基于GNR先验的电力设备热成像超分辨率方法

刘云峰; 赵洪山; 杨晋彪; 韩晋锋; 刘秉聪

基于GNR先验的电力设备热成像超分辨率方法

1.
国网晋城供电公司，山西晋城 048000
2.
华北电力大学（保定）电力工程系，河北保定 071000

基金项目:

国网山西电力公司科技项目配电设备智能传感与态势感知运维技术研究 921010025

详细信息

作者简介:
刘云峰（1978-），男，高级工程师，主要研究方向为电力系统自动化。E-mail：50044024@qq.com

中图分类号: TM41
计量
- 文章访问数: 118
- HTML全文浏览量: 39
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2021-06-08
- 修回日期: 2021-08-03
- 刊出日期: 2023-01-20

Super Resolution Method for Power Equipment Infrared Imaging Based on Gradient Norm-ratio Prior

1.
State Grid Jincheng Power Supply Company, Jincheng 048000, China
2.
Department of Power Engineering, North China Electric Power University (Baoding), Baoding 071000, China

摘要

摘要: 电力设备红外图像在电力设备状态监测、故障识别等方面发挥着重要作用。针对红外图像应用时存在的分辨率低，清晰度不足的问题，本文提出一种基于图像梯度范数比（Gradient Norm-ratio, GNR）先验约束的压缩感知电力设备红外图像超分辨率方法。通过分析电力设备红外图像在不同采样比时重建图像高频信息的变化规律，将GNR先验引入传统压缩感知超分辨率模型中。并针对改进后的模型设计了有效的求解算法，通过半二次分裂方法引入辅助变量，对不同变量交替迭代求解，实现红外图像超分辨率重建。仿真实验结果验证了GNR先验信息的引入，有利于超分辨率算法取得更好的重建效果。与现有经典超分辨率方法相比，本文方法重建图像无论在主观视觉效果还是客观评价指标上都有了较好的提升。
- 电力设备 /
- 红外图像 /
- 压缩感知 /
- 超分辨率 /
- 梯度范数比
Abstract: Infrared images play an important role in the condition monitoring and fault identification of power equipment. Aiming at solving the problems of low resolution and low definition in the application of infrared images, this paper proposes a super-resolution method for compressed infrared images of sensing power equipment based on the prior constraint of the image gradient ratio (GNR). The GNR prior was introduced into the traditional compressed sensing super-resolution model by analyzing the variation in high-frequency information of power equipment infrared images at different sampling ratios. An effective algorithm was designed to solve the improved model. By introducing auxiliary variables into the semi-quadratic splitting method, different variables were iteratively and alternately solved to realize the super-resolution reconstruction of infrared images. The simulation results show that the introduction of GNR prior information was conducive to the super-resolution algorithm achieving better reconstruction. Compared with existing classical super-resolution methods, the proposed method improves both the subjective visual effect and objective evaluation index.
- electrical equipment /
- infrared image /
- compressed sensing /
- super resolution /
- gradient norm-ratio

HTML全文

图 1 采用不同约束项时重建图像高频信息成本变化图

Figure 1. Reconstruction of high frequency information cost chart of image with different constraints

下载: 全尺寸图片幻灯片

图 2 求解算法流程图

Figure 2. Flow chart of solving algorithm

下载: 全尺寸图片幻灯片

图 3 人工下采样红外图像不同方法重建结果

Figure 3. Reconstruction results of artificial down sampling infrared image by different methods

下载: 全尺寸图片幻灯片

图 4 1号图像不同方法重建结果

Figure 4. Reconstruction results of No.1 image by different methods

下载: 全尺寸图片幻灯片

图 5 2号图像不同方法重建结果

Figure 5. Reconstruction results of No.2 image by different methods

下载: 全尺寸图片幻灯片

图 6 不同大小图像重建所需时间

Figure 6. The time required to reconstruct images of different sizes

下载: 全尺寸图片幻灯片

表 1 不同方法重建图像PSNR评价指标对比

Table 1. Comparison of PSNR evaluation indexes of reconstructed images by different methods

Image number	Zhang's	Original CS	Yang's	Li's	Ours
1	23.0657	24.9632	26.5846	27.2059	29.3659
2	22.5691	24.6251	25.9631	26.3620	29.0325
3	23.6245	24.3219	26.4263	26.3494	30.0316
4	22.9521	23.6594	25.6845	25.2631	28.9613
5	22.1694	24.2351	26.4975	26.1279	30.1034
6	23.1784	23.9865	27.3249	26.6947	28.9674
7	22.5728	24.6937	26.3592	27.0691	29.3461
8	21.6891	23.4967	26.6481	27.9612	28.6541
9	22.7816	25.1435	26.2597	26.6729	30.5319
10	23.0684	24.3167	26.9437	27.1636	28.6755

下载: 导出CSV

表 2 不同方法重建图像SSIM评价指标对比

Table 2. Comparison of SSIM evaluation indexes of reconstructed images by different methods

