基于复滤波器组的红外弱小目标检测算法

 为了有效抑制复杂背景的干扰,降低复杂背景所带来的虚警,提高目标检测的信噪比,提出了一种基于复滤波器组的红外弱小目标检测算法。分析了复杂背景下带有弱小目标的红外图像中复杂背景和弱小目标图像各自的频谱特性,并引入了分频段处理的思想。比较了各种滤波器的性能,并选用了基于复小波的滤波器组,用该滤波器组将红外弱小目标图像分解到各个子频域;对分解后的各频段图像分别进行基于罗宾逊滤波的目标检测处理,提取各频段图像中的奇异点;根据目标图像和背景图像的频谱特性的定量分析结果,选取合适的权值,将各频段检测的结果进行加权融合,得到最终的处理效果。实验结果表明:弱小目标检测方法较之于传统的不分频段的高通滤波处理方式可以获得更高的信噪比,目标得到明显的增强,背景杂波得到更有效的抑制,各项探测指标均更优。     

一种快速合成多虚拟声源的头相关传输函数模型

提出了一种用于实时快速合成多个虚拟声源的头相关传输函数(HRTF)模型。首先对水平面的头相关脉冲响应(HRIR,头相关传输函数的时域形式)数据进行两层小波包分解,然后用一组子带滤波器和综合滤波器建立模型。子带滤波器的系数由HRIR小波系数的零插值得到,综合滤波器的系数由小波函数计算得到。通过使用阈值法对小波系数进行压缩,即可达到简化模型、减小运算量的目的。计算表明,只需要使用30点的小波系数建模,可使模型的重构误差控制在1%的量级。而心理声学实验表明,使用35点的小波系数,模型可得到和原始的HRTF滤波器相当的听觉效果。在同时合成多个虚拟声源的实时计算中,模型的运算量明显小于普通的HRTF滤波器。      

软门限去噪法在X射线能谱分析中的应用

从函数空间的剖分和理想滤波器组的角度分析了多分辨率信号的分解与重构，提出了一种X射线能谱去噪的方法，即应用Mallat算法对谱信号进行小波分解，然后应用一个非线性软门限函数在小波域内将噪声抑制和消除，最后重构得到去噪后的能谱。该方法明显改善了能谱定量分析的结果。     

小波能量积累器在信号处理中的应用

 根据小波变换的多尺度分解特性,提出了多尺度小波能量积累器的概念,并给出了相应的多尺度小波能量积累算法,将信号在不同分解尺度上的小波能量进行积累。在此基础上设计出一种适用于各种雷达,尤其是微波雷达弱信号处理的小波能量检测算法,对低信噪比信号检测进行了仿真实验。结果表明,该检测算法对强白噪声及强色噪声背景下的低信噪比信号的检测非常有效。     

具有紧支撑正交非张量积小波的图像融合

提出了基于一种新的小波——具有紧支撑、正交性、伸缩矩阵为[^2 0 ^0 2]的非张量积小波的图像融合方法。首先根据非张量积小波理论,利用Daubechies构造的单变量滤波器构造出基于四通道的不可分的小波滤波器组,用此滤波器组对参加融合的图像进行分解,然后对低频部分采用取均值、高频部分采用系数绝对值取大的融合算法对分解子图进行融合,最后重构。并采用熵、交叉熵、互信息、均方根误差和峰值信噪比等指标对该方法进行了客观评价。对可见光图像与红外图像、远红外图像与近红外图像、遥感图像、多聚焦图像和其它多类图像的融合实验结果证明本方法有较好的融合效果,其融合性能与采用同样融合算法的张量积db2小波的融合方法的融合性能相当。     

双正交小波插值的CCD图像超分辨恢复

给出了一种构造双正交小波滤波器的设计方法,应用该方法得到的双正交小波对低分辨率图像进行小波分解。对分解后的图像结构进行分析,提出双三次插值与方向插值相结合的方法,对分解后图像插值。对插值后图像小波逆变换,得到超分辨率图像。应用于CCD图像,获得了比双线性插值高的峰值信噪比(25.3090dB),图像细节信息增强了。实验结果表明,算法应用于CCD图像不仅提高了图像的空间分辨率,同时也提高了图像的峰值信噪比,还比较好地保留了图像的边缘信息,且经过该方法恢复后的图像更适合人眼观察,细节丰富,更加清晰,畸变小,是一种提高CCD图像空间分辨率的有效方法。     

