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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