变压器油下图像增强算法
作者:
作者单位:

1.沈阳理工大学;2.青岛超高清视频创新科技有限公司

作者简介:

通讯作者:

中图分类号:

TP273

基金项目:

中国博士后科学基金


Image Enhancement Algorithm Under Transformer Oil
Author:
Affiliation:

Shenyang Ligong University

Fund Project:

China Postdoctoral Science Foundation

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    由于变压器油长期工作于高温、高压的变压器内部,会出现变质、变色的现象,因此变压器检测机器人搭载的视觉系统在变压器油下获取的图像存在色彩失真、对比度低等问题。本文提出了一种变压器油下图像融合增强算法,以提高变压器内部故障点检测的准确度。首先,利用完美反射算法对图像进行白平衡处理,以消除油下光照强度不均匀对图像颜色的影响,使色彩更加均衡。然后,对色彩校正的图像进行自适应伽马校正,以提高图像的对比度。最后,采用多尺度融合策略对色彩校正后的图像及自适应伽马校正处理后的图像进行融合,得到变压器油下清晰的图像。实验结果表明, 经本文算法处理后的变压器油下图像色彩鲜明、细节丰富,与原始图像相比,图像质量评价指标UCIQE、特征点匹配、信息熵在测试下均有显著提高,可为变压器内部故障检测提供清晰的数据。

    Abstract:

    Since the transformer oil works in a high temperature and high pressure transformer, the phenomenon of deterioration and discoloration will occur, so the image of transformer oil obtained by the visual system of the transformer internal fault detection robot has a problem of color distortion and low contrast. This paper describes a transformer oil image fusion enhancement algorithm to improve the accuracy of internal fault point detection of transformers. First, the image is white balanced by the perfect reflection algorithm to eliminate the impact on uneven oil light, the color of the image is made more balanced. Then, conduct adaptive gamma correction on the color correction image to improve the contrast to the image. Finally, a multi-scale fusion strategy is used to fuse the image after the color correction and the image after the adaptive gamma correction to obtain a clear image of transformer oil. The experimental results show that after the algorithm is treated, the color of the image of the transformer oil is more vivid, more detailed. Compared with the original image, the image quality evaluation indicator UCIE, feature point matching, information entropy is significantly improved under test, which provides a clear data for the internal fault detection of the transformer.

    参考文献
    相似文献
    引证文献
引用本文
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2021-09-13
  • 最后修改日期:2022-01-25
  • 录用日期:2022-01-28
  • 在线发布日期: 2022-03-01
  • 出版日期: