基于小波变换的紫外光和可见光图像融合算法

针对高压设备电晕放电和故障点定位的问题,设计了一种基于紫外电晕技术的高压放电检测系统,并提出了一种适用于紫外光图像和可见光图像融合的小波算法。利用240 nm^280 nm这一日盲波段获取紫外放电图,引入了图像融合技术,实现了对电晕区域精确地定位。小波变换具有很好的时频特性,可以用于同时提取不同尺度上的有用信息,为图像融合提供了有利条件。通过仿真实验,将该算法与以往的加权平均方法和拉普拉斯金字塔变换方法进行了比较。研究结果表明,由该方法得到的融合图像可以有效地保留细节和边缘,图像更加清晰,细节更加丰富,融合效果明显优于其他两种方法。该方法满足紫外检测系统的需要,能够广泛应用于电晕放电的检测和定位,从而为解决电晕问题提供依据。

Image fusion algorithm of ultraviolet and visible images based on wavelet transform

Aiming at the phenomena of the corona discharge on high-voltage apparatus and fault location,the detection system to highvoltage discharge based on UV corona technology was designed to solve the problem,and a new image fusion method based on wavelet transform which applies to UV and visible image was proposed.Ultraviolet discharge images were acquired by using the sun-blind band from 240 nm to 280 nm.In order to locate the position of the corona precisely,image fusion technology was introduced.Useful information on different scales can be extracted by wavelet transform with good time-frequency characteristics,and favorable conditions were provided for image fusion.The algorithm was compared with weighted average method,Laplacian-based and wavelet-based through simulation experiments.The results indicate that the proposed algorithm is better than others.Its fused image is clearer and more detailed,preserving the detail and edge effectively.The new method can meet the needs of ultraviolet detection system and improve the locating precision of the corona detection system,so as to provide the basis for solving the problem of corona.