Image number	Zhang’s	Original CS	Yang’s	Li’s	Ours
1	0.7934	0.8635	0.8966	0.9103	0.9731
2	0.7349	0.8526	0.8791	0.8955	0.9436
3	0.8172	0.8678	0.9249	0.9027	0.9348
4	0.7393	0.8211	0.8842	0.8931	0.9274
5	0.7304	0.8197	0.8991	0.8769	0.9533
6	0.7681	0.7987	0.9014	0.8643	0.9264
7	0.7311	0.8235	0.8624	0.8971	0.9083
8	0.7129	0.8433	0.8742	0.8892	0.9151
9	0.7486	0.8730	0.8785	0.8797	0.9762
10	0.7519	0.8261	0.8834	0.9031	0.9216

下载: 导出CSV

表 3 不同方法重建图像AG评价指标对比

Table 3. Comparison of AG evaluation indexes of reconstructed images by different methods

Image number	Zhang’s	Original CS	Yang’s	Li’s	Ours
1	20.6496	20.3521	29.1152	28.4946	31.0694
2	18.6232	24.7993	32.6134	30.6779	33.9552
3	20.5298	23.6657	31.5780	32.6144	34.3967
4	22.3863	22.6349	30.3440	29.4893	31.3995
5	19.8414	23.3463	27.4514	24.9311	31.3776
6	17.6890	21.1064	28.9477	31.3425	33.1015
7	18.6451	25.6108	32.6944	32.4871	36.2160
8	22.3637	24.4892	30.9645	28.5638	34.2556
9	20.0684	25.6946	31.9865	30.9783	36.9943
10	19.6452	20.3521	29.1152	28.4946	31.0694

下载: 导出CSV

表 4 不同方法重建图像IE评价指标对比

Table 4. Comparison of IE evaluation indexes of reconstructed images by different methods

Image number	Zhang’s	Original CS	Yang’s	Li’s	Ours
1	5.8113	6.0168	6.3153	6.2154	6.5312
2	5.7256	5.8364	6.0215	5.9342	6.1249
3	6.2316	6.1546	6.2599	6.5644	6.7291
4	5.9233	5.9487	6.1547	6.3263	6.3478
5	6.0532	6.1306	6.1658	6.2431	6.4528
6	5.625	5.8314	5.8947	6.1456	6.1569
7	5.8449	6.1125	6.0387	6.2831	6.5937
8	5.7052	5.8841	6.2436	6.1463	6.2649
9	6.1242	5.8543	6.1488	6.1395	6.1531
10	5.9439	6.0337	6.3894	6.2343	6.5013

下载: 导出CSV

参考文献(31)

