Home > Archive>Volume 19, Issue 4, 2023 >248-251. DOI:https://doi.org/10.1007/s11801-023-2168-0
PDF HTML XML Export Cite reminder
Unsupervised model-driven neural network based image denoising for transmission line monitoring
CSTR:
[cstr]
Author:
Affiliation:

1. Research Institute, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China;2. State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China[ This work has been supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co., Ltd: Research on Early Warning Technology of Overhead Transmission Channel External Invasion Risk Based on Layered Calculation (No.J2021064).

  • Article
    • | |
  • Metrics
    • |
  • Reference [24]
    • |
  • Related [20]
    • | | |
  • Comments
    Abstract:

    With the expansion of smart grid and Internet of things (IoT) technology, edge computing has a wide variety of applications in these domains. The criteria for real-time monitoring and accuracy are particularly high in the field of online real-time monitoring of electricity lines. Based on edge technology, high-quality real-time monitoring can be performed for transmission lines using image processing techniques. Therefore, we propose an image denoising method, which can learn clean images using a stream-based generative model. The stream model uses a two-stage approach in the network to handle the different training periods of denoising separately. Experimental results show that the proposed method has good denoising performance.

    Reference
    [1] KRESTINSKAYA O, JAMES A P, CHUA L O. Neuromemristive circuits for edge computing：a review[J]. IEEE transactions on neural networks and learning systems, 2019, 31(1)：4-23.
    [2] SHI W, CAO J, ZHANG Q, et al. Edge computing：vision and challenges[J]. IEEE internet of things journal, 2016, 3(5)：637-646.
    [3] CHEN S, WEN H, WU J, et al. Internet of things based smart grids supported by intelligent edge computing[J]. IEEE access, 2019, 7：74089-74102.
    [4] MEHMOOD M Y, OAD A, ABRAR M, et al. Edge computing for IoT-enabled smart grid[J]. Security and communication networks, 2021.
    [5] SAMIE F, BAUER L, HENKEL J. Edge computing for smart grid：an overview on architectures and solutions[J]. IoT for smart grids, 2019：21-42.
    [6] HUANG Y, LU Y, WANG F, et al. An edge computing framework for real-time monitoring in smart grid[C]//2018 IEEE International Conference on Industrial Internet (ICII), October 21-23, 2018, Seattle, WA, USA. New York：IEEE, 2018：99-108.
    [7] LI H, DONG Y, YIN C, et al. A real-time monitoring and warning system for power grids based on edge computing[J]. Mathematical problems in engineering, 2022, 2022：8719227.
    [8] KHODAYAR M, LIU G, WANG J, et al. Deep learning in power systems research：a review[J]. CSEE journal of power and energy systems, 2020, 7(2)：209-220.
    [9] LI B, XIAO C, WANG L, et al. Dense nested attention network for infrared small target detection[EB/OL]. (2021-06-01) [2022-09-24]. https：//arxiv.org/abs/
    2106.00487.
    [10] HAN M, LIU H. Retraction note：super-resolution restoration of degraded image based on fuzzy enhance-
    ment[J]. Arabian journal of geosciences, 2022, 15(9)：1-1.
    [11] ZHANG K, ZUO W, GU S, et al. Learning deep CNN denoiser prior for image restoration[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York：IEEE, 2017：3929-3938.
    [12] LIU W, LEE J. A 3-D atrous convolution neural network for hyperspectral image denoising[J]. IEEE transactions on geoscience and remote sensing, 2019, 57(8)：5701-5715.
    [13] LIU L, OUYANG W, WANG X, et al. Deep learning for generic object detection：a survey[J]. International journal of computer vision, 2020, 128(2)：261-318.
    [14] CALVARONS A F. Improved noise2noise denoising with limited data[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 20-25, 2021, Nashville, TN, USA. New York：IEEE, 202：796-805.
    [15] KRULL A, BUCHHOLZ T O, JUG F. Noise2void-learning denoising from single noisy images[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 16-22, 2019, Long Beach, USA. New York：IEEE, 2019：2129-2137.
    [16] ULYANOV D, VEDALDI A, LEMPITSKY V. Deep image prior[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York：IEEE, 2018：9446-9454.
    [17] ONISHI Y, HASHIMOTO F, OTE K, et al. Anatomical-guided attention enhances unsupervised PET image denoising performance[J]. Medical image analysis, 2021, 74：102226.
    [18] AGCAOGLU O, SILVA R F, CALHOUN V. Multimodal fusion of brain imaging data with joint non-linear independent component analysis[C]//2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), June 26-29, 2022, Nafplio, Greece. New York：IEEE, 2022：1-5.
    [19] CAI Z, XIONG Z, XU H, et al. Generative adversarial networks：a survey toward private and secure applications[J]. ACM computing surveys, 2021, 54(6)：1-38.
    [20] KINGMA D P, DHARIWAL P. Glow：generative flow with invertible 1×1 convolutions[EB/OL]. (2018-07-09) [2022-09-24]. https：//arxiv.org/abs/1807.03039.
    [21] KINGMA D P, BA J. Adam：a method for stochastic optimization[J]. Computer Science, 2014.
    [22] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York：IEEE, 2016：770-778.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

YAO Nan, WANG Zhen, ZHANG Jun, ZHU Xueqiong, XUE Hai. Unsupervised model-driven neural network based image denoising for transmission line monitoring[J]. Optoelectronics Letters,2023,19(4):248-251

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:October 09,2022
  • Revised:November 26,2022
  • Online: April 19,2023
Article QR Code
Phone：86-22-60214471
URL：http://www.oelett.com E-mail：oel@263.net
Supported by:Beijing E-Tiller Technology Development Co., Ltd.
® 2025 All Rights Reserved