Underwater image enhancement based on SK attention residual network
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Affiliation:

1.School of Automation,Nanjing University of Information Science Technology,Nan Jing;2.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology CICAEET,Nanjing University of Information Science Technology,Nanjing

Clc Number:

TP399

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    Abstract:

    In order to solve the problems of color distortion, key information blur and detail loss of underwater image, an underwater image enhancement method based on SK attention residual network is proposed. The generator structure in the generative adversarial network is improved, and residual module is introduced to reduce the feature loss between the encoder and decoder, and enhance the image detail and color. In order to make the network adapt to different scale feature maps to extract key information of images, SK attention mechanism is added after the residual module. At the same time,a parametric rectified linear unit is used to improve the fitting ability of the network. This method is verified in real and synthetic underwater image datasets, and the traditional method and deep learning method are used for subjective and objective evaluation. In the subjective effect analysis, it is found that the color, key information and detail features of the enhanced image have been greatly improved. In the objective evaluation index, it is found that the index values of this method are higher than the existing underwater image enhancement algorithms, which shows the effectiveness of this algorithm.

    Reference
    [1] Reference:
    [2] [1] Honnutagi P,Mytri V D, Lalitha Y S. Optimized Weight Maps and Fusion for Underwat er Image Enhancem ent[C].International Conference on Electrical, Electronics, Commun ication,Computer,and Optimization Tech niques (ICEECCOT). IEEE,2018.
    [3] [2] Jian M,Liu X,Luo H,et al.Underwater image processing and analysis:A review[J]. Signal Processing Image Communication, 2021,91(5):1160088.
    [4] [3] D.M.Kocak.F.R.Dalgeish,F.M.Caimi,and Y.Y.Schechner.A Focus on Recent Developments and Trends in Underwater Imaging[J].Marine Technology Society Journal 2008, 42(1):52-67.
    [5] [4] Wang Y, Song Wei, Fortino G, et al.An experimental-based review of image and image restoration methods for underwater imaging[J]. IEEE access, 2019,7:14023 3-140251.
    [6] [5] Liu Y C,Chen W H,Chen Y Q.Automatic white balance for digital still camera[J].IEEE Transactions on Consumer Electronics,1995,41(3):460-466 .
    [7] [6] Buchsbaum.G. A spatial processor model for object color perception[J]. Journal of the Fr anklin institute, 1980,310(1):1-26.
    [8] [7] Iqbal K,Odetayo M O,James A E, et al.Enhancing the low quality images using Unsuper vised Colour Correction Method[C]//IEEE,2010:1703-1709.
    [9] [8] Hitam M S,Awalludin E A,Yussof W, et al.Mixture contrast Limited adaptive histogram equalization for underwater image enhancement[C]//International Conference on Compu ter Applications Technology IEEE, 2013:1-5.
    [10] [9] Hu H F,Zhao L,Li X,et al. Underwater image recovery under the nonuniform optical field based on polarimetric imaging[J]. IEEE Photonics Joural, 2018, 10(1):1-1.
    [11] [10] 郭继昌,李重仪,郭春乐,等.水下图像增强和复原方法研究进展[J].中国图像学报,2017,22(3):273-287.
    [12] Guo J C,Li C Y,Guo C L,et al.Research progress of underwater image enhancement and restoration methods [J].Journal of Image and Graphics, 2017, 22(3):273-287.
    [13] [11] Acharya T,Ray AK,Gallagher. Image processing:principles and applications[J]. Journal of Electronic Imaging,2006, 15(3): 9901.
    [14] [12] He K M,Sun J,Tang X. Single Image Haze Removal Using Dark Channel Prior[J]. IEEE transactions on pattern analysis and machine intelligence, 2010,33(12): 2341-2353.
