Image Enhancement under Transformer Oil Based on Multi-Scale Weighted Retinex

QIANG Hu; ZHONG Yuzhong; DIAN Songyi

doi:10.11999/JEIT240645

Volume 47 Issue 1

Jan. 2025

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(1): 223-232

QIANG Hu, ZHONG Yuzhong, DIAN Songyi. Image Enhancement under Transformer Oil Based on Multi-Scale Weighted Retinex[J]. Journal of Electronics & Information Technology, 2025, 47(1): 223-232. doi: 10.11999/JEIT240645

Citation:

QIANG Hu, ZHONG Yuzhong, DIAN Songyi. Image Enhancement under Transformer Oil Based on Multi-Scale Weighted Retinex[J]. Journal of Electronics & Information Technology, 2025, 47(1): 223-232. doi: 10.11999/JEIT240645

Citation:

PDF( 9622 KB)

Image Enhancement under Transformer Oil Based on Multi-Scale Weighted Retinex

doi: 10.11999/JEIT240645

1.
College of Electrical Engineering, Sichuan University, Chengdu 610065, China
2.
State Key Laboratory of Intelligent Construction and Healthy Operation and Maintenance of Deep Underground Engineering, Sichuan University, Chengdu 610065, China

Funds: The National Natural Science Foundation of China (62203314)

Received Date: 2024-07-23
Rev Recd Date: 2024-11-08

Available Online: 2024-11-13

Publish Date: 2025-01-31

Abstract

Abstract

Objective: Large oil-immersed transformers are critical in power systems, with their operational status essential for maintaining grid stability and reliability. Periodic inspections are necessary to identify and resolve transformer faults and ensure normal operation. However, manual inspections require significant human and material resources. Moreover, conventional inspection methods often fail to promptly detect or accurately locate internal faults, which may ultimately affect transformer lifespan. Robots equipped with visual systems can replace manual inspections for fault identification inside oil-immersed transformers, enabling timely fault detection and expanding the inspection range compared to manual methods. However, high-definition visual imaging is crucial for effective fault detection using robots. Transformer oil degrades and discolors under high-temperature, high-pressure conditions, with these effects varying over time. The oil color typically shifts from pale yellow to reddish-brown, and the types and forms of suspended particles evolve dynamically. These factors cause complex light attenuation and scattering, leading to color distortion and detail loss in captured images. Additionally, the sealed metallic structure of oil-immersed transformers requires robots to rely on onboard artificial light sources during inspections. The limited illumination from these sources further reduces image brightness, hindering clarity and impacting fault detection accuracy. To address issues such as color distortion, low brightness, and detail loss in images captured under transformer oil, this paper proposes a multi-scale weighted Retinex algorithm for image enhancement. Methods: This paper proposes a multi-scale weighted Retinex algorithm for image enhancement under transformer oil. To mitigate color distortion, a hybrid dynamic color channel compensation algorithm is proposed, which dynamically adjusts compensation based on the attenuation of each channel in the captured image. To address detail loss, a sharpening weight strategy is applied. Finally, a pyramid multi-scale fusion strategy integrates Retinex reflection components from multiple scales with their corresponding weight maps, producing clearer images under transformer oil. Results and Discussions: Qualitative experimental results (Fig. 5, Fig. 6, Fig. 7) indicate that the UCM algorithm, based on non-physical models, achieves color correction by assuming minimal attenuation in the blue channel. However, the dynamic changes in transformer oil result in varying channels with the least attenuation, reducing the algorithm’s generalization capability. Enhancement results from physical-model algorithms, including UDCP, IBLA, and ULAP, exhibited low brightness, often leading to the loss of critical image details. Furthermore, these physical-model methods not only fail to resolve color distortion but frequently intensify it. Deep learning-based algorithms, such as Water-Net, Shallow-uwnet, and UDnet, demonstrated effectiveness in mitigating mild color distortion. However, their enhancement results still suffer from low brightness and blurred details. In contrast, the algorithm proposed in this paper fully accounts for the dynamic characteristics of transformer oil, effectively addressing color distortion, blurring, and detail loss in images captured under transformer oil. Quantitative experiments (Table 1) show that the UIQM value of images enhanced by the proposed algorithm increased by an average of 121.206% compared with the original images, the FDUM value increased by an average of 105.978%, and the NIQE value decreased by an average of 6.772%. Both qualitative and quantitative results demonstrate that the proposed algorithm effectively resolves image degradation issues under transformer oil and outperforms the comparison methods. Additionally, applicability tests reveal that the algorithm not only performs well for transformer oil images but also demonstrates strong enhancement capabilities in underwater imaging. Conclusions: Experimental results demonstrate that the algorithm proposed in this paper effectively addresses the complex degradation issues in images captured under transformer oil. Although the proposed algorithm achieves superior enhancement performance, processing a 1 280×720 resolution image requires an average of 2.16 s, which does not meet the demands for embedded real-time applications, such as robotic inspections. Future research will focus on optimizing the algorithm to improve its real-time performance.
- Image enhancement under transformer oil,
- Retinex,
- Channel compensation,
- Multi-scale weighted

