基于改进小波阈值的无人机巡检图像去噪方法  

Denoising Method of UAV Inspection Image Based on Improved Wavelet Threshold

作　　者：刘云飞 周光远 尹凤梅 刘聪 郭清玥 黄照阳 LIU Yunfei;ZHOU Guangyuan;YIN Fengmei;LIU Cong;GUO Qingyue;HUANG Zhaoyang(State Grid Jingmen Power Supply Company,Jingmen 448000,China;Hubei Information Engineering College,Jingmen 448000,China)

机构地区：[1]国网湖北省电力有限公司荆门供电公司,湖北荆门448000 [2]湖北信息工程学校,湖北荆门448000

出　　处：《湖南电力》2024年第6期113-119,共7页Hunan Electric Power

基　　金：国家电网有限公司科技项目(5500-202322539A-3-2-ZN)。

摘　　要：针对基于传统小波阈值的无人机巡检图像去噪方法存在的不足,提出一种改进的小波阈值去噪方法。通过改进阈值选取方式和阈值函数,无人机巡检图像去噪效果更加显著,并适用于解决多分辨率问题。改进的阈值函数通过引入调节因子避免了硬阈值函数不连续从而导致在图像重构时产生的震荡,同时避免了软阈值函数存在固定偏差导致的图像重构时精度下降、图像失真问题。通过改进阈值选取方式,克服了传统的阈值选取方法造成的去噪不彻底或者图像有用信息损失过多的问题。Aiming at the deficiencies of unmanned arerial vehicle(UAV)inspection image denoising method based on the traditional wavelet threshold,an improved wavelet threshold denoising method is proposed.By improving the threshold selection method and threshold function,the denoising effect of UAV inspection image is more remarkable and suitable to solve the multi⁃resolution problem.By introducing regulating factors the improved threshold function avoids the discontinuity of hard threshold function which can lead to oscillations during image reconstruction and the fixed deviation of soft threshold function which can lead to loss of accuracy and distortion in image reconstruction.The improved threshold selection method avoids the problem that denoising is incom⁃plete or the useful image information is lost too much caused by the traditional threshold selection method.

关 键 词：无人机 电力巡检 小波变换 图像去噪 阈值处理 

分 类 号：TM755[电气工程—电力系统及自动化]