小目标识别的小波阈值去噪方法

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。     

低信噪比下基于多元变分模态分解的水下蛙人特征提取

为提高低信噪比下对蛙人辐射噪声的提取能力,提出了一种矢量多通道信号的水下蛙人辐射噪声特征提取方法。首先针对矢量多通道信号使用多元变分模态分解,得到固有模态函数。然后选取能量最高的模态函数组作为研究对象,计算该模态函数组互谱后的加权排列熵。最后提取加权排列熵频谱的蛙人特征量。海试结果表明,本方法可在无需先验信息情况下,提取由矢量水听器采集的蛙人信号特征,在检测率为80%的情况下抗噪能力较传统算法提升了10 dB。     

基于改进中值滤波和提升小波变换的阈值去噪方法研究

对于实际拍摄的一些图像信噪比低,噪声密度大,且含有混合噪声,而现有算法大多只能去除单一噪声的问题。针对混合噪声中含有的脉冲噪声和高斯噪声,提出基于改进中值滤波和提升小波变换去噪相结合的方法。去噪过程中,使用中值滤波器提取脉冲噪声并采用中值滤波算法滤波后,构造提升小波,采用改进阈值函数提升小波阈值去噪方法去除高斯噪声。实验结果表明,当噪声值(,)=(0.4, 20)时,采用本文去噪方法,峰值信噪比（PSNR）为34.002 1,平均绝对误差（MAE）为2.365 3。     

利用经验模态分解的高频水声信号滤波方法*

研究了一种高频水声信号的滤波问题,提出了一种改进的经验模态分解加小波阈值滤波方法。首先对信号进行带通滤波处理,再进行经验模态分解,将分解得到的各个模态转换为频域信号,采用小波软阈值方法对这些频域信号进行滤波,最后对信号进行重构,并转换为时域信号。经数值仿真与试验数据验证表明此方法是可行有效的,与原基于经验模态分解的小波阈值滤波方法相比,本方法滤波效果较好:对不同输入信噪比的仿真信号进行滤波后,本方法的输出信噪比最大提高17.41 d B,滤波后所得信号与加噪前纯信号的相关系数最大提高0.90;对实验数据进行滤波后,不同时间段信号的相关系数最大提高0.62。     

贝叶斯优化卷积神经网络公共场所异常声识别*

针对公共场所异常声的感知和识别问题,提出一种基于贝叶斯优化卷积神经网络的识别方法。提取声信号的Gammatone倒谱系数、倍频程功率谱、短时能量和谱质心,组合成声信号的特征图。构建卷积神经网络作为分类器,利用递增的卷积核设置和池化操作处理不同尺度的特征。基于贝叶斯优化算法优化卷积神经网络的模型参数,对包括火苗噼啪声、婴儿啼哭声、烟花燃放声、玻璃破碎声和警报声的5种公共场所异常声进行识别。该方法的识别结果与基于不同的特征提取和分类器方案得到的识别结果进行比较,结果表明该方法的识别效果优于其他特征提取和分类器方案的识别效果。最后分析了该方法在不同信噪比噪声干扰下的识别结果,验证了该方法的有效性。     

Perceptual effects of plosive feature modification

In the 1970-1980's, a number of papers explored the role of the transitional and burst features in consonant-vowel context. These papers left unresolved the relative importance of these two acoustic cues. This research takes advantage of refined signal processing methods, allowing for the visualization and modification of acoustic details. This experiment explores the impact of modifying the strength of the acoustic burst feature on the recognition scores P(c)(SNR) (function of the signal-to-noise ratio), for four plosive sounds /ta, ka, da, ga/. These results show high correlations between the relative burst intensity and the scores P(c)(SNR). Based on this correlation, one must conclude that these bursts are the primary acoustic cues used for the identification of these four consonants. This is in contrast to previous experiments, which used less precise methods to manipulate speech, and observe complex relationships between the scores, bursts and transition cues. In cases where the burst feature is removed entirely, it is shown that naturally existing conflicting acoustic features dominate the score. These observations seem directly inconsistent with transition cues playing a role: if the transition cues were important, they would dominate over low-level conflicting burst cues. These limited results arguably rule out the concept of redundant cues.     

