共查询到20条相似文献，搜索用时 203 毫秒
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本文针对SAR图像的特点及多尺度变换的优势,综述了多尺度变换在SAR图像的去噪和融合方面的进展。 相似文献
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利用小波技术对监测数据进行去噪分析是一种具有广阔应用前景的数据挖掘技术。首先介绍了小波分析的原理与小波变换尺度间去噪方法,在此基础上,应用小波对一组模拟监测数据进行去噪分析,结论显示小波可以用于监测数据的预测分析。 相似文献
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本文提出了对非平稳的EEG信号的降噪和提取方法。小波变换是一个多尺度的时频分析方法,利用小波变换对预处理后的EEG信号进行多尺度分解,并与自适应滤波相结合进行消噪。用AR模型对复原的EEG信号进行谱估计。根据从人体的大脑皮层采集得到的数据,利用MATLAB进行了仿真实验,得到了比较满意的结果。 相似文献
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一种基于子波变换的语音增强方法 总被引：1，自引：0，他引：1
在分析了随机噪声的子波变换系数在不同尺度上的传递特性和噪声信号奇异性与子波模极大值的关系后,提出了用一尺度间变化的门限阈值来抑制带噪训音信号在不同尺度上噪声子波系数,从而实现了在重构信号中消除噪声的目的。文中还给出了不同信噪比语音信号的子波去噪的计算机仿真结果,从结果上看出,本文的方法有较好的语音去噪、增强效果。 相似文献
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为了解决红外图像对比度低、模糊、含噪声等问题,本文提出基于信息熵准则乌鸦搜索算法和三维块匹配(block matching 3D,BM3D)自适应去噪的电气设备非下采样剪切波变换(non-subsampled shearlet transform, NSST)红外图像增强方法。通过NSST变换将图像分解为不同频率的2个部分。低频图像主要为设备特征,高频子带主要为噪声。针对低频图像中的背景干扰问题,提出乌鸦搜索算法进行分割得到设备前景和环境后景,前者经灰度扩增得到前景增强图,后者经直方图均衡得到后景增强图。针对高频子带中的噪声干扰问题,提出BM3D自适应去噪算法用于去噪。通过实验可知,基于乌鸦搜索算法和自适应BM3D的图像增强算法针对图像分割细节处理、多余杂音的过滤以及对比度的提高均有显著效果。 相似文献
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针对暂态信号的时频分布特点，提出了采用小波包的去噪新方
法。首
先采用小波包变换对暂态信号进行多尺度分解，搜索子频带范数随尺度变化而增大并达到峰
值的子频带，然后对子频带内的小波系数进行阈值处理，最后进行信号重构。理论分析和实
验结果表明本方法简单、有效。 相似文献
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利用小波变换消除噪声的方法有多种,如小波模极大值去噪、小波阀值去噪等.本文首先分析小波变换的基本原理,分别对小波变换的模极大值去噪法和阈值去噪法的原理进行阐述,通过计算机仿真表明小波阈值法和模极大值法去噪的有效可行. 相似文献
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本文系统分析了基于小波变换的门限值去噪方法,研究了小波变换门限值选择的准则及其算法.通过计算机仿真,比较了各种准则的性能,验证了基于小波变换门限值去噪方法的滤波效果.仿真结果表明,基于小波变换门限值的去噪方法可以有效地去除宽带噪声. 相似文献
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形象思维是人们接受视觉形象并在脑中进行复制与分解学习的思维能力,应用形象思维训练可大大提高人们对视觉印象的学习能力,如武术、舞蹈等。因此,可将其应用到健美操教学,以提高教学效率。文章分析了如何将形象思维训练具体应用到健美操教学中。 相似文献
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《Journal of The Franklin Institute》2022,359(9):4489-4512
Ill-posedness results in regularization-based methods being widely used in single image super-resolution (SISR). However, producing super-resolved images with finer details and fewer artifacts is still a great challenge. With the help of the plug-and-play framework, we introduce a novel fidelity term and learned prior knowledge to produce a powerful SISR model. In the proposed fidelity term, called multi-fidelity, the similarity between the observed data and simulated data is measured in terms of both intensity and gradient; these measurements can reflect the tendency of feature response results to degrade into multiple image layers. Based on the half quadratic splitting (HQS) method, the proposed SISR model is split into two sub-problems, which include the multi-fidelity and regularization terms, respectively. In this paper, we design a deep network, named as R&BED, to learn prior image knowledge. Compared with the manually designed regularization term, learned knowledge can preserve easily ignored features. Experimental results indicate that the subjective and objective metrics corresponding to the proposed method are better than those obtained using the comparison methods. 相似文献
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基于PCA及SVM的图像信息隐藏检测* 总被引：1，自引：0，他引：1
本文提出了一种基于主成分分析（PCA, principal components analysis）及支持向量机（SVM, support vector machines）的信息隐藏盲检测方法。该方法根据信息隐藏时对载体图像引入噪声的特点,通过分析图像块的主成分,计算出图像的特征向量。通过对原始样本图像和藏密样本图像特征向量的学习和训练,得到SVM检测模型,可用于信息隐藏的盲检测。实验结果表明,该方法能够有效地检测出目前常用的信息隐藏方法。 相似文献
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《Journal of The Franklin Institute》2022,359(8):3808-3830
Compared to the traditional single color plane based image denoising methods, the quaternion valued singular value decomposition (QSVD) exploits the relationship among different color planes. Hence, it has been applied to the color image denoising. On the other hand, compared to the non-overlapping based image denoising methods, the two dimensional real valued singular spectrum analysis (2DSSA) constructs the trajectory matrix with many elements in the matrix being overlapped. Since the 2DSSA exploits the local information within each color plane, it has also been applied to the single color plane based image denoising. However, neither these two image denoising methods can exploit the relationship among the color planes and the local information within each color plane simultaneously. Therefore, this paper proposes a two dimensional quaternion valued singular spectrum analysis (2DQSSA) based method for performing the color image denoising. Our proposed method can enjoy the advantages of both methods. However, the most critical issue for the 2DQSSA is on the selection of these 2DQSSA components. This paper finds that the optimal total number of the 2DQSSA components used for performing the reconstruction is monotonic decreasing with respect to the power of the noise in the image. Therefore, the polynomial fitting approach is proposed to model this relationship. Since the 2DQSSA based denoising method exploits the relationship among the red color plane, the green color plane and the blue color plane, the 2DQSSA based denoising method outperforms the conventional single color plane based denoising methods. Moreover, since the 2DQSSA based denoising method also exploits the local relationship within each color plane, the 2DQSSA based denoising method outperforms the non-overlapping based methods. 相似文献