融合彩色模型空间的电力设备红外图像增强

王振; 刘磊

融合彩色模型空间的电力设备红外图像增强

王振,
刘磊^,

安徽工业大学电气与信息工程学院, 安徽马鞍山 243000

基金项目:

国家自然科学基金青年项目 61602008

详细信息

作者简介:
王振(1996-)，男，硕士研究生，研究方向为图像处理与模式识别。E-mail: wz0608@petalmail.com

通讯作者:
刘磊(1981-)，男，讲师，博士，研究方向为机器视觉、图像处理与模式识别。E-mail: liulei198109@126.com

中图分类号: TP391
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- 文章访问数: 120
- HTML全文浏览量: 31
- PDF下载量: 28
出版历程
- 收稿日期: 2022-03-30
- 修回日期: 2022-05-01
- 刊出日期: 2024-02-19

Infrared Image Enhancement for Power Equipment Based on Fusion Color Model Space

WANG Zhen,
LIU Lei^,

School of Electrical and Information Engineering, Anhui University of Technology, Maanshan 243000, China

摘要

摘要: 电力设备红外图像普遍存在亮度暗、对比度低等问题，针对此现象，提出了一种融合彩色模型空间的图像增强算法。该方法将图像的对比度与亮度增强转换至HSV与RGB空间中分别进行处理：RGB空间中，首先预处理图像中的高灰度级，并采取混合滤波的方式抑制图中噪声，然后使用增强函数提高图像亮度，最后将增强图像转换至HSV空间中并提取H、S、V三分量图；HSV空间中，采用伽马变换和CLAHE(contrast limited adaptive histogram equalization)算法实现V分量的亮度提升，并采取非线性饱和度矫正函数处理S分量提升图像对比度，最后将处理分量与提取分量进行对应融合得到HSV空间中的增强图像，并将其转回RGB空间中得到最终的输出图像。实验结果表明，本文算法能明显地提升红外图像的亮度与对比度，增强后的3组图像其灰度均值和标准差平均值分别为115.94和78.65，相对于原图的平均值分别提升了81.59和36.17。
- 电力设备红外图像 /
- 图像增强 /
- 彩色模型空间融合 /
- HSV、RGB分量增强
Abstract: There are numerous problems with infrared imaging using power equipment, such as dark brightness and low contrast. To solve these problems, an enhancement algorithm using a color-model space was proposed. In this method, the contrast and brightness enhancement of the image are processed in the HSV and RGB spaces, respectively. First, the high gray levels of the image are preprocessed, and the mixed filtering method is adopted to suppress the noise in the image. An enhancement function is used to improve the brightness of the image. Finally, the enhanced image is converted into the HSV space, the H, S and V component images are extracted, the gamma transform and CLAHE algorithms are used to improve the brightness of V component, and a nonlinear saturation correction function is used to process component S to improve the image contrast. Finally, the enhanced image in the HSV space is obtained by the corresponding fusion of each processing and extraction component, and is transferred back to the RGB space to obtain the final output image. Experimental results show that the proposed algorithm can significantly improve the contrast and brightness of infrared images. The average gray mean and standard deviation of the enhanced 6 groups of images were 115.94 and 78.65, respectively, which are improvements of 81.59 and 36.17 compared with the original image.
- infrared image of power equipment /
- image enhancement /
- color model space fusion /
- HSV and RGB component enhancement

HTML全文

图 1 本文图像增强算法流程

Figure 1. Flow chart of image enhancement algorithm proposed in this paper

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图 2 原始图像及其RGB空间分量的灰度直方图

Figure 2. Original image and its RGB space component gray histogram

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图 3 增强图像及其RGB空间分量的灰度直方图

Figure 3. Enhanced image and its RGB space component gray histogram

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图 4 V分量处理结果对比

Figure 4. Comparison of V component processing results

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图 5 S分量处理结果对比

Figure 5. Comparison of S component processing results

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图 6 两空间图像融合结果

Figure 6. Two space image fusion result

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图 7 用于测试的电力设备红外图像

Figure 7. Infrared images of power equipments for test

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图 8 红外图像1的增强对比图

Figure 8. Enhanced contrast images of infrared image 1

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图 9 红外图像2的增强对比图

Figure 9. Enhanced contrast images of infrared image 2

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图 10 红外图像3的增强对比图

Figure 10. Enhanced contrast images of infrared image 3

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表 1 客观评价结果

Table 1 Objective evaluation results

Evaluation criterion	Comparison algorithm	Image 1	Image 2	Image 3
μ	Original	15.42	29.26	58.35
	HE	178.21	136.75	129.58
	Homomorphic	31.82	55.50	69.67
	MSR	27.05	46.70	70.25
	CLAHE	26.88	44.61	48.08
	AGCWD	25.57	40.73	92.89
	Reference [12]	45.70	86.46	140.67
	Proposed	68.89	132.59	146.33
δ	Original	38.84	55.38	33.21
	HE	35.11	69.55	71.79
	Homomorphic	65.54	72.37	44.11
	MSR	51.60	56.61	46.43
	CLAHE	56.31	62.23	49.29
	AGCWD	64.34	74.70	55.29
	Reference [12]	81.23	84.28	59.44
	Proposed	89.39	81.35	65.21

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