An auditory periphery model for improving narrow-band noise recognition rate of underwater targets

The recognition rate of the auditory periphery features decreases when the model is used to identify underwater targets in practice. To solve this problem, an improved method based on Gammatone filter bank is proposed. Firstly, after the reason of the decreasing of the recognition results is analyzed,the mechanism of multichannel data acquisition in acoustic engineering may narrow down signal frequency range, which leads to time-frequency features distortion. Secondly, the Gammatone filter bank is implemented to simulate frequency decomposition characteristics of human ear basilar membrane. Since the class information of the underwater noise signal is mostly contained in low frequency range, the auditory features of the conventional model are interpolated and the channel number of the filter bank and the central frequency of each frequency band are adjusted accordingly to obtain a 27-dimensional feature vector of the narrow-band target signal. The adjusted model may reflect the target's timefrequency feature more precisely. Finally, the performance of the auditory features is tested by a Neural Network classifier. The experiment results show that the modified auditory model is more effective than the conventional ones. The major information contained in broadband signals is reserved and the classification ability for real targets is further enhanced. The recognition results are increased from 82.59% to 88.80%. The modified auditory features effectively improve the recognition rate for underwater target radiated noise signals.     

氢气拉曼光谱压缩感知方法分析研究

气体监测与我们的生活息息相关,氢气作为一种理想的研究模型更是受到广泛关注。拉曼光谱作为一种气体分析手段,具有无损非接触等优点。气体拉曼光谱测量存在的一个主要问题是拉曼散射信号弱。在一些特定场景下,需要信号采集时间较短,因此获得的拉曼光谱信噪比低。压缩感知方法作为一种新发展起来的信号处理手段,不仅可以压缩采样,缩短采样时间,而且可以降噪,提高信噪比,以更好地实现原始信号的恢复和重建。该研究以氢气和氘气为测量对象,分别采用洛伦兹函数设计原子构建字典OMP（orthogonal matching pursuit）算法重构和傅里叶变换滤波后多个正交基构建正交基字典OMP重构两种压缩感知方法分析氢同位素气体的拉曼光谱。通过对仿真数据和实际测量数据的处理,比对了两种压缩感知分析与小波软阈值、小波硬阈值和SG（Sawitzky-Golay）滤波处理的谱峰强度效果以及信噪比和均方根误差,证明洛伦兹函数设计原子构建字典OMP算法重构可以用于氢气拉曼光谱降噪。     

光学小波包变换及其滤波器的研究

基于对光学小波变换必要条件的分析，提出光学小波包变换的概念.选出虹膜图库的联合最优小波包基，利用最优基的线性组合生成相应的复合光学小波包滤波器.将滤波器用于光电混合虹膜识别系统中对待识别输入进行小波包特征提取预处理，模拟结果不仅证明引入该滤波器可明显提升系统的识别效果，也证明了光学小波包变换提出的意义.     

小波包自适应阈值语音降噪新算法

为了克服低信噪比输入下,语音增强造成语音清音中的弱分量损失,造成重构信号包络失真的问题。论文提出了一种新的语音增强方法。该方法根据语音感知模型,采用不完全小波包分解拟合语音临界频带,并对语音按子带能量进行清浊音区分处理,在阈值计算上,提出了一种清浊音分离,基于子带信号能量的小波包自适应阈值算法。通过仿真实验,客观评测和听音测试表明,该算法在低信噪比输入时较传统算法,能够更加有效地减少重构信号包络失真,在不损伤语音清晰度和自然度的前提下,使输出信噪比明显提高。将该算法与能量谱减法结合,进行二次增强能进一步提高降噪输出的语音质量。     

