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多尺度加权Retinex变压器油下图像增强

强虎 钟羽中 佃松宜

强虎, 钟羽中, 佃松宜. 多尺度加权Retinex变压器油下图像增强[J]. 电子与信息学报. doi: 10.11999/JEIT240645
引用本文: 强虎, 钟羽中, 佃松宜. 多尺度加权Retinex变压器油下图像增强[J]. 电子与信息学报. doi: 10.11999/JEIT240645
QIANG Hu, ZHONG Yuzhong, DIAN Songyi. Image Enhancement under Transformer Oil Based on Multi-Scale Weighted Retinex[J]. Journal of Electronics & Information Technology. 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. doi: 10.11999/JEIT240645

多尺度加权Retinex变压器油下图像增强

doi: 10.11999/JEIT240645
基金项目: 国家自然科学基金(62203314)
详细信息
    作者简介:

    强虎:男,博士生,研究方向为人工智能、计算机视觉

    钟羽中:女,副教授,研究方向为计算机视觉、图像处理

    佃松宜:男,教授,研究方向为先进控制、感知与人工智能

    通讯作者:

    佃松宜 scudiansy@scu.edu.cn

  • 中图分类号: TN911.73; TP391.41

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

Funds: The National Natural Science Foundation of China (62203314)
  • 摘要: 针对变压器油下图像存在颜色失真、亮度低和细节失真问题,该文提出一种多尺度加权Retinex变压器油下图像增强算法。首先,为了缓解变压器油下图像颜色失真问题,提出一种混合动态颜色通道补偿算法,根据拍摄图像各通道的衰减状态对衰减通道进行动态补偿。然后,为了解决细节失真问题,提出一种锐化权重加权策略。最后,该文创新性采用金字塔多尺度融合策略对不同尺度Retinex反射分量和相应权重图进行加权融合得到变压器油下清晰图像。实验结果表明所提算法可以有效解决变压器油下图像复杂退化问题。
  • 图  1  算法流程图

    图  2  不同尺度Retinex反射分量图

    图  3  不同实验场景

    图  4  不同场景下采集的变压器油下图像

    图  5  不同算法对图4(a)增强结果

    图  6  不同算法对图4(b)增强结果

    图  7  不同算法对图4(c)增强结果

    图  8  不同子模块对变压器油下图像增强效果

    图  9  所提算法对水下退化图像增强效果

    1  混合动态颜色通道补偿

     输入:相机拍摄图像${I_{{\text{in}}}}$,增益系数$\omega $
     输出:补偿后的图像${I_{{\text{out}}}}$
     (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)   计算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根据$ {V}_{\mathrm{min}}=G $,
        ${V_{{\text{med}}}} = B,{V_{\max }} = R$
     (7)  end if
     (8)  if ${I_{{\text{Min}}}} = \bar B$ then
     (9)   计算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根据${V_{\min }} = B$, ${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)  计算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根据${V_{\min }} = R$, ${V_{{\text{med}}}} = G $,
         ${V_{\max }} = B$
     (15) end if
     (16) if ${I_{{\text{Min}}}} = \bar G$ then
     (17)  计算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根据${V_{\min }} = G$, ${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)  计算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根据${V_{\min }} = R$, ${V_{{\text{med}}}} = B $,
        ${V_{\max }} = G$
     (23) end if
     (24) if ${I_{{\text{Min}}}} = \bar B$ then
     (25)  计算${V_{{\text{com\_min}}}},{V_{{\text{com\_med}}}}$根据${V_{\min }} = B$, ${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 ${I_{{\text{out}}}}$
    下载: 导出CSV

    表  1  UIQM, FUDM和NIQE无参考图像质量评估结果

    指标方法
    原图UCMUDCPIBLAULAPWater-NetShallow-UWnetUDnet本文
    UIQM1.4761.9431.4172.1441.3792.2721.2731.8803.265
    FDUM0.1840.2240.2290.2980.2940.2490.1870.1830.379
    NIQE5.0214.8645.3155.7144.8154.8595.2404.7544.681
    下载: 导出CSV

    表  2  不同模块消融实验

    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
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-07-23
  • 修回日期:  2024-11-08
  • 网络出版日期:  2024-11-13

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