共查询到17条相似文献,搜索用时 109 毫秒
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荧光造影图像分析是一种重要的糖尿病眼底病变测试方法,针对荧光造影图像易受噪声污染以及白内障病变影响导致眼底视网膜图像辨识困难等问题,基于盲信号处理理论,运用维纳滤波与独立分量分析进行降噪,并运用FASTICA算法进行源信号提取.测试图像处理结果表明:该方法能够有效地抑制噪声,提高分离图像的信噪比,且较好地恢复原始图像. 相似文献
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沥青施工过程中,采集的红外图像容易受到周围环境噪声的影响,使图像变得模糊、信噪比低,从而导致后续图像处理分析的准确度降低。针对该噪声特性,提出了一种Contourlet变换和遗传算法相结合的红外图像增强方法。首先对原始红外图像进行Contourlet变换,得到带有多尺度、多方向信息的带通子带,然后对其进行模糊增强,并通过自适应遗传算法优化模糊增强参数,最后对增强后的带通子带进行Contourlet逆变换,得到效果增强的红外图像。实验结果表明,与其它几种常用的红外图像增强方法相比,此方法能更有效地抑制噪声,提高清晰度,取得了较好的增强效果。 相似文献
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一种结合小波变换和维纳滤波的图像去噪算法 总被引:2,自引:1,他引:1
目的为了有效消除噪声图像中的椒盐噪声、高斯噪声甚至混合噪声,结合维纳滤波的优势和小波分解各分量的特点,提出一种新的图像去噪算法。方法该算法先将含噪声图像进行小波变换,分离出1个低频分量和3个中高频分量,然后对低频分量进行自适应维纳滤波,对3个中高频分量用Canny算子提取边缘,最后将处理后的4个分量进行重构得到去噪后的图像。结果仿真结果表明,该算法对扫描仪引入的常见噪声均表现出较好的去噪效果,PSNR值均大于20 d B。尤其是对于高斯噪声和混合噪声,新算法去噪后的PSNR结果高于维纳滤波、软阈值小波滤波和文献[9]算法1~8 d B,效果较好。结论结合小波变换和维纳滤波的图像去噪算法,能够较好去除噪声图像的多种类型噪声,是一种较为优秀的去噪算法。 相似文献
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单通道语音信号在信噪比较大的环境下经过增强后再识别,能表现出较高的识别率。但是在低信噪比环境下,增强后语音信号的识别率急剧下降。针对此种情况,提出了一种用在识别系统前端的语音增强算法,该增强算法将采集到的带噪语音信号先使用对数最小均方误差(Logarithmic Minimum Mean Square Error,Log MMSE)提高其信噪比,然后再利用改进的维纳滤波去除噪声残留并提升语音可懂度,最后用梅尔频率倒谱系数(Mel-Frequency Cepstral Coefficients,MFCC)和隐马尔科夫模型(Hidden Markov Model,HMM)对增强后的语音信号做特征提取并识别。实验分析结果表明,该方法能有效地抑制背景噪声并减少噪声残留,显著提升低信噪比环境下语音识别的准确性。 相似文献
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Mondal PP Rajan K Ahmad I 《Journal of the Optical Society of America. A, Optics, image science, and vision》2006,23(7):1678-1686
Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a priori, knowledge about the type of noise corrupting the image. This makes the standard filters application specific. Widely used filters such as average, Gaussian, and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high-frequency details, making the image nonsmooth. An integrated general approach to design a finite impulse response filter based on Hebbian learning is proposed for optimal image filtering. This algorithm exploits the interpixel correlation by updating the filter coefficients using Hebbian learning. The algorithm is made iterative for achieving efficient learning from the neighborhood pixels. This algorithm performs optimal smoothing of the noisy image by preserving high-frequency as well as low-frequency features. Evaluation results show that the proposed finite impulse response filter is robust under various noise distributions such as Gaussian noise, salt-and-pepper noise, and speckle noise. Furthermore, the proposed approach does not require any a priori knowledge about the type of noise. The number of unknown parameters is few, and most of these parameters are adaptively obtained from the processed image. The proposed filter is successfully applied for image reconstruction in a positron emission tomography imaging modality. The images reconstructed by the proposed algorithm are found to be superior in quality compared with those reconstructed by existing PET image reconstruction methodologies. 相似文献
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The quantitative characterization of defects in images is commonly performed using the signal-to-noise ratio (SNR). However, there is a strong debate about this measure. First, because there is no single accepted definition of SNR. Second, because the SNR measurements are highly affected by the regions used to estimate the power of the signal and noise in the image. This work provides an overview of some of the most commonly used SNR measures. Images with different sources of noise, and defects with different contrasts, are used to evaluate and compare the ability of these measures to quantitatively characterize defects. The measures are also evaluated when the images are transformed using common image processing operations, including filtering and gamma correction. This work also proposes a methodology to define the regions used to estimate the power of the signal and noise in the images. Two alternative procedures are proposed weather prior information is available about the inspected specimen or not. The proposed methodology is applied on real data from infrared testing, where the considered SNR measures are evaluated. 相似文献
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手背静脉图像的采集过程中,由于图像采集设备、光照、皮下脂肪厚度等因素的影响,手背静脉图像的对比度比较低,同时图像噪声严重影响静脉提取。针对此问题,本文提出了一种基于静脉灰度值特征的图像分割与对比度增强算法。首先提取ROI(有效的感兴趣区域)和对ROI进行维纳滤波;然后采用新的图像分割算法对静脉图像进行静脉提取,利用8-邻接内边界跟踪方法和形态学处理方法对静脉二值图像进行去噪;最后将ROI与去噪后的图像进行加权叠加得到对比度增强的静脉图像。实验结果表明,通过采用基于静脉灰度值特征的图像分割算法可以很好地获取到静脉脉络,最终可以获得高对比度的静脉图像。 相似文献
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针对噪声环境下语音识别率急剧下降的问题,提出了一种基于语音时频域稀疏性原理的改进最小方差无畸变响应波束形成与改进维纳滤波结合的算法。该算法首先利用麦克风阵列语音信号的空间信息,通过基于时频掩蔽的改进最小方差无畸变响应波束形成器,增强目标声源方向的语音信号,抑制其他方向噪声的干扰,然后再使用改进的维纳滤波器去除残留噪声并提高语音可懂度,对增强后的语音信号提取梅尔频率倒谱系数作为特征参数,使用隐马尔可夫模型搭建语音识别系统。实验结果表明,该方法能够有效提高低信噪比环境下的语音识别率,具有较好的鲁棒性。 相似文献
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Spectral histogram using the minimization algorithm-theory and applications to flaw detection 总被引:3,自引:0,他引:3
Li X Bilgutay NM Murthy R 《IEEE transactions on ultrasonics, ferroelectrics, and frequency control》1992,39(2):279-284
In ultrasonic flaw detection in large grained materials, backscattered grain noise often masks the flaw signal. To enhance the flaw visibility, a frequency diverse statistical filtering technique known as split-spectrum processing has been developed. This technique splits the received wideband signal into an ensemble of narrowband signals exhibiting different signal-to-noise ratios (SNR). Using a minimization algorithm, SNR enhancement can be obtained at the output. The nonlinear properties of the frequency diverse statistic filter are characterized based on the spectral histogram, which is the statistical distribution of the spectral windows selected by the minimization algorithm. The theoretical analysis indicates that the spectral histogram is similar in nature to the Wiener filter transfer function. Therefore, the optimal filter frequency region can be determined adaptively based on the spectral histogram without prior knowledge of the signal and noise spectra. 相似文献