共查询到18条相似文献,搜索用时 62 毫秒
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结点阈值小波包变换图像去噪新算法 总被引:2,自引:0,他引:2
小波包变换是小波变换的推广,可视为普通小波函数的线性组合,具有灵活的时频分析能力,随着分解层数的增加,小波包分解能够在所有的频率范围聚焦。提出一种应用结点阈值小波包变换的新型图像去噪算法。利用小波包变换对含噪图像进行分解,在图像信号的子带层次上进行结点阈值操作,采用软阈值的方法进行阈值处理,结点噪声采用谱熵法估计,并使用峰值信噪比评估去噪后的图像质量。实验结果表明,相比于使用其它阈值方法的小波包图像去噪算法,该算法具有更好的图像去噪性能。 相似文献
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水声目标信号在发送、传播过程中,易受到环境噪声、系统自噪声等影响,因此水声监听过程中目标信号会掺杂大量噪声信息。为提高获取目标信号的准确性和可靠性,降低噪声,在已有小波分析基础上,提出小波包节点相对能量判断最优分解层,最优分解层节点系数分段阈值处理重构方法,实现水声监听信号分频段去噪。将0.1 kHz~8.4 k Hz实验数据按节点频率排序划分为5个强弱不同的频段信号实现消噪提取,结果表明该方法可将噪声信号与目标信号有效分离,与全局单一阈值相比,具有较好降噪能力。该方法打破了小波阈值去噪高频处理的局限性,提高了识别精度,改善了全局单一阈值去噪存在的短板,在鱼类分析识别、舰船监听、深海探测等方面具有一定的推广和应用价值。 相似文献
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针对光电探测器传统降噪处理中软、硬阈值函数存在的缺点,提出了一种含参数的阈值函数和逐层变化的阈值相结合的小波阈值降噪算法.该算法可以调整参数使生成的阈值函数于软、硬阈值函数之间,且在临界阈值处平滑过渡,保留部分有用信号.应用过程中阈值可随着分解层数的改变而改变,对各个分解层有自适应特征,减少小波系数阈值处理中的固定偏差,从而在保留原有信号的同时减除不必要噪声.仿真及实测结果表明,采用该小波阈值降噪算法处理的信号信噪比较高、均方误差较小,有效地抑制噪声对光电探测器输出信号的干扰. 相似文献
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使用光谱仪采集到的信号难免受到不同噪声源的影响。为了提高光谱信号解析的精准度,通过分析小波应用于信号降噪的原理以及经典的软、硬阈值降噪法存在的缺陷,提出了一种改进的阈值降噪法。该方法既克服了硬阈值法产生间断点,软阈值法产生恒定偏差的缺陷,又尽量地保留了有用信号。实验选用的小波基函数为SymletsA,分解层数为4,结合Birge Massart策略模型确定的分层阈值对硒化镉量子点荧光光谱信号进行降噪处理。结果表明,与经典的软、硬阈值降噪法相比,通过改进阈值降噪法得到重构信号的信噪比(SNR=47.550 2)、能量占比(PER=0.973 3)和均方误差(MSE=149.421 3)均有提高和改善。 相似文献
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小波阈值降噪模型在红外光谱信号处理中的应用研究 总被引:2,自引:1,他引:1
针对近红外光谱经常受到噪声干扰的特点,提出了利用小波阈值降噪方法进行光谱数据的降噪处理,以山羊绒表面油脂的近红外光谱检测为例,对比分析了三种小波阈值降噪模型(Penalty阈值降噪模型、Brige-Massart阈值降噪模型、缺省阈值降噪模型)的降噪性能。对降噪后的光谱数据采用偏最小二乘和支持向量机回归相结合建立了校正和预测模型,通过对比校验参数R2,RMSEC,RMSEP,分析评价了三种小波阈值降噪模型的降噪效果。结果表明: 三种降噪模型都能在一定程度上降低光谱信号的噪声,提高信噪比,改善光谱预测模型的精度,其中,Brige-Massart阈值降噪模型和缺省阈值降噪模型的降噪效果明显优于Penalty阈值降噪模型,与原始光谱信号建模的预测精度(R2=0.793,RMSEC=0.233,RMSEP=0.225)相比较,经过Brige-Massart阈值降噪模型降噪后的光谱信号建模的预测精度(R2=0.882,RMSEC=0.144,RMSEP=0.136)和经过缺省阈值降噪模型降噪后的光谱信号建模的预测精度(R2=0.876,RMSEC=0.151,RMSEP=0.142)均有较大程度的改善和提高,说明提出的小波阈值降噪方法能有效地降低原始光谱噪声作用,使光谱数据多变量分析模型更具有代表性和稳健性, 从而可以提高模型的预测精度。 相似文献
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基于小波阈值理论的光学图像去云处理新算法 总被引:3,自引:0,他引:3
分析了部分云覆盖的单幅光学遥感图像经过多层小波变换后,景物信息和云噪音在小波分解系数中的分布关系,并提出了云区阈值法来有效去除云噪音.通过选择适当的分界层数,将景物信息、云噪音尽可能分别分配到低层和高层细节系数中.高层细节系数中除主要包含云噪音外,也有部分有用景物信息.由于云噪音亮度大于景物,根据亮度特征合理选择高层细节系数的亮度阈值,去除云噪音,而保留其中的景物信息.通过对低层、高层细节系数和近似系数分别设置权重,增加景物对比度,减小残留云影响,从而重构得到恢复图像.提出了以信息熵作为分界层数、权重、阈值等参量选择的定量标准.实验证明,按信息熵标准能正确地选择参量,依据本文算法得到的去云效果远优越于同态滤波和Retinex算法,且能充分保留云区以外景物信息. 相似文献
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Siren noises usually severely disturb the intelligibility of voice communication inside the cabs of police, paramedic and fire vehicles. It is often desired that such unwanted noise can be removed from the speech signal. In this paper, a new method is proposed to adaptively cancel siren noises and enhance speech signals. Based on the characteristics of siren noises, an anti-speech filter and a time delayer are employed in the single and dual channel noise cancellation systems to reduce the siren noises. Experiment results demonstrate that the effectiveness of the proposed method for canceling the siren noises and the performance of the enhanced speech signal is satisfying. 相似文献
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A new regularized forward blind source separation algorithm for automatic speech quality enhancement
This paper addresses the problem of speech enhancement and acoustic noise reduction by adaptive filtering algorithms in a moving car through blind source separation (BSS) structures. In this paper we propose a new regularized forward blind source separation (RFBSS) algorithm that does not need voice activity detection (VAD) systems, and allows getting efficient speech enhancement performances with low complexity. 相似文献
