基于自适应加权的多尺度图像融合研究

胡雪凯; 罗蓬; 李铁成; 蔡玉汝; 马娜; 周雪青

基于自适应加权的多尺度图像融合研究

胡雪凯¹,
罗蓬¹,
李铁成¹,
蔡玉汝^{2, 3},
马娜^4, ,,
周雪青¹

1.
国网河北省电力有限公司电力科学研究院, 河北石家庄 050021
2.
南瑞集团(国网电力科学研究院)有限公司, 江苏南京 210061
3.
国网电力科学研究院武汉南瑞有限责任公司, 湖北武汉430074
4.
中国科学院上海高等研究院, 上海 201210

基金项目: 国家电网项目《基于云边计算协同的异构传感器网络开放式接入网关与组网技术研究》

详细信息

作者简介:
胡雪凯（1987-），男，硕士，高级工程师，主要从事电力系统计算分析、新能源并网检测分析等方面的研究

通讯作者:
马娜（1985-），女，硕士，助理研究员，主要从事物联网在智能电网，智能工业等方面的研究。E-mail：man@sari.ac.cn

中图分类号: TN219
计量
- 文章访问数: 213
- HTML全文浏览量: 76
- PDF下载量: 54
- 被引次数: 0
出版历程
- 收稿日期: 2021-07-05
- 修回日期: 2021-11-17
- 刊出日期: 2022-04-20

Multi-scale Image Fusion Based on Adaptive Weighting

HU Xuekai¹,
LUO Peng¹,
LI Tiecheng¹,
CAI Yuru^{2, 3},
MA Na^{4
, ,},
ZHOU Xueqing¹

1.
State Grid Hebei Electric Power Research Institute, Shijiazhuang 050021, China
2.
NARI Group Corporation, State Grid Electric Power Research Institute, Nanjing 210061, China
3.
Wuhan NARI Limited Liability Company, State Grid Electric Power Research Institute, Wuhan 430074, China
4.
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China

摘要

摘要: 近年来图像融合技术广泛应用到电力行业，通过不同类型的图像传感器采集电力设备和输电线的图像，经过红外和可见光的图像融合处理，实现电力设备及输电线的智能巡视和故障分析。文中提出一种基于自适应加权的多尺度图像融合算法，采用配准后的可见光和红外图像，进行多尺度小波分解，根据高低频的不同图像特征，低频采用自适应加权融合规则，高频采用绝对值最大的融合规则，将融合后的小波系数进行逆变换后得到全新的融合图像。通过对融合图像的主观和客观评价分析，证明融合算法解决了单一图像传感器采集图像存在的完整性问题，提高了融合图像细节信息，提升了场景的置信度。
- 红外与可见光 /
- 图像融合 /
- 电力设备 /
- 多源传感器
Abstract: In recent years, image fusion technology has been widely used in the power industry. Different types of image sensors are used to collect images of power equipment and transmission lines. Through the fusion of infrared and visible light images, intelligent inspection and fault analysis of power equipment and transmission lines can be realized. This article first briefly introduces common image fusion algorithms and fusion image evaluation standards. A multi-scale image fusion algorithm based on adaptive weighting is proposed, which uses the registered visible light and infrared images to perform multi-scale wavelet decomposition. According to the different image characteristics of high and low frequencies, the low frequency adopts the adaptive weighted fusion rule and the high frequency adopts the fusion rule with the largest absolute value. The fused wavelet coefficients are inversely transformed to obtain a new fused image. Subjective and objective evaluation and analysis of the fusion image confirm that the fusion algorithm solves the integrity problem of the image collected by a single image sensor, enhances the detailed information of the fusion image, and improves the confidence of the scene.
- infrared and visible light /
- image fusion /
- electric equipment /
- multi-sensor

HTML全文

图 1 加权融合算法

Figure 1. Weighted fusion algorithm

下载: 全尺寸图片幻灯片

图 2 基于余弦变换的图像融合处理

Figure 2. Image fusion processing based on cosine transform

下载: 全尺寸图片幻灯片

图 3 基于小波变换的图像融合处理

Figure 3. Wavelet transform image fusion

下载: 全尺寸图片幻灯片

图 4 基于自适应多尺度小波变换的图像融合流程

Figure 4. Image fusion process based on adaptive multi-scale wavelet transform

下载: 全尺寸图片幻灯片

图 5 不同融合算法的融合结果图

Figure 5. Fusion results of different fusion algorithms

下载: 全尺寸图片幻灯片

图 6 小波变换及改进算法的图像融合

Figure 6. Image fusion of WT and improved algorithm

下载: 全尺寸图片幻灯片

表 1 高空输电线图像的融合客观评价标准对比

Table 1. Comparison of objective evaluation criteria for fusion of high-altitude transmission line images

	STD	MEAN	AG	EN	RMSE	SSIM	CEN
Weighted average	39.3650	98.5524	3.1722	7.1353	71.4027	0.3234	1.0664
wavelet transform	39.3650	98.5523	3.1722	7.1353	71.4027	0.3234	1.0664
WT & weighted average	38.2143	109.7571	2.9482	7.1895	85.4409	0.2735	1.1609
Improved wavelet transform	41.2748	98.2816	5.2196	7.2012	70.9102	0.3392	4.0186

