WANG Yan, ZHANG Jinfeng, WANG Likang, FAN Xianghui. Underwater Image Enhancement Based on Attention Mechanism and Feature Reconstruction[J]. Infrared Technology , 2024, 46(9): 1006-1014.
Citation: WANG Yan, ZHANG Jinfeng, WANG Likang, FAN Xianghui. Underwater Image Enhancement Based on Attention Mechanism and Feature Reconstruction[J]. Infrared Technology , 2024, 46(9): 1006-1014.

Underwater Image Enhancement Based on Attention Mechanism and Feature Reconstruction

More Information
  • Received Date: June 05, 2023
  • Revised Date: August 29, 2023
  • To address the issues of existing underwater image enhancement methods, which lack focus on critical target objects in images and exhibit poor enhancement effects on edge detail information, in this study, an underwater image enhancement approach is proposed based on an attention mechanism and feature reconstruction. First, a superpixel image enhancement model is constructed by integrating the residual module with the Convolutional Block Attention Module (CBAM), which not only improves the overall quality of underwater images but also enhances the clarity and visibility of target objects in images. Second, an edge difference module is designed to enable the model to focus on high-frequency information in the images, thereby strengthening the edge details of the target objects. Finally, a multi-granularity feature reconstruction module is built to reconstruct the hidden layer features of the superpixel image enhancement model, restore the input image, and further optimize the model parameters. Experimental results demonstrate that when compared with contrastive methods, the proposed model realizes improvements in three evaluation metrics: Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), and Underwater Image Quality Measures (UIQM), indicating better enhancement performance. Notably, it exhibits a remarkable effect in enhancing critical target objects in underwater images.

  • [1]
    LIU K, PENG L, TANG S. Underwater object detection using TC-YOLO with attention mechanisms[J]. Sensors, 2023, 23(5): 2567. DOI: 10.3390/s23052567
    [2]
    LIU Z, ZHUANG Y, JIA P, et al. A novel underwater image enhancement algorithm and an improved underwater biological detection pipeline[J]. Journal of Marine Science and Engineering, 2022, 10(9): 1204. DOI: 10.3390/jmse10091204
    [3]
    邓晶. 水下图像增强方法综述[J]. 电视技术, 2022, 46(12): 4-7, 13.

    DENG Jing. A review of underwater image enhancement methods[J]. Television Technology, 2022, 46(12): 4-7, 13.
    [4]
    Alenezi F, Armghan A, Santosh K. Underwater image dehazing using global color features[J]. Engineering Applications of Artificial Intelligence, 2022, 116: 105489. DOI: 10.1016/j.engappai.2022.105489
    [5]
    ZHENG M, LUO W. Underwater image enhancement using improved CNN based defogging[J]. Electronics, 2022, 11(1): 150. DOI: 10.3390/electronics11010150
    [6]
    ZHOU J, ZHANG D, ZHANG W. Underwater image enhancement method via multi-feature prior fusion[J]. Applied Intelligence, 2022, 52(14): 16435-16457. DOI: 10.1007/s10489-022-03275-z
    [7]
    Ghani A S A, Isa N A M. Underwater image quality enhancement through integrated color model with Rayleigh distribution[J]. Applied Soft Computing, 2015, 27: 219-230. DOI: 10.1016/j.asoc.2014.11.020
    [8]
    Ancuti C, Ancuti C O, Haber T, et al. Enhancing underwater images and videos by fusion[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2012: 81-88.
    [9]
    王亚茹, 刘雨青, 黄璐瑶, 等. 基于卷积神经网络的水下图像增强技术研究[J]. 制造业自动化, 2023, 45(2): 65-68, 92.

    WANG Yaru, LIU Yuqing, HUANG Luyao, et al. Research on underwater image enhancement technology based on convolutional neural network[J]. Manufacturing Automation, 2023, 45(2): 65-68, 92.
    [10]
    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
    [11]
    LI C, Anwar S, Hou J, 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
    [12]
    GONG T Y, ZHANG M M, ZHOU Y, et al. Underwater image enhancement based on color feature fusion[J]. Electronics, 2023, 12(24): 4999. DOI: 10.3390/electronics12244999
    [13]
    LIU R, JIANG Z, YANG S, et al. Twin adversarial contrastive learning for underwater image enhancement and beyond[J]. IEEE Transactions on Image Processing, 2022, 31: 4922-4936. DOI: 10.1109/TIP.2022.3190209
    [14]
    QI Q, LI K, ZHENG H, 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
    [15]
    SUN S, WANG H, ZHANG H, et al. Underwater image enhancement with reinforcement learning[J]. IEEE Journal of Oceanic Engineering, 2024, 49(1): 249-261. DOI: 10.1109/JOE.2022.3152519
    [16]
    PENG L, ZHU C, BIAN L. U-Shape transformer for underwater image enhancement[J]. IEEE Transactions on Image Processing, 2023, 32: 3066-3079. DOI: 10.1109/TIP.2023.3276332
    [17]
    Avcibas I, Sankur B, Sayood K. Statistical evaluation of image quality measures[J]. J. Electron. Imag., 2002, 11(2): 206-223. DOI: 10.1117/1.1455011
    [18]
    WANG Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans. Image Process. , 2004, 13(4): 600-612. DOI: 10.1109/TIP.2003.819861
    [19]
    Panetta K, GAO C, 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
    [20]
    Islam M J, XIA 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
    [21]
    LI C, GUO C, REN W, 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
    [22]
    Drews P L, Nascimento E R, Botelho S S, et al. Underwater depth estimation and image restoration based on single images[J]. IEEE Comput. Graph. Appl. , 2016, 36(2): 24-35. DOI: 10.1109/MCG.2016.26
    [23]
    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
    [24]
    SONG W, WANG Y, HUANG D M, et al. Arapid scene depth estimation model based on underwater light attenuation prior for underwater image restoration[C]//Proceedings of 2018 Advances in Multimedia Information Processing, 2018: 678-688.
    [25]
    Zuiderveld K J. Contrast Limited Adaptive Histogram Equalization[C]//Proceedings of the Graphics Gems, 1994: 474-485.
    [26]
    LIU S, FAN H, LIN S, et al. Adaptive learning attention network for underwater image enhancemen [J]. IEEE Rob Autom Lett., 2022, 7(2): 5326-5333. DOI: 10.1109/LRA.2022.3156176

Catalog

    Article views (53) PDF downloads (21) Cited by()
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return