    [15] [13] 胡易,邹立,昝世良,等.基于暗通道和伽马变换的水下图像增强[J].电光与控制,2021,28(3):81-85.
    [16] Hu Y,Zuo L,Zan S L,et al. Underwater image enhancement based on dark channel and Gamma transform[J]. Electronic Optics and Control,2021,28(3):81-85.
    [17] [14] Cong Runmin, Zhang Yumo,Zhang Chen, et al. Research Progress of Deep Learning Driven Underwater Image Enhancement and Restoration[J]. Journal of Signal Processin, 2020,36(9):1377-1389.
    [18] [15] Anwar S,Li C,Porikli F. Deep underwater image enhancement[J]. arXiv: 1807.03528, 2018.
    [19] [16] Liu.P, Wang.G,Qi.H,et al.Underwater image enhancement with a deep residual framewor k[J]. IEEE Access,2019,7: 94 614-94 629.
    [20] [17] Goodfellow I J, Pouget-Abadie J, Mirza M,et al.Generative Adversarial Networks[J]. Ad vances in Neural Information Processing Systems,2014,27:2672-2680.
    [21] [18] Islam M J,Xia Y Sattar J. Fast Underwater Image Enhancement for Improved Visual Per ception[J]. IEEE Robotics and Automation Letters,2020,5(2):3227-3234.
    [22] [19] Fabbri C, Islam M J. Enhancing underwater imagery using generative adversarial networ ks[C]//2018 IEEE International Conference on Robtics and Automation (ICRA).IEEE, 2018:7159-7165.
    [23] [20] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]// International Conference on Medical image computing and computer- assisted intervention,2015:234-241.
    [24] [21] Chen Y,Dai X,Liu M,et al. Dynamic ReLU[J]. Computer Vision and Pattern Recognition. arXiv:2003.10027, 2020.
    [25] [22] Johnson J,Alahi A,Fei-Fei L. Perceptual Losses for Real-Time Style Transfer and Super Resolution[J]. 2016.
    [26] [23] Kingma D P,Ba J. Adam:a method for stochastic optimization[J]. Computer Science, 2014.
    [27] [24] Drews P L J,Nascimento E R,Botelho S S C,et al. Underwater Depth estimation and image restoration based on single images[J]. IEEE computer graphics and application, 2016,36(2):24-35.
    [28] [25] Peng Y T,Cosman P C. Underwater Image Restoration Based on Image Blurrings and Light Absorption[J]. IEEE Transactions on Image Processing, 2017,26 (4):1579-1594.
    [29] [26] 陈学磊,张品,权令伟,等. 融合深度学习与成像模型的水下图像增强算法[J].计算机工程,2022, 48(02): 243-249.
    [30] Chen X,Quan L,Yi C,et al. Underwater Image Enhancement based on Deeping Learning and Image
    [31] Formation Model[J]. Computer Engineering,2022,48(02):243-249.
    [32] [27] Li C,Guo J,Guo C. Emerging from water:Underwater image color correction based on weakly supervised col or transfer[J]. IEEE Signal processing letters, 2018,25(3):323-327.
    [33] [28] Zhou W, Bovik A C, Sheikh H R, et al. Image qualty assesment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13 (4):600-612.
    [34] [29] Panetta K,Gao C,Agaian S. Human-visual-system-inspired-underwater image quality measures[J]. IEEE Jour nal of Oceanic Engineering, 2015,41(3), 541-551.
    [35] [30] Yang M,Sowmya A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015,24(12), 6062-6071.
    [36] [31] Ghani A. Underwater image quality enhancement through Integrated color model with Rayleigh distribution [J]. Applied Soft Computing, 2014,27:219-230.
    [37] [32] Canny J. A computational approach to edge detection[J]. IEEE Transactions on pattern analysis and machine intelligence. 1986(6):679-698.
    [38] [33] Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision,2004,60(2):91-110.
    [39] Underwater image enhancement based on SK attention residual path network
    [40] CHEN Haixiu1,2 LIU Lei1
    [41] 1 School of Automation,Nanjing University of Information Science Technology,Nan Jing 210044;
    [42] 2 Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science Technology, Nanjing, 210044
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History
  • Received:June 21,2022
  • Revised:July 08,2022
  • Adopted:July 22,2022
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