下载: 导出CSV

表 2 电力设备图像融合客观评价指标对比

Table 2. Comparison of objective evaluation indicators for image fusion of power equipment

	STD	MEAN	AG	EN	RMSE	SSIM	CEN
Wavelet transform	51.1815	147.3664	6.1471	7.5610	40.2199	0.5510	0.3889
Improved wavelet transform	55.0531↑	147.1156	10.4049↑	7.6992↑	40.4952	0.4329↓	0.3403↓

下载: 导出CSV

参考文献(12)

[1]	王景致, 刘刚, 袁嘉彬, 等. 电力巡检中的图像融合技术与应用[J]. 自动化技术与应用, 2019, 38(8): 4. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDHJ201908026.htm WANG Jingzhi, LIU Gang, YUAN Jiabin, et al. Image fusion technology and application in power inspection[J]. Techniques of Automation and Applications, 2019, 38(8): 4. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDHJ201908026.htm
[2]	Pohl C, Van Genderen J L. Review article multisensor image fusion in remote sensing: concepts, methods and applications[J]. International Journal of Remote Sensing, 1998, 19(5): 823-854. doi: 10.1080/014311698215748
[3]	李婵飞, 刘文晶. 一种新颖的红外与可见光图像融合方法[J]. 红外技术, 2020, 42(4): 74-81. http://hwjs.nvir.cn/article/id/hwjs202004010 LI Chanfei, LIU Wenjing. Novel fusion method for infrared and visible light images[J]. Infrared Technology, 2020, 42(4): 74-81. http://hwjs.nvir.cn/article/id/hwjs202004010
[4]	陈凤翔, 刘博迪, 方广东. 基于机器视觉的无人机电力巡线技术[J]. 电子技术与软件工程, 2019, 150(4): 76-77. https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU201904047.htm CHEN Fengxiang, LIU Bodi, FANG Guangdong. Research on the technology of UAU power line inspection based on machine vision[J]. Electronic Technology & Software Engineering, 2019, 150(4): 76-77. https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU201904047.htm
[5]	王立军, 张拓, 刘光伟, 等. 基于机器视觉技术的高压断路器机械特性诊断[J]. 高电压技术, 2020, 46(6): 303-309. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202006036.htm WANG Lijun, ZHANG Tuo, LIU Guangwei, et al. Diagnostics on mechanical characteristics of high voltage circuit breaker based on machine vision technology [J]. High Voltage Engineering, 2020, 46(6): 303-309. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202006036.htm
[6]	Ardeshir A Goshtasby, Stavri Nikolov. Guest editorial: image fusion: advances in the state of the art[J]. Information Fusion, 2007, 8(2): 114-118. doi: 10.1016/j.inffus.2006.04.001
[7]	苗启广, 王宝树. 基于改进的拉普拉斯金字塔变换的图像融合方法[J]. 光学学报, 2007, 27(9): 1605-1610. doi: 10.3321/j.issn:0253-2239.2007.09.013 MIAO Qiguang, WANG Baoshu. Multi-sensor image fusion based on improved Laplacian pyramid transform[J]. Acta Optics Sinica, 2007, 27(9): 1605-1610. doi: 10.3321/j.issn:0253-2239.2007.09.013
[8]	晁锐, 张科, 李言俊. 一种基于小波变换的图像融合算法[J]. 电子学报, 2004, 32(5): 750-753. doi: 10.3321/j.issn:0372-2112.2004.05.011 CHAO Rui, ZHANG Ke, LI Yanjun. An image fusion algorithm using wavelet transform [J]. Acta Electronica Sinica, 2004, 32(5): 750-753. doi: 10.3321/j.issn:0372-2112.2004.05.011
[9]	ZHANG Bin, ZHENG Yongguo, FANG Wei, et al. A new image fusion algorithm based on second generation wavelet transform [C]// Computational Intelligence & Natural Computing Proceedings Second International Conference, 2010, 1: 390-393.
[10]	陶冰洁, 王敬儒, 许俊平. 基于小波分析的不同融合规则的图像融合研究[J]. 红外技术, 2006(7): 62-65. doi: 10.3969/j.issn.1001-8891.2006.07.014 TAO Bingjie, WANG Jingru, XU Junping. Study on image fusion based on different fusion rules of wavelet transform[J]. Infrared Technology, 2006(7): 62-65. doi: 10.3969/j.issn.1001-8891.2006.07.014
[11]	张生伟, 李伟, 赵雪景. 一种基于稀疏表示的可见光与红外图像融合方法[J]. 电光与控制, 2017, 24(6): 47-52. https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201706012.htm ZHANG Shengwei, LI Wei, ZHAO Xuejing. A method for fusion of visible and infrared images based on sparse representation[J]. Electronics Optics & Control, 2017, 24(6): 47-52. https://www.cnki.com.cn/Article/CJFDTOTAL-DGKQ201706012.htm
[12]	杨艳春, 李娇, 王阳萍. 图像融合质量评价方法研究综述[J]. 计算机科学与探索, 2018, 12(7): 6-20. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTS201807002.htm YANG Yanchun, LI Jiao, WANG Yangping. Review of image fusion quality evaluation methods[J]. Journal of Frontiers of Computer Science and Technology, 2018, 12(7): 6-20. https://www.cnki.com.cn/Article/CJFDTOTAL-KXTS201807002.htm