FullText(HTML)

References(26)

References

[1]	JHA M and BHANDARI A K. CBLA: Color balanced locally adjustable underwater image enhancement[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 5020911. doi: 10.1109/TIM.2024.3396850.
[2]	ZHANG Dehuan, WU Chenyu, ZHOU Jingchun, et al. Robust underwater image enhancement with cascaded multi-level sub-networks and triple attention mechanism[J]. Neural Networks, 2024, 169: 685–697. doi: 10.1016/j.neunet.2023.11.008.
[3]	YANG H Y, CHEN Peiyin, HUANG C C, et al. Low complexity underwater image enhancement based on dark channel prior[C]. 2011 Second International Conference on Innovations in Bio-inspired Computing and Applications, Shenzhen, China, 2011: 17–20. doi: 10.1109/IBICA.2011.9.
[4]	QIANG Hu, ZHONG Yuzhong, ZHU Yuqi, et al. Underwater image enhancement based on multichannel adaptive compensation[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 5014810. doi: 10.1109/TIM.2024.3378290.
[5]	DREWS JR P, DO NASCIMENTO E, MORAES F, et al. Transmission estimation in underwater single images[C]. 2013 IEEE International Conference on Computer Vision Workshops, Sydney, Australia, 2013: 825–830. doi: 10.1109/ICCVW.2013.113.
[6]	SONG Wei, WANG Yan, HUANG Dongmei, et al. A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration[C]. Proceedings of the 19th Pacific-Rim Conference on Multimedia on Advances in Multimedia Information Processing – PCM 2018, Hefei, China, 2018: 678–688. doi: 10.1007/978-3-030-00776-8_62.
[7]	ZHANG Song, ZHAO Shili, AN Dong, et al. LiteEnhanceNet: A lightweight network for real-time single underwater image enhancement[J]. Expert Systems with Applications, 2024, 240: 122546. doi: 10.1016/j.eswa.2023.122546.
[8]	WANG Zhengyong, SHEN Liquan, XU Mai, et al. Domain adaptation for underwater image enhancement[J]. IEEE Transactions on Image Processing, 2023, 32: 1442–1457. doi: 10.1109/TIP.2023.3244647.
[9]	米泽田, 晋洁, 李圆圆, 等. 基于多尺度级联网络的水下图像增强方法[J]. 电子与信息学报, 2022, 44(10): 3353–3362. doi: 10.11999/JEIT220375. MI Zetian, JIN Jie, LI Yuanyuan, et al. Underwater image enhancement method based on multi-scale cascade network[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3353–3362. doi: 10.11999/JEIT220375.
[10]	LI Chongyi, ANWAR S, and PORIKLI F. Underwater scene prior inspired deep underwater image and video enhancement[J]. Pattern Recognition, 2020, 98: 107038. doi: 10.1016/j.patcog.2019.107038.
[11]	RAO Yuan, LIU Wenjie, LI Kunqian, et al. Deep color compensation for generalized underwater image enhancement[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2024, 34(4): 2577–2590. doi: 10.1109/TCSVT.2023.3305777.
[12]	WANG Keyan, HU Yan, CHEN Jun, et al. Underwater image restoration based on a parallel convolutional neural network[J]. Remote Sensing, 2019, 11(13): 1591. doi: 10.3390/rs11131591.
