瞬态散射回波中的高斯白噪声干扰

本文提出一种采用频域紧支集正交小波基的非线性柔性阈值波方法,该方法利用频域紧集小波基的良好频谱特性和信号压缩能力,有效地消除了高斯白噪声对瞬态散射回波的干扰。     

预测混合交叉有效平滑法去噪声

本文研究了左预测和右预测及交叉有效（ＣＶ）平滑法，并证明了ＣＶ平滑中的两个限制条件。提出了预测混合ＣＶ平滑法去噪，能同时达到以上目的。分析了该方法的统计特性。并通过模拟试验了该方法，并与有限混合中值滤波（ＦＭＨ）法和自适应小波收缩法（ＷＳＡ）作了比较，试验结果说明了该方法是切实可行的，并优于ＦＭＨ和ＷＳＡ。     

小波变换在图像边缘检测中的应用

目前,被广泛使用的经典边缘检测算子有Sobel算子,Prewitt算子,Roberts算子,Log算子,Canny算子等等。这些算子的核心思想是图像的边缘点是相对应于图像灰度值梯度的局部极大值点。然而,当图像中含有噪声时这些算子对噪声都比较敏感,使得将噪声作为边缘点。由于噪声的干扰,不能检测出真正的边缘。一个拥有良好属性的的边缘检测算法是每个研究者的追求。利用小波交换的特点,设计了三次B样条平滑滤波算子。通过利用这个算子,对利用小波变换来检测图像的边缘进行了一定的研究和理解。     

一种新颖的消噪算法及其在通信中的应用

本文提出了一种自适应变窗长平滑滤波的消噪算法,它依据信号在小波变换域和时域的幅度分布特性,把信号分成特性不同的时间片段,然后按片段信号的特性采用自适应变窗长平滑滤波的方法,对经过内插处理后的高采样率信号进行消噪处理.这种方法非常适合分段连续或分段平稳信号的消噪处理,仿真结果表明该算法具有良好的性能.本文将该算法应用于扩频通信系统中脉冲干扰的消除,仿真结果说明这种算法与脉冲置零法相比,性能得到了较大的改善.     

Computational methods for hidden Markov tree models-an application to wavelet trees

The hidden Markov tree models were introduced by Crouse et al. in 1998 for modeling nonindependent, non-Gaussian wavelet transform coefficients. In their paper, they developed the equivalent of the forward-backward algorithm for hidden Markov tree models and called it the "upward-downward algorithm". This algorithm is subject to the same numerical limitations as the forward-backward algorithm for hidden Markov chains (HMCs). In this paper, adapting the ideas of Devijver from 1985, we propose a new "upward-downward" algorithm, which is a true smoothing algorithm and is immune to numerical underflow. Furthermore, we propose a Viterbi-like algorithm for global restoration of the hidden state tree. The contribution of those algorithms as diagnosis tools is illustrated through the modeling of statistical dependencies between wavelet coefficients with a special emphasis on local regularity changes.     

信号在多尺度空间的滤波

本文从多分辨率分析出发,结合离散正交小波变换（ＤＷＴ）的理论,提出了一种在保护信号边缘的前提下有效去除脉冲噪声的方法,该方法通过对各尺度下的未抽取前的小波系数进行非线性滤波处理,达到了在保护信号边缘的同时,有效去除白噪声及脉冲噪声的目的。并用实验结果进行了验证。     

Data analytic wavelet threshold selection in 2-D signal denoising

A data adaptive scheme for wavelet shrinkage-based noise removal is developed. The method involves a statistical test of hypotheses that takes into account the wavelet coefficients' magnitudes and relative positions. The amount of smoothing performed during noise removal is controlled by the user-supplied confidence level of the tests     

基于混沌加密的dwt域数字水印算法

提出了一种将二维混沌系统应用到水印图像预处理中,对水印图像进行混沌加密。然后对载体图像进行三级小波分解,选择在水平和垂直子带中根据分块能量的大小,选择能量块较大的块系数嵌入水印,该算法在水印提取过程中需要原始载体图像。实验结果表明:该算法嵌入的数字水印具有很好的隐蔽性,同时对叠加噪声、平滑滤波等攻击具有很好的鲁棒性。     

基于高阶偏微分方程的非线性去噪算法

提出了一种将小波分析与高阶偏微分方程相结合的非线性方法。这种方法利用小波分析的时频局部性和高阶偏微分方程的平缓图像轮廓的特性对图像进行处理,不仅很好地抑制了噪声,而且使图像保留了尽可能多的细节,看起来更平滑自然。实验结果表明了该方法的有效性。     

Wavelet based watermarking method for digital images using thehuman visual system

A wavelet based multiresolution watermarking method using the human visual system (HVS) is proposed, with the number of watermarks embedded proportional to the energy contained in each band. Experiments show that the proposed three-level wavelet based watermarking method is robust to some attacks such as, for example, joint photographic experts group (JPEG) compression, smoothing, cropping and collusion     

