Super Resolution Method for Power Equipment Infrared Imaging Based on Gradient Norm-ratio Prior
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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。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.
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Key words:
- electrical equipment /
- infrared image /
- compressed sensing /
- super resolution /
- gradient norm-ratio
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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 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 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
numberZhang’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 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
numberZhang’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 -
[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.htmWANG 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.htmLI 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.htmLIANG 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.htmZHAO 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.0372-2112.2020.07.022TANG 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.htmYANG 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].