Denoising Method of UAV Inspection Image Based on Improved Wavelet Threshold

doi:10.3969/j.issn.1008-0198.2024.06.016

Abstract

Abstract: 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 incomplete or the useful image information is lost too much caused by the traditional threshold selection method.

Key words: unmanned aerial vehicle (UAV), power inspection, wavelet transform, image denoising, threshold processing

CLC Number:

TM755

LIU Yunfei, ZHOU Guangyuan, YIN Fengmei, LIU Cong, GUO Qingyue, HUANG Zhaoyang. Denoising Method of UAV Inspection Image Based on Improved Wavelet Threshold[J]. Hunan Electric Power, 2024, 44(6): 113-119.

References

[1] 杨洪朝,席燕辉,项胜,等. 基于深度学习的输电线路巡检无人机图像分类方法[J]. 湖南电力,2024,44(1):53-58.
[2] 陈重旭. 高压输电线路无人机巡检图像目标检测理论及算法研究[D]. 重庆:重庆大学,2021.
[3] 霍红刚,周蠡,蔡杰,等. 基于先验知识Faster R-CNN的输电线路无人机图像识别方法[J]. 智慧电力,2024,52(6):108-115.
[4] 倪澎涛. 研究数字图像处理技术的现状及其发展方向[J]. 信息通信,2017,30(8):110-111.
[5] 李俊杰,张晓龙,王聪,等. 一种改进的BM3D绝缘子图像去噪算法[J]. 软件导刊,2021,20(8):191-194.
[6] 姜文涛,卜艺凡. 融合CNN和Transformer的图像去噪网络[J]. 计算机系统应用,2024,33(7):39-51.
[7] 韩天奇. 基于小波变换与深度学习的图像去噪算法研究[D]. 张家口:河北建筑工程学院,2023.
[8] ZHANG K,ZUO W,ZHANG L.FFDNet:toward a fast and flexible solution for CNN-based image denoising[J]. IEEE Transactions on Image Processing,2018,27(9):4608-4622.
[9] 逯兆虎,贾少雷,李广豪,等. 自适应新小波阈值函数中子图像去噪方法[J]. 同位素,2024,37(2):153-163.
[10] MANJON J V,COUPE P,MARTI-BONMATI L,et al.Adaptive non-local means denoising of MR images with spatially varying noise levels[J]. Journal of Magnetic Resonance Imaging, 2010,31(1):192-203.
[11] ZHANG K,ZUO W,CHEN Y J,et al.Beyond a gaussian denoiser:Residual learning of deep cnn for image denoising[J]. IEEE Transactions on Image Processing,2017,26(7):3142-3155.
[12] 胡帅,高峰,龚卓然,等. 基于Transformer和通道混合并行卷积的高光谱图像去噪[J]. 中国图象图形学报,2024,29(7):2063-2074.
[13] 吴垒. 基于深度学习的图像去噪研究与应用[D]. 长春:吉林大学,2023.
[14] 林东升. 一种基于遗传算法的小波阈值去噪方法[J]. 科技与创新,2018(21):115-117.
[15] CAI J H,XIAO Y L.Impulse interference processing for MT data based on a new adaptive wavelet threshold de-noising method[J]. Arabian Journal of Geosciences,2017,10(18):407.
[16] 董广杰,林旭梅. 小波分析在图像降噪中的应用[J]. 微型机与应用,2013,32(12):32-34.
[17] 何永洋. 基于离散小波变换的图像压缩感知编码技术研究[D]. 南京:南京邮电大学,2018.
[18] 周小明. 基于小波去噪和梯度校正的分水岭图像分割技术研究[J]. 数学杂志,2019,39(4):583-588.
[19] WANG J F.A wavelet denoising method based on the improved threshold function[C]// 2014 International Conference on Wavelet Analysis and Pattern Recognition. Lanzhou,China. IEEE,2014:70-74.
[20] 曾友伟,杨恢先,唐飞,等. 基于灰色关联度改进的Contourlet变换图像去噪算法[J]. 计算机应用,2013,33(4):1103-1107.
[21] LI N,ZHANG J Y,DENG Z Y.Optimization of wavelet threshold denoising based on edge detection[C]//Ninth International Conference on Digital Image Processing(ICDIP2017).Hongkong,China.SPIE,2017,10420:137-141.
[22] CAO J,WANG P H,WU B,et al.Improved wavelet hierarchical threshold filter method for optical coherence tomography image de-noising[J]. Journal of Innovative Optical Health Sciences,2018,11(3):54-64.