会议电视系统比特率控制方法的研究

会议电视系统中核心部分是比特率的控制方法，它对编码的图象质量产生直接影响。文中从缓冲器平滑波动数码流的原理入手，分析了缓冲器纯占有率控制算法及参数设置，并针对缓冲器纯占有率控制算法的编码图象质量不够理想的机理，研究了缓冲器占有率的视觉调整算法和基于比特分配的缓冲容量控制算法，并对三种算法进行了比较和论证。     

Spatially adaptive wavelet-based multiscale image restoration

In this paper, we present a new spatially adaptive approach to the restoration of noisy blurred images, which is particularly effective at producing sharp deconvolution while suppressing the noise in the flat regions of an image. This is accomplished through a multiscale Kalman smoothing filter applied to a prefiltered observed image in the discrete, separable, 2-D wavelet domain. The prefiltering step involves constrained least-squares filtering based on optimal choices for the regularization parameter. This leads to a reduction in the support of the required state vectors of the multiscale restoration filter in the wavelet domain and improvement in the computational efficiency of the multiscale filter. The proposed method has the benefit that the majority of the regularization, or noise suppression, of the restoration is accomplished by the efficient multiscale filtering of wavelet detail coefficients ordered on quadtrees. Not only does this lead to potential parallel implementation schemes, but it permits adaptivity to the local edge information in the image. In particular, this method changes filter parameters depending on scale, local signal-to-noise ratio (SNR), and orientation. Because the wavelet detail coefficients are a manifestation of the multiscale edge information in an image, this algorithm may be viewed as an "edge-adaptive" multiscale restoration approach.     

Spatially adaptive multiplicative noise image denoising technique

A new image denoising technique in the wavelet transform domain for multiplicative noise is presented. Unlike most existing techniques, this approach does not require prior modeling of either the image or the noise statistics. It uses the variance of the detail wavelet coefficients to decide whether to smooth or to preserve these coefficients. The approach takes advantage of wavelet transform property in generating three detail subimages each providing specific information with certain feature directivity. This allows the ability to combine information provided by different detail subimages to direct the filtering operation. The algorithm uses the hypothesis test based on the F-distribution to decide whether detail wavelet coefficients are due to image related features or they are due to noise. The effectiveness of the proposed technique is tested for orthogonal as well as biorthogonal mother wavelets in order to study the effect of the smoothing process under different wavelet types.     

双模噪声背景下自适应小波阈值去噪

在分析双模噪声模型统计特性的基础上提出自适应小波阈值算法。新算法中设计改进的阈值函数和控制函数,克服了传统硬、软阈值法的不足,并且自适应得到最佳控制因子。该算法对加入双模噪声的信号进行闭环反馈处理：小波分解、阈值量化处理、小波逆变换重构信号、控制函数寻优。Matlab 2012 a仿真结果表明,该算法相对于传统硬、软阈值法,去噪图形曲线清晰、光滑、连续性好,信噪比分别提高9 dB和4 dB。在双模噪声背景下,自适应小波阈值去噪有效、可行,拓展了小波阈值算法的应用。     

基于TwIST-TV 约束的图像去模糊方法

传统的基于频域和小波域的去模糊算法所得的复原图像总是存在比较明显的边缘振铃及模糊效应,而较为有效的空域迭代优化去模糊算法速度通常比较慢。为了解决上述问题,提出了基于二步迭代阈值收缩（TwIST）与总变分（TV）约束相结合的图像去模糊算法（TwIST-TV）。首先在去模糊目标函数中加入对图像的TV 正则化约束,其次在对图像小波系数的每次二步迭代之前,加入对图像的TV 优化去噪约束,最后迭代获取去模糊图像。实验结果表明:相对于基于频域和小波域的模糊图像恢复算法,TwIST-TV 能有效抑制边缘模糊和振铃效应,复原图像的信噪比（SNR）、峰值信噪比（PSNR）高出1~7 dB,平均结构相似度指标（MSSIM）可高出0.05,相对于空域解卷积算法在保证求解精度相当的情况下具备6 倍以上的速度优势。     

基于SWT自适应模糊萎缩的SAR图像降斑算法

提出了基于小波域高斯混合模型贝叶斯估计模糊萎缩的SAR图像降斑算法.该算法分析了SAR图像在平稳小波变换(SWT)域中的统计模型,并用高斯混合模型对其进行描述,推导出基于贝叶斯估计的信号最小均方误差(MMSE)的模糊萎缩因子.籍此再根据小波域相邻尺度间小波系数的相关性,采用分区域模糊萎缩思想,很好地得到无斑点真实信号小波系数的估计.仿真结果表明该算法在大大抑制斑点噪声的同时,有效的保持了边缘,其性能优于改进Lee滤波,小波软阈值和SWT萎缩降斑算法.