Multi-scale Image Fusion Based on Adaptive Weighting
-
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。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.
-
Key words:
- infrared and visible light /
- image fusion /
- electric equipment /
- multi-sensor
-
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 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↓ -
[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.htmWANG 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/hwjs202004010LI 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.htmCHEN 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.htmWANG 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:0253-2239.2007.09.013MIAO 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:0372-2112.2004.05.011CHAO 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.1001-8891.2006.07.014TAO 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.htmZHANG 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.htmYANG 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