基于多分辨率显著性滤波的微动信号增强方法

Micro-motion signal enhancement method based on multi-resolution saliency filtering

微动信号是典型的非平稳信号,时频分析能够获得微动信号的联合时间频率分布图像,是微动信号分析的主要工具之一,良好的时频图像质量能保证后续特征提取和参数估计的准确性。然而在实际场景中,时频图像通常受到噪声污染,使得微动信号难以分辨,严重制约了后续特征提取和参数估计。根据显著性检测和图像金字塔的基本原理,本文在多分辨率表示图像上分别计算显著性并滤波,最后进行加权融合获得增强的时频图像,有效抑制了噪声,提升了低信噪比(signal to noise ratio,SNR)下时频图像的质量和微动信号的显著性。实验结果表明,对于仿真信号以及暗室测量信号,在-7~7 dB SNR下,采用该方法均能显著提升时频图像质量,且-3 dB以下时能大幅提高周期估计的准确率,是一种有效的微动信号增强方法。

Micro-motion signal is a typical non-stationary signal.Time-frequency analysis can obtain the joint time-frequency distribution image of micro-motion signal,and it is one of the main tools for micro-motion signal analysis.Good time-frequency image quality can ensure the accuracy of the subsequent feature extraction and parameter estimation.However,in the actual scene,time-frequency images are usually polluted by noise,which makes it difficult to distinguish micro-motion signals,which seriously restricts the subsequent feature extraction and parameter estimation.According to the basic principles of saliency detection and image pyramid,we compute the saliency on the multi-resolution images separately,and then perform the filtering process.Finally,we perform weighted fusion of them to obtain the enhanced time-frequency image.This method effectively suppresses noise,improves the quality of time-frequency images and the saliency of micro-motion signals under low signal to noise ratio(SNR).Experimental results show that for simulated signals as well as darkroom measured signals,the proposed method can significantly improve the time-frequency image quality when the SNR is from-7 dB to 7 dB,and the accuracy of period estimation can be greatly improved when the SNR is below-3 dB.Therefore,it is an effective method for micro-motion signal enhancement.