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In this paper, an ant colony combined with adaptive threshold denoising algorithm for THz image is proposed. The ant colony is introduced to tackle the image edge detection problem; it is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. The adaptive threshold is derived in a Bayesian framework; it is adaptive to each subband because it depends on data-driven estimates of the parameters, and it is used to handle edge image and non-edge image which are obtained from ant colony. Experimental results are provided to show that the proposed approach outperforms several THz image denoising approaches developed in the traditional and literature. 相似文献
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This paper addresses the problem of speech intelligibility enhancement by adaptive filtering algorithms employed with subband techniques. The two structures named the forward and backward blind source separation structures are extensively used in the speech enhancement and source separation areas, and largely studied in the literature with convolutive and non-convolutive mixtures. These two structures use two-microphones to generate the convolutive/non-convolutive mixing signal, and provide at the outputs the target and the jammer signal components. In this paper, we focus our interest on the backward structure employed to enhance the speech signal from a convolutive mixture. Furthermore, we propose a subband implementation of this structure to improve its behavior with speech signal. The new proposed subband-Backward BSS (SBBSS) structure allows a very important improvement of the convergence speed of the adaptive filtering algorithms when the subband-number is selected high. In order to improve the robustness of the proposed subband structure, we have adapted then applied a new criterion that combines the System Mismatch and the Mean-Errors criterion minimization. The proposed subband backward structure, when it is combined with this new criterion minimization, allows to enhance the output speech signal by reducing the distortion and the noise components. The performance of the proposed subband backward structure is validated through several objective criteria which are given and described in this paper. 相似文献
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In this paper, a single-channel speech enhancement algorithm based on non-linear and multi-band Adaptive Gain Control (AGC) is proposed. The algorithm requires neither Signal-to-Noise Ratio (SNR) nor noise parameters estimation. It reduces the background noise in the temporal domain rather than the spectral domain using a non-linear and automatically adjustable gain function for multi-band AGC. The gain function varies in time and is deduced from the temporal envelope of each frequency band to highly compress the frequency regions where noise is present and lightly compress the frequency regions where speech is present. Objective evaluation using the PESQ (Perceptual Evaluation of Speech Quality) metric shows that the proposed algorithm performs better than three benchmarks, namely: the spectral subtraction, the Wiener filter based on a priori SNR estimation and a band-pass modulation filtering algorithm. In addition, blind subjective tests show that the proposed algorithm introduces less musical noise compared to the benchmark algorithms and was preferred 78.8% of the time in terms of signal quality. The proposed algorithm is implemented in a miniature low power digital signal processor to validate its feasibility and complexity for smart hearing protection in noisy environments. 相似文献
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小目标识别的小波阈值去噪方法 总被引:4,自引:0,他引:4
为改善小目标识别的滤噪效果并提高其信噪比,构造了新的阈值函数并采用局部方差估计法来计算阈值对小目标进行去噪处理。对小波分解层次中各高频子带选取不同的阈值,其中大于阈值的小波系数采用改进的双曲线函数作为阈值函数,小于阈值的小波系数采用指数函数与对数函数相互组合的方式作为阈值函数。对采用的阈值函数进行了理论推导,并与软、硬阈值法进行了实验对比。计算机仿真结果表明:经本文阈值法处理后,信噪比相对于含噪图像提高了70.8%,而软、硬阈值法分别提高了49.8%和59.7%。光学实验进一步证实:该方法能更有效地提高信噪比,增强联合变换相关器对于小目标的识别能力。 相似文献