[13]	WU Shengcong, LUO Ting, JIANG Gangyi, et al. A two-stage underwater enhancement network based on structure decomposition and characteristics of underwater imaging[J]. IEEE Journal of Oceanic Engineering, 2021, 46(4): 1213–1227. doi: 10.1109/JOE.2021.3064093.
[14]	BUCHSBAUM G. A spatial processor model for object colour perception[J]. Journal of the Franklin Institute, 1980, 310(1): 1–26. doi: 10.1016/0016-0032(80)90058-7.
[15]	LAND E H and MCCANN J J. Lightness and retinex theory[J]. Journal of the Optical Society of America, 1971, 61(1): 1–11. doi: 10.1364/JOSA.61.000001.
[16]	JOBSON D J, RAHMAN Z, and WOODELL G A. Properties and performance of a center/surround retinex[J]. IEEE Transactions on Image Processing, 1997, 6(3): 451–462. doi: 10.1109/83.557356.
[17]	RAHMAN Z, JOBSON D J, and WOODELL G A. Multi-scale retinex for color image enhancement[C]. The 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland, 1996: 1003–1006. doi: 10.1109/ICIP.1996.560995.
[18]	PANETTA K, GAO Chen, and AGAIAN S. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 2016, 41(3): 541–551. doi: 10.1109/JOE.2015.2469915.
[19]	YANG Ning, ZHONG Qihang, LI Kun, et al. A reference-free underwater image quality assessment metric in frequency domain[J]. Signal Processing: Image Communication, 2021, 94: 116218. doi: 10.1016/j.image.2021.116218.
[20]	MITTAL A, SOUNDARARAJAN R, and BOVIK A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209–212. doi: 10.1109/LSP.2012.2227726.
[21]	IQBAL K, ODETAYO M, JAMES A, et al. Enhancing the low quality images using unsupervised colour correction method[C]. 2010 IEEE International Conference on Systems, Man and Cybernetics, Istanbul, Turkey, 2010: 1703–1709. doi: 10.1109/ICSMC.2010.5642311.
[22]	PENG Y T and COSMAN P C. Underwater image restoration based on image blurriness and light absorption[J]. IEEE Transactions on Image Processing, 2017, 26(4): 1579–1594. doi: 10.1109/TIP.2017.2663846.
[23]	LI Chongyi, GUO Chunle, REN Wenqi, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2020, 29: 4376–4389. doi: 10.1109/TIP.2019.2955241.
[24]	NAIK A, SWARNAKAR A, and MITTAL K. Shallow-UWnet: Compressed model for underwater image enhancement (student abstract)[C]. The Thirty-Fifth AAAI Conference on Artificial Intelligence, Palo Alto, USA, 2021: 15853–15854. doi: 10.1609/aaai.v35i18.17923.
[25]	SALEH A, SHEAVES M, JERRY D, et al. Adaptive uncertainty distribution in deep learning for unsupervised underwater image enhancement[J]. arXiv preprint arXiv: 2212.08983, 2022.
[26]	LIU Risheng, FAN Xin, ZHU Ming, et al. Real-world underwater enhancement: Challenges, benchmarks, and solutions under natural light[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(12): 4861–4875. doi: 10.1109/TCSVT.2019.2963772.