基于多尺度Retinex和ASIFT特征的变电站设备红外和可见光图像配准研究

Ning Yang, Yang Yang, Peng Li, Fei Gao
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引用次数: 2

摘要

变电站设备红外和可见光图像配准对电力设备检测和故障诊断具有重要意义。变电站的场景比较复杂,设备图像的背景通常比较杂乱,可见图像的特征点容易落在背景上。该金属具有良好的导热性,其温度接近环境温度。以金属塔为背景的红外图像中金属部分无法清晰显示,容易造成图像失配甚至无法匹配。现有的SIFT、SURF、ASIFT等配准方法难以有效解决这种背景复杂的变电站设备图像配准问题。为了解决这一问题,本文提出了一种基于多尺度Retinex和ASIFT特征的红外与可见光图像配准算法。首先,采用多尺度Retinex算法对可见光图像中代表目标属性的分量进行分离,以减弱杂波背景的影响;然后,采用ASIFT算法进行仿射变换,模拟所有视差下的仿射变形,对特征点进行粗略匹配,最后加入随机采样一致算法消除不匹配点。实验结果表明,该算法可将匹配点数量增加至少4倍,平均匹配精度提高13%,平均匹配时间缩短183ms。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on infrared and visible image registration of substation equipment based on multi-scale Retinex and ASIFT features
Infrared and visible image registration of substation equipment is of great significance for power equipment detection and fault diagnosis. The scene of substation is complex, and the background of equipment image is usually messy, and the feature points of visible image are easy to fall on the background. The metal has good thermal conductivity, and its temperature is close to the ambient temperature. The metal part in the infrared image with metal tower as the background can not be clearly displayed, which is easy to cause the image mismatch or even unable to match. The existing registration methods such as SIFT, SURF and ASIFT are difficult to effectively solve this kind of image registration problem of substation equipment with complex background. To solve this problem, this paper proposes an infrared and visible image registration algorithm based on Multi-scale Retinex and ASIFT features. Firstly, the Multi-scale Retinex algorithm is used to separate the components representing the properties of the object in the visible image, so as to weaken the influence of the clutter background. Then, the ASIFT algorithm is used to do affine transformation to simulate the affine deformation under all parallax, and the feature points are roughly matched Finally, the random sampling consistent algorithm is added to eliminate the mismatching points. Experimental results show that the algorithm can increase the number of matching points by at least 4 times, the average matching accuracy is improved by 13%, and the average matching time is shortened by 183ms.
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