[1]	王毅, 陈启鑫, 张宁, 等. 5G通信与泛在电力物联网的融合: 应用分析与研究展望[J]. 电网技术, 2019, 43(5): 1575-1585. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201905012.htm WANG Yi, CHEN Qixin, ZHANG Ning, et al. Fusion of the 5G communication and the ubiquitous electric internet of things: application analysis and research prospects[J]. Power System Technology, 2019, 43(5): 1575-1585. https://www.cnki.com.cn/Article/CJFDTOTAL-DWJS201905012.htm
[2]	李鹏, 毕建刚, 于浩, 等. 变电设备智能传感与状态感知技术及应用[J]. 高电压技术, 2020, 46(9): 3097-3113. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202009013.htm LI Peng, BI Jiangang, YU Hao, et al. Technology and application of intelligent sensing and state sensing for transformation equipment[J]. High Voltage Engineering, 2020, 46(9): 3097-3113. https://www.cnki.com.cn/Article/CJFDTOTAL-GDYJ202009013.htm
[3]	梁兴, 严居斌, 尹磊. 基于红外图像的输电线路故障识别[J]. 电测与仪表, 2019, 56(24): 99-103. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ201924016.htm LIANG Xing, YAN Jubin, YIN Lei. Fault identification of transmission lines based on infrared image[J]. Electrical Measurement & Instrumentation, 2019, 56(24): 99-103. https://www.cnki.com.cn/Article/CJFDTOTAL-DCYQ201924016.htm
[4]	赵仕策, 赵洪山, 寿佩瑶. 智能电力设备关键技术及运维探讨[J]. 电力系统自动化, 2020, 44(20): 1-10. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT202020001.htm ZHAO Shice, ZHAO Hongshan, SHOU Peiyao. Discussion on key technology and operation & maintenance of intelligent power equipment[J]. Automation of Electric Power Systems, 2020, 44(20): 1-10. https://www.cnki.com.cn/Article/CJFDTOTAL-DLXT202020001.htm
[5]	Lozanov Y, Tzvetkova S. A methodology for processing of thermographic images for diagnostics of electrical equipment[C]//2019 11th Electrical Engineering Faculty Conference (BulEF). IEEE, 2019: 1-4.
[6]	LI Y, ZHAO K, REN F, et al. Research on super-resolution image reconstruction based on low-resolution infrared sensor[J]. IEEE Access, 2020, 8: 69186-69199. doi: 10.1109/ACCESS.2020.2984945
[7]	唐艳秋, 潘泓, 朱亚平, 等. 图像超分辨率重建研究综述[J]. 电子学报, 2020, 48(7): 1407-1420. doi: 10.3969/j.issn.0372-2112.2020.07.022 TANG Yanqiu, PAN Hong, ZHU Yaping, et al. A survey of image super-resolution reconstruction[J]. Acta Electronica Sinica, 2020, 48(7): 1407-1420. doi: 10.3969/j.issn.0372-2112.2020.07.022
[8]	Bätz M, Eichenseer A, Kaup A. Multi-image super-resolution using a dual weighting scheme based on Voronoi tessellation[C]//2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016: 2822-2826.
[9]	WANG L, LIN Z, DENG X, et al. Multi-frame image super-resolution with fast upscaling technique[J/OL]. arXiv preprint arXiv: 1706.06266, 2017.
[10]	Rasti P, Demirel H, Anbarjafari G. Improved iterative back projection for video super-resolution[C]//2014 22nd Signal Processing and Com-munications Applications Conference (SIU). IEEE, 2014: 552-555.
[11]	杨欣, 费树岷, 周大可. 基于MAP的自适应图像配准及超分辨率重建[J]. 仪器仪表学报, 2011, 32(8): 1771-1775. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201108014.htm YANG Xin, FEI Shumin, ZHOU Dake. Self-adapting weighted technology for simultaneous image registration and super-resolution reconstruction based on MAP[J]. Chinese Journal of Scientific Instrument, 2011, 32(8): 1771-1775. https://www.cnki.com.cn/Article/CJFDTOTAL-YQXB201108014.htm
[12]	Kato T, Hino H, Murata N. Multi-frame image super resolution based on sparse coding[J]. Neural Networks, 2015, 66: 64-78.
[13]	Greaves A, Winter H. Multi-frame video super-resolution using convolutional neural networks[J]. IEEE Transactions on Multimedia, 2017, 8(3): 76-85.
[14]	Hayat K. Multimedia super-resolution via deep learning: a survey[J]. Digital Signal Processing, 2018, 81: 198-217.
[15]	DONG W, ZHANG L, Lukac R, et al. Sparse representation-based image interpolation with nonlocal autoregressive modeling[J]. IEEE Transactions on Image Processing, 2013, 22(4): 1382-1394.
[16]	WANG L, WU H, PAN C. Fast image upsampling via the displacement field[J]. IEEE Transactions on Image Processing, 2014, 23(12): 5123-5135.
[17]	ZHANG Y, FAN Q, BAO F, et al. Single-image super-resolution based on rational fractal interpolation[J]. IEEE Transactions on Image Processing, 2018, 27(8): 3782-3797.
[18]	Manjón J V, Coupé P, Buades A, et al. Non-local MRI upsampling[J]. Medical Image Analysis, 2010, 14(6): 784-792.
[19]	ZHANG X, Burger M, Bresson X, et al. Bregmanized nonlocal regularization for deconvolution and sparse reconstruction[J]. SIAM Journal on Imaging Sciences, 2010, 3(3): 253-276.
[20]	LI L, XIE Y, HU W, et al. Single image super-resolution using combined total variation regularization by split Bregman Iteration[J]. Neurocomputing, 2014, 142: 551-560.
[21]	Rasti P, Demirel H, Anbarjafari G. Improved iterative back projection for video super-resolution[C]//2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014: 552-555.
[22]	YANG J, Wright J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873.
[23]	WANG X, YU K, DONG C, et al. Recovering realistic texture in image super-resolution by deep spatial feature transform[C]//Proceedings of The IEEE Conference on Computer Vision and Pattern Recognition, 2018: 606-615.
[24]	Zoph B, Le Q V. Neural architecture search with reinforcement learning[J/OL]. arXiv preprint arXiv: 1611.01578, 2016.
[25]	Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[26]	Candes E, Romberg J. Sparsity and incoherence in compressive sampling[J]. Inverse Problems, 2007, 23(3): 969.
[27]	Sulam J, Ophir B, Zibulevsky M, et al. Trainlets: Dictionary learning in high dimensions[J/OL]. arXiv preprint arXiv: 1602.00212, 2016.
[28]	ZHANG H, Hager W W. A nonmonotone line search technique and its application to unconstrained optimization[J]. SIAM Journal on Opti-mization, 2004, 14(4): 1043-1056.
[29]	JIN J, YUAN J, SHEN Q, et al. Curvelet transform based adaptive image deblocking method[J]. Computers & Electrical Engineering, 2014, 40(8): 117-129.
[30]	Chengbo L. An efficient algorithm for total variation regularization with applications to the single pixel camera and compressive sensing[D]. Houston: Department of Computational and Applied Mathematics, 2009.
[31]	中华人民共和国国家质量监督检验检疫总局. 工业检测型红外热像仪[S]. GB/T 19870-200, [2021-06-08]. General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China. Industrial Inspecting Thermal Imagers[S]. GB/T 19870-200, [2021-06-08].