基于改进直方图均衡的SF6泄漏区域增强算法

卢泉; 黄粒峰; 胡梦竹

基于改进直方图均衡的SF6泄漏区域增强算法

1.
广西大学电气工程学院, 广西南宁 530004
2.
广西电网有限责任公司电力科学研究院, 广西南宁 530004

基金项目:

国家自然科学基金资助项目 61863002

详细信息

作者简介:
卢泉（1982-），男，博士，副教授，主要研究方向：智能电力技术。E-mail: luquan@gxu.edu.cn

中图分类号: TP391
计量
- 文章访问数: 21
- HTML全文浏览量: 12
- PDF下载量: 5
- 被引次数: 0
出版历程
- 收稿日期: 2022-03-16
- 修回日期: 2022-04-20
- 刊出日期: 2024-04-20

SF6 Leakage Region Enhancement Algorithm Based on Improved HE

1.
School of Electrical Engineering, Guangxi University, Nanning 530004, China
2.
Electric Power Science Research Institute, Guangxi Power Grid Co. Ltd., Nanning 530004, China

摘要

摘要: SF6气体红外成像易受环境噪声影响，呈低对比度与低信噪比特性。导致现有算法无法自适应增强SF6泄漏区域和抑制高斯噪声。针对上述问题，提出一种基于改进HE的SF6泄漏区域增强算法。该算法首先采用单尺度Retinex处理原始SF6图像获得反射图像，然后利用引导滤波将反射图像分解为细节层和基本层，最后采用改进的直方图均衡化来自适应处理基本层，并将增强后的图像进行融合来获得最终的图像。实验结果表明：本文算法不仅能够自适应增强泄漏区域的对比度，而且具有良好边缘保持特性和抑制高斯噪声的性能。其增强效果优于现有的SF6红外图像增强算法。有效改善了SF6红外图像低对比度和低信噪比特性。
- 单尺度Retinex /
- 引导滤波 /
- SF6红外图像 /
- 直方图均衡化
Abstract: Infrared imaging of SF6 gas is easily affected by environmental noise and exhibits low contrast and signal-to-noise ratio. As a result, existing algorithms cannot adaptively enhance the SF6 leakage area or suppress Gaussian noise. Therefore, this study proposes an improved HE-based SF6 leakage-area enhancement algorithm. The algorithm first uses SSR to process the original SF6 image to obtain the reflection image R, and then uses guided filtering to decompose the reflection image R into detail and base layers. Finally, an improved histogram equalization is used to adaptively process the base layer, and the enhanced images are fused to obtain the final image. The experiment results demonstrate that the proposed algorithm can not only adaptively enhance the contrast of the leaked area but also has good edge preservation and Gaussian noise suppression performance. Its enhancement effect is superior to that of the existing SF6 infrared image enhancement algorithm. This effectively improves the low-contrast and low signal-to-noise ratio characteristics of the SF6 infrared images.
- single-scale-retinex /
- guide filtering /
- SF6 infrared image /
- histogram equalization

HTML全文

图 1 本文算法流程

Figure 1. Flow chart of the algorithm in this paper

下载: 全尺寸图片幻灯片

图 2 Retinex理论示意图

Figure 2. Schematic diagram of the Retinex theory

下载: 全尺寸图片幻灯片

图 3 不同算法对SF6图像a的增强结果

Figure 3. The enhancement results of the SF6 image a by the different algorithms

下载: 全尺寸图片幻灯片

图 4 不同算法对SF6图像b的增强结果

Figure 4. The enhancement results of the SF6 image b by the different algorithms

下载: 全尺寸图片幻灯片

表 1 不同算法的客观评价指标

Table 1. Objective evaluation indicators for the different algorithms

Image	Index	Original	HE	Literature [7]	Proposed
Image a	IE	6.031	7.103	7.066	7.227
	SD	37.046	73.821	44.946	46.465
	PSNR	-	6.615	9.975	16.045
	T/s	-	0.047	0.089	0.082
Image b	IE	6.405	7.118	6.781	7.273
	SD	62.109	74.408	63.094	64.496
	PSNR	-	4.891	5.561	8.951
	T/s	-	0.046	0.086	0.085

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参考文献(12)

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