期刊信息
- 刊名： 河北师范大学学报（自然科学版）Journal of Hebei Normal University (Natural Science)
- 主办： 河北师范大学
- ISSN： 1000-5854
- CN： 13-1061/N
- 中国科技核心期刊
- 中国期刊方阵入选期刊
- 中国高校优秀科技期刊
- 华北优秀期刊
- 河北省优秀科技期刊
基于峰度ICA和本征图像滤波的超高斯信号去噪方法
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1. 淮北职业技术学院 计算机系, 安徽 淮北 235000;
2. 淮北师范大学 物理与电子信息学院, 安徽 淮北 235000 -
DOI：10.13763/j.cnki.jhebnu.nse.2020.03.004
De-noising Method for Super Gaussian Signal Based on Kurtosis ICA and Intrinsic Image Filtering
摘要/Abstract
针对超高斯信号的分析领域中，传统独立分量分析法（ICA）提取的独立分量信噪比低、耗时长的问题，提出了基于峰度的ICA算法，以混合超高斯信号的统计特性为基础，采用峰度作为超高斯性的唯一判断依据，首先经过本征图像滤波压缩信源空间，然后经过球化消除信源的二阶相关性，最后通过迭代判断最大峰度，对应输出一个独立分量.超高斯地震信号实验表明，所提算法有效可行，与传统ICA、基于负熵的ICA相比，迭代次数更少，输出信噪比更高.
In the field of super Gaussian signal analysis,the signal-to-noise ratio of independent components extracted by traditional ICA is low and the process is time-consuming.To solve these problems,we propose an ICA algorithm based on kurtosis.Based on the statistical characteristics of the mixed super Gaussian signal,the proposed method uses kurtosis as the only judgment basis of super Gaussian.Firstly,the source space is compressed by intrinsic image filtering,then the second-order correlation of the source is eliminated by spheroidization,finally,the maximum kurtosis is determined by iteration,which output an independent component correspondingly.The experiment of super Gaussian seismic signal demonstrates that the proposed method is effective and feasible.Compared with traditional ICA and ICA based on negative entropy,it has fewer iterations and higher output SNR.
关键词
参考文献 12
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