首页> 外文会议>International Congress on Image and Signal Processing >Research on Insulator Infrared Image Denoising Using Significant Wavelet-Domain Hidden Markov Tree Models
【24h】

Research on Insulator Infrared Image Denoising Using Significant Wavelet-Domain Hidden Markov Tree Models

机译:利用重要小波域隐马尔可夫树模型研究绝缘子红外图像去噪

获取原文

摘要

Infrared temperature measurement has been already applied for online monitoring of electric power equipment. However, for the high noise and low contrast degree of infrared images produced by monitoring process, how to remove the noise of images effectively has become the key point of recent research. In this paper, we propose a new insulator infrared image denoising method using significant coefficient rule. In order to incorporate the spatial dependencies into the denoising procedure, HMT model is explored and EM algorithm is proposed to estimate model parameters. The experimental results show that, compared with the existing insulator infrared image denoising methods, the proposed method is not only propitious to keep image edge from damaging and solve the edge blurring problem, but also increasing PSNR of images. In addition, the proposed method also gets a better visual effect.
机译:红外线温度测量已应用于电力设备的在线监控。然而,对于通过监控过程产生的高噪声和红外图像的低对比度,如何有效地消除图像的噪声已成为最近研究的关键点。在本文中,我们提出了一种利用显着系数规则的新型绝缘体红外图像去噪方法。为了将空间依赖性纳入去噪过程中,探索了HMT模型,并提出了EM算法来估计模型参数。实验结果表明,与现有的绝缘体红外图像去噪方法相比,所提出的方法不仅有利于保持图像边缘损坏和解决边缘模糊问题,而且增加了图像的PSNR。此外,所提出的方法还获得了更好的视觉效果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号