Extended fractional wavelet joint transform correlator

An extended fractional wavelet joint transform correlator is implemented for real-time target recognition applications. The real-time input scene captured using a charge-coupled device camera along with the reference image is fractional Fourier transformed. The obtained joint power spectrum is multiplied by an appropriately scaled wavelet filter and the resultant function is differentiated. The application of wavelet filter enhances the correlation outputs and differential processing of wavelet-filtered joint power spectrum improves the detection efficiency by reducing the zero-order spectra. Targets with Gaussian and speckle noise have also been used to check the correlation output. The performance metrics: correlation peak intensity, peak-to-correlation energy, peak-to-sidelobe ratio and signal to clutter ratio have been calculated. The experimental results are presented in support of the proposed idea.     

光学小波匹配滤波器与图形识别

小波变换弥补了傅里叶变换的不足，在信号及图像处理方面具有广阔的应用前景。文章深入讨论了小波变换匹配滤波器的理论基础及光学实现方法，并将其用于图形识别，给出了计算机模拟结果，与传统的匹配滤波器进行了比较，证实了小波变换在光学信息处理中的优越性。     

Evaluation of an auditory model for echo delay accuracy in wideband biosonar

In a psychophysical task with echoes that jitter in delay, big brown bats can detect changes as small as 10-20 ns at an echo signal-to-noise ratio of approximately 49 dB and 40 ns at approximately 36 dB. This performance is possible to achieve with ideal coherent processing of the wideband echoes, but it is widely assumed that the bat's peripheral auditory system is incapable of encoding signal waveforms to represent delay with the requisite precision or phase at ultrasonic frequencies. This assumption was examined by modeling inner-ear transduction with a bank of parallel bandpass filters followed by low-pass smoothing. Several versions of the filterbank model were tested to learn how the smoothing filters, which are the most critical parameter for controlling the coherence of the representation, affect replication of the bat's performance. When tested at a signal-to-noise ratio of 36 dB, the model achieved a delay acuity of 83 ns using a second-order smoothing filter with a cutoff frequency of 8 kHz. The same model achieved a delay acuity of 17 ns when tested with a signal-to-noise ratio of 50 dB. Jitter detection thresholds were an order of magnitude worse than the bat for fifth-order smoothing or for lower cutoff frequencies. Most surprising is that effectively coherent reception is possible with filter cutoff frequencies well below any of the ultrasonic frequencies contained in the bat's sonar sounds. The results suggest that only a modest rise in the frequency response of smoothing in the bat's inner ear can confer full phase sensitivity on subsequent processing and account for the bat's fine acuity or delay.     

水中目标窄带噪声识别的听觉外周模型

为解决听觉外周模型特征在具有工程背景的水中目标声信号分类研究中识别率下降问题,提出了一种外周模型Gammatone滤波器组修正方法,获得的窄带噪声特征可明显提高水中目标识别性能。首先,分析了识别率下降原因,发现声学工程应用中多通道数据采集,导致信号频率范围变窄,而引起声信号的时频特征发生变化。其次,根据听觉模型用Gammatone滤波器组模拟人耳基底膜频率分解特性、低频信息包含水中目标噪声信号的重要类别特征,对原有的听觉模型特征进行插值,对滤波器组的通道数与中心频率进行适应性修正,得到目标噪声在较窄频带的27维特征,修正后的模型能够更精细地反映出目标时频特性。最后,采用神经网络分类器进行实验。结果表明,修正后的听觉模型保留了原较宽频带特征的主要信息,而且进一步提高了对实际目标的分类能力,识别率由原来的82.59%提高到88.80%。本文提出根据工程应用平台的有效接收频带优化听觉外周模型Gammatone滤波器组的设计,采用阵元级的多通道数据进行分析,侧重于工程应用,解决了多通道数据采集中,由于频带变窄,导致信号的特征信息量下降,进而引起声特征识别性能下降的问题,修正后的听觉模型特征,有效地提高水中目标辐射噪声的识别效果。本文对从事无源声呐目标识别、有源声呐目标识别、带宽受限的多通道声数据采集的时频特性分析研究人员具有一定的参考价值。