多尺度加權(quán)Retinex變壓器油下圖像增強(qiáng)

強(qiáng)虎; 鐘羽中; 佃松宜

doi:10.11999/JEIT240645

多尺度加權(quán)Retinex變壓器油下圖像增強(qiáng)

doi: 10.11999/JEIT240645

1.
四川大學(xué)電氣工程學(xué)院成都 610065
2.
四川大學(xué)深地工程智能建造與健康運(yùn)維全國重點(diǎn)實(shí)驗(yàn)室成都 610065

基金項(xiàng)目: 國家自然科學(xué)基金(62203314)

詳細(xì)信息

作者簡介:
強(qiáng)虎：男，博士生，研究方向?yàn)槿斯ぶ悄?、?jì)算機(jī)視覺

鐘羽中：女，副教授，研究方向?yàn)橛?jì)算機(jī)視覺、圖像處理

佃松宜：男，教授，研究方向?yàn)橄冗M(jìn)控制、感知與人工智能

通訊作者:
佃松宜　scudiansy@scu.edu.cn

中圖分類號(hào): TN911.73; TP391.41
計(jì)量
- 文章訪問數(shù): 121
- HTML全文瀏覽量: 50
- PDF下載量: 23
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-07-23
- 修回日期: 2024-11-08
- 網(wǎng)絡(luò)出版日期: 2024-11-13
- 刊出日期: 2025-01-31

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

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)

摘要

摘要: 針對(duì)變壓器油下圖像存在顏色失真、亮度低和細(xì)節(jié)失真問題，該文提出一種多尺度加權(quán)Retinex變壓器油下圖像增強(qiáng)算法。首先，為了緩解變壓器油下圖像顏色失真問題，提出一種混合動(dòng)態(tài)顏色通道補(bǔ)償算法，根據(jù)拍攝圖像各通道的衰減狀態(tài)對(duì)衰減通道進(jìn)行動(dòng)態(tài)補(bǔ)償。然后，為了解決細(xì)節(jié)失真問題，提出一種銳化權(quán)重加權(quán)策略。最后，該文創(chuàng)新性采用金字塔多尺度融合策略對(duì)不同尺度Retinex反射分量和相應(yīng)權(quán)重圖進(jìn)行加權(quán)融合得到變壓器油下清晰圖像。實(shí)驗(yàn)結(jié)果表明所提算法可以有效解決變壓器油下圖像復(fù)雜退化問題。
- 變壓器油下圖像增強(qiáng) /
- Retinex /
- 通道補(bǔ)償 /
- 多尺度加權(quán)
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

HTML全文

圖 1 算法流程圖

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圖 2 不同尺度Retinex反射分量圖

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圖 3 不同實(shí)驗(yàn)場景

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圖 4 不同場景下采集的變壓器油下圖像

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圖 5 不同算法對(duì)圖4(a)增強(qiáng)結(jié)果

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圖 6 不同算法對(duì)圖4(b)增強(qiáng)結(jié)果

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圖 7 不同算法對(duì)圖4(c)增強(qiáng)結(jié)果

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圖 8 不同子模塊對(duì)變壓器油下圖像增強(qiáng)效果

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圖 9 所提算法對(duì)水下退化圖像增強(qiáng)效果

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1 混合動(dòng)態(tài)顏色通道補(bǔ)償

輸入：相機(jī)拍攝圖像${I_{{\text{in}}}}$，增益系數(shù)$\omega $
輸出：補(bǔ)償后的圖像${I_{{\text{out}}}}$
(1) $B,G,R \leftarrow {\text{split}}({I_{{\text{in}}}})$
(2) ${I_{{\text{Max}}}} \leftarrow \max (R,G,B)$
(3) ${I_{{\text{Min}}}} \leftarrow \min (R,G,B)$
(4) if ${I_{{\text{Max}}}} = \bar R$
(5) 　if ${I_{{\text{Min}}}} = \bar G$ then
(6) 　　計(jì)算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根據(jù)$ {V}_{\mathrm{min}}=G $, 　　　　${V_{{\text{med}}}} = B,{V_{\max }} = R$
(7) 　end if
(8) 　if ${I_{{\text{Min}}}} = \bar B$ then
(9) 　　計(jì)算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根據(jù)${V_{\min }} = B$, ${V_{{\text{med}}}} = G $, 　　　　${V_{\max }} = R$
(10) end if
(11) end if
(12) if ${I_{{\text{Max}}}} = \bar B$
(13) if ${I_{{\text{Min}}}} = \bar R$ then
(14) 　計(jì)算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根據(jù)${V_{\min }} = R$, ${V_{{\text{med}}}} = G $, 　　　　 ${V_{\max }} = B$
(15) end if
(16) if ${I_{{\text{Min}}}} = \bar G$ then
(17) 　計(jì)算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根據(jù)${V_{\min }} = G$, ${V_{{\text{med}}}} = R $, 　　　　${V_{\max }} = B$
(18) end if
(19) end if
(20) if ${I_{{\text{Max}}}} = \bar G$
(21) if ${I_{{\text{Min}}}} = \bar R$ then
(22) 　計(jì)算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根據(jù)${V_{\min }} = R$, ${V_{{\text{med}}}} = B $, 　　　　${V_{\max }} = G$
(23) end if
(24) if ${I_{{\text{Min}}}} = \bar B$ then
(25) 　計(jì)算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根據(jù)${V_{\min }} = B$, ${V_{{\text{med}}}} = R $, 　　　　${V_{\max }} = G$
(26) end if
(27) end if
(28) ${I_{{\text{out}}}} \leftarrow {\text{merge}}(\bar B,\bar G,\bar R)$
(29) return ${I_{{\text{out}}}}$

下載: 導(dǎo)出CSV

表 1 UIQM, FUDM和NIQE無參考圖像質(zhì)量評(píng)估結(jié)果

指標(biāo)	方法
指標(biāo)	原圖	UCM	UDCP	IBLA	ULAP	Water-Net	Shallow-UWnet	UDnet	本文
UIQM	1.476	1.943	1.417	2.144	1.379	2.272	1.273	1.880	3.265
FDUM	0.184	0.224	0.229	0.298	0.294	0.249	0.187	0.183	0.379
NIQE	5.021	4.864	5.315	5.714	4.815	4.859	5.240	4.754	4.681

下載: 導(dǎo)出CSV

表 2 不同模塊消融實(shí)驗(yàn)

HCC	DW	PF	UIQM	FDUM	NIQE
			1.476	0.184	5.021
√			1.508	0.206	4.982
		√	2.965	0.283	4.630
	√	√	3.112	0.343	4.501
√	√	√	3.265	0.379	4.681

下載: 導(dǎo)出CSV

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