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基于多域属性表征解耦的水下图像无监督可控增强

周世健 朱鹏莅 陈瀚 刘厶源

周世健, 朱鹏莅, 陈瀚, 等. 基于多域属性表征解耦的水下图像无监督可控增强[J]. 水下无人系统学报, 2024, 32(6): 1-11 doi: 10.11993/j.issn.2096-3920.2023-0165
引用本文: 周世健, 朱鹏莅, 陈瀚, 等. 基于多域属性表征解耦的水下图像无监督可控增强[J]. 水下无人系统学报, 2024, 32(6): 1-11 doi: 10.11993/j.issn.2096-3920.2023-0165
ZHOU Shijian, ZHU Pengli, CHEN Han, LIU Siyuan. Unsupervised Controllable Enhancement of Underwater Images Based on Multi-Attribute Representation Disentanglement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0165
Citation: ZHOU Shijian, ZHU Pengli, CHEN Han, LIU Siyuan. Unsupervised Controllable Enhancement of Underwater Images Based on Multi-Attribute Representation Disentanglement[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2023-0165

基于多域属性表征解耦的水下图像无监督可控增强

doi: 10.11993/j.issn.2096-3920.2023-0165
基金项目: 国家自然科学基金项目资助(62301107).
详细信息
    作者简介:

    刘厶源(1990-), 男, 博士, 教授, 主要研究方向为水下智能感知与建模、机器学习

  • 中图分类号: TP391.4; TJ630

Unsupervised Controllable Enhancement of Underwater Images Based on Multi-Attribute Representation Disentanglement

  • 摘要: 水下图像无监督增强技术多面向特定失真因素, 对于水下多类失真图像适应性略显不足; 图像的内容属性(结构)会随风格属性(外观)迁移变化, 导致增强效果不受控, 影响后续环境感知处理的稳定性和准确性。针对这一问题, 文中提出一种基于多域表征解耦的水下图像无监督可控增强方法。首先设计了多域统一的表征解耦循环一致对抗变换框架, 提高了算法对多失真因素的适应性; 其次构建了双编码-条件解码网络结构; 最后设计了多域属性表征解耦的系列损失, 提高了质量、内容、风格等属性表征的独立表达性和可控性。实验结果表明, 所提算法不仅可以消除水下图像的色差、模糊、噪声和低光照等多类失真, 还可通过线性插值的方式量化图像风格码对水下图像进行可控增强。

     

  • 图  1  MARD 双编码-条件解码网络结构

    Figure  1.  Bi-encoder conditional-decoder network architecture

    图  2  多属性表征解耦损失

    Figure  2.  Multi-attribute Representation Disentanglement Loss

    图  3  单种失真类型水下图像增强效果

    Figure  3.  The enhancement effect diagram of single-distorted underwater image

    图  4  不同方法对真实水下图像增强效果的定性比较

    Figure  4.  Qualitative comparison of authentic underwater images using different methods

    图  5  水下图像可控增强效果图

    Figure  5.  Controllable enhanced effect of underwater image

    图  6  消融实验定性结果对比图

    Figure  6.  Qualitative comparison results of ablation experiment

    表  1  不同方法对真实水下图像增强效果的定量比较

    Table  1.   Quantitative comparison of MARD on authentic underwater images

    方法UICMUIConMUIQMUCIQEFDUM
    原图3.62240.26252.53403.91420.4109
    IBLA6.48380.18122.16235.51390.6325
    RGHS5.29570.29073.15113.54980.5450
    UNTV5.39400.26602.33295.13420.7444
    HLRP7.30820.24232.55125.94610.6860
    CycleGAN3.44470.29343.05453.51040.4416
    UGAN4.58030.30123.13574.10500.5558
    Water-Net4.63800.30533.11174.16740.4678
    UWCNN6.29460.14072.16275.43480.5818
    FUnIE-GAN5.20130.29893.23225.08360.5693
    UWCNN-SD5.30610.28123.16824.61630.6730
    MARD6.49660.30803.48245.60650.7912
    下载: 导出CSV

    表  2  不同方法测试不同分辨率图像所需运行时间比较

    Table  2.   Comparison of runtime required to test images of various resolutions by different methods

    分辨率(ppi)UNTV(s)ULAP(s)Water-Net(s)Ucolor(s)MARD(s)
    256×2563.5420.3641.0367.7930.059
    640×48010.2911.7082.54529.1920.254
    800×60016.2672.7043.45942.7600.469
    1280×72027.0484.6644.630183.8500.791
    1920×108059.64210.53810.793261.4181.728
    下载: 导出CSV

