Application of multiwavelet based image processing to corona detection

WANG Xin; YU Xiao; SUI Yong-xin; YANG Huai-jiang; PANG Yun-jie

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Application of multiwavelet based image processing to corona detection

Article | 更新时间：2020-08-12

- Application of multiwavelet based image processing to corona detection
- Optics and Precision Engineering Vol. 14, Issue 4, Pages: 714-719(2006)
- 作者机构：
  
  1. 吉林大学计算机科学与技术学院,吉林长春,130012
  2. 空军航空大学, 吉林长春 130022
  3. 中国科学院长春光学精密仪器机械与物理研究所应用光学国家重点实验室,吉林长春,130033
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Received：22 August 2005，
  
  Revised：12 June 2006，
  
  Published：2006
- 稿件说明：
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王欣, 于晓, 隋永新, 杨怀江, 庞云阶. 基于多小波的图像处理在电晕检测中的应用[J]. 光学精密工程, 2006,14(4): 714-719

WANG Xin, YU Xiao, SUI Yong-xin, YANG Huai-jiang, PANG Yun-jie. Application of multiwavelet based image processing to corona detection[J]. Editorial Office of Optics and Precision Engineering, 2006,14(4): 714-719
王欣, 于晓, 隋永新, 杨怀江, 庞云阶. 基于多小波的图像处理在电晕检测中的应用[J]. 光学精密工程, 2006,14(4): 714-719 DOI：

WANG Xin, YU Xiao, SUI Yong-xin, YANG Huai-jiang, PANG Yun-jie. Application of multiwavelet based image processing to corona detection[J]. Editorial Office of Optics and Precision Engineering, 2006,14(4): 714-719 DOI：

摘要

提出了基于多小波变换的图像处理方法

该方法以多小波变换为基础

在一次多小波分解与重构之间完成双谱段图像处理。首先进行多小波变换

将变换系数进行软阈值收缩消去噪声;然后根据图像中需增强的信息

选择增强系数进行子带增强;最后提出一种新的自适应权值融合规则

采用这个规则融合变换系数

进行小波重构得到处理后的单幅图像。实验表明

这种方法不仅能提高图像的视觉效果

增强源图像的边缘信息

而且能很好地将源图像中对电晕检测有用的信息融合在一起

提高电晕检测系统的定位精度。

Abstract

With combination of the multiwavelet threshold shrinkage

subband enhancement and image fusion

a image processing method accomplished by means of discrete multiwavelet transform was presented. In this method

Multi Wavelet Transform(MWT) is the first step and the MWT coefficients are denoised by soft threshold multiwavelet shrinkage. Then subband enhancement is used to enhance the edge related coefficients. A new adaptive weight average image fusion rule was proposed to merge the coefficients and acquire fused coefficients. The experimental results show that the proposed image processing method can produce visually acceptable image and reduce noise while the source image is enhanced. This method also can fuse details of input images and improve the locating precision of the corona detection system.

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references

SHARK L K, YU C. Denoising by optimal fuzzy thresholding in wavelet domain [J]. IEEE Electronical Letters, 2000, 36 (6): 581-582.[2] PAJARES G, CRUZ J M. A wavelet-based image fusion tutorial [J]. Pattern Recognition, 2004, 37(9): 1855-1872.[3] 陈洪波, 王强, 张孝飞, 等. 基于小波系数邻域特征的图像融合[J]. 光学精密工程, 2003, 11(5): 516-522. CHEN H B, WANG Q, ZHANG X F, et al. Image fusion based on neighborhood features of wavelet coefficients[J]. Optics and Precisions Engineering, 2003, 11(5): 516-522.(in Chinese)[4] STRELA V. Multiwavelets: theory and application . Ph.D.Dissertation, MIT, 1996.[5] LEBURN J, VETTERLI M. High order balanced multiwavelets . Proc. IEEE in Conf. Acoustic Speech Signal Process (ICASSP), 1998: 12-15.[6] DONOHO D. Denoising by soft thresholding [J]. IEEE Trans. on Information Theory, 1995, 41 (3): 613-627.[7] ZHANG Z, BLUM R S. A categorization of multiscale-decompostion-based image fusion schemes with a performance study for a digital camera application[J]. Proc. IEEE, 1999, 87(8): 1315-1326.[8] CHANG S G, YU B, VATTERELI M. Adaptive wavelet thresholding for image denoising and compression [J]. IEEE Transactions on Image Processing, 2000, 9(9): 1532-1546.[9] QU G H, ZHANG D L, YAN P F. Information measure for performance of image fusion [J]. Electronic Letters, 2002, 38 (7): 313-315.

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