    表  3  不同损失下MARD的定量比较

    Table  3.   Quantitative comparison using different losses

    方法UICMUIConMUIQMUCIQEFDUM
    M1 ($ {L_{\rm{adv}}} $)5.3 8850.2 4803.0 1524.3 6910.5 439
    M2 (M1 &$ {L_{{\text{cyc}}}} $)6.1 8660.2 7823.2 3835.0 1220.6 502
    M3 (M2 &$ {L_{\rm{cont}}} $)6.1 8110.2 9483.1 4394.5 6230.6 504
    M4 (M3 &$ {L_{\rm{sty}}} $$ {L_{\rm{sty}}} $)6.3 0420.2 9443.3 2904.6 4930.6 958
    M5 (M4 &$ {L_{\rm{rec}}} $)6.3 4270.2 9313.4 2534.4 5860.7 674
    M6 (M5 &$ {L_{\rm{KL}}} $)6.4 9660.3 0803.4 8245.6 0650.7 912
    下载: 导出CSV
  • [1] Song W, Wang Y, Huang D M, et al. Enhancement of underwater images with statistical model of background light and optimization of transmission map[J]. IEEE Transactions on Broadcasting, 2020, 66(1): 153-169. doi: 10.1109/TBC.2019.2960942
    [2] Xie J, Hou G J, Wang G D, et al. A variational framework for underwater image dehazing and deblurring[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 32(6): 3514-3526.
    [3] McGlamery B L. A computer model for underwater camera systems[J]. Proceedings of the Society of Photo-0ptical Instrumentation Engineers, 1980, 208: 221-231.
    [4] Zhuang P X, Wu J M, Porikli F, et al. Underwater image enhancement with hyper-laplacian reflectance priors[J]. IEEE Transactions on Image Processing, 2022, 31: 5442-5455. doi: 10.1109/TIP.2022.3196546
    [5] Zhou J C, Pang L, Zhang D H, et al. Underwater image enhancement method via multi-interval subhistogram perspective equalization[J]. IEEE Journal of Oceanic Engineering, 2023, 48(2): 474-488. doi: 10.1109/JOE.2022.3223733
    [6] Zhuang P X, Li C Y, Wu J M. Bayesian retinex underwater image enhancement[J]. Engineering Applications of Artificial Intelligence, 2021, 101: 104171. doi: 10.1016/j.engappai.2021.104171
    [7] Zhuang P X, Ding X H. Underwater image enhancement using an edge-preserving filtering retinex algorithm[J]. Multimedia Tools and Applications, 2020, 79: 17257-17277. doi: 10.1007/s11042-019-08404-4
    [8] Fu X Y, Zhuang P X, Huang Y, et al. A retinex-based enhancing approach for single underwater image[C]//2014 IEEE international conference on image processing (ICIP). Paris, France: IEEE, 2014: 4572-4576.
    [9] Li C Y, Guo C L, Ren W Q, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2019, 29: 4376-4389.
    [10] Qi Q, Li K Q, Zheng H Y, et al. SGUIE-Net: Semantic attention guided underwater image enhancement with multi-scale perception[J]. IEEE Transactions on Image Processing, 2022, 31: 6816-6830. doi: 10.1109/TIP.2022.3216208
    [11] Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets[C]//28th Conference on Neural Information Processing Systems (NIPS), advances in neural information processing systems, Montreal, Canada: NIPS, 2014, 27: 2672-2680.
    [12] 胡雨航, 赵磊, 李恒等. 多特征选择与双向残差融合的无监督水下图像增强[J]. 电子测量与仪器学报, 2023, 37(9): 190-202.

    Hu Yuhang, Zhao Lei, Li Heng, et al. Unsupervised underwater image enhancement with multi-feature selection and bidirectional residual fusion[J]. Journal of Electronics Measurement & Instrumentation, 2023, 37(9): 190-202.
    [13] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy: IEEE, 2017: 2242-2251
    [14] 刘彦呈, 董张伟, 朱鹏莅等. 基于特征解耦的无监督水下图像增强[J]. 电子与信息学报, 2022, 44(10): 3389-3398. doi: 10.11999/JEIT211517

    Liu Yancheng, Dong Zhangwei, Zhu Pengli, Liu Siyuan. Unsupervised underwater image enhancement based on feature disentanglement[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3389-3398. doi: 10.11999/JEIT211517
    [15] Yu X M, Ying Z Q, Li T M, et al. Multi-mapping image-to-image translation with central biasing normalization[J/OL]. arXiv preprint(2018-04-17)https://www.arxiv.org/abs/1806.10050v5.
    [16] Zhu P L, Liu Y C, Xu M Y, et al. Unsupervised multiple representation disentanglement framework for improved underwater visual perception[J]. IEEE Journal of Oceanic Engineering, 2023.
    [17] Marques T P, Albu A B. L2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, USA: IEEE, , 2020: 2286-2295.
    [18] Peng Y T, 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
    [19] Huang D, Wang Y, Song W, et al. Shallow-water image enhancement using relative global histogram stretching based on adaptive parameter acquisition[C]//2018 MultiMedia Modeling: 24th International Conference. Bangkok, Thailand: MMIC, 2018: 453-465.
    [20] Song W, Wang Y, Huang D M, et al. A rapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration[J]. Advances in Multimedia Information Processing-PCM, 2018, 11164: 678-688.
    [21] Fabbri C, Islam M J, Sattar J. Enhancing underwater imagery using generative adversarial networks[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, Australia: IEEE, 2018: 7159-7165.
    [22] Li C, Anwar S, 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
    [23] Islam M J, Xia Y Y, Sattar J. Fast underwater image enhancement for improved visual perception[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 3227-3234. doi: 10.1109/LRA.2020.2974710
    [24] Wu S C, Luo T, Jiang G Y, 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
    [25] Li C Y, Anwar S, Hou J H, et al. Underwater image enhancement via medium transmission-guided multi-color space embedding[J]. IEEE Transactions on Image Processing, 2021, 30: 4985-5000. doi: 10.1109/TIP.2021.3076367
    [26] Yang M, Sowmya A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015, 24(12): 6062-6071. doi: 10.1109/TIP.2015.2491020
    [27] Panetta K, Gao C, Agaian S. Human-visual-system-inspired underwater image quality measures[J]. IEEE Journal of Oceanic Engineering, 2015, 41(3): 541-551.
    [28] Yang N, Zhong Q H, Li K, 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
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出版历程
  • 收稿日期:  2023-12-18
  • 修回日期:  2024-02-06
  • 录用日期:  2024-02-07
  • 网络出版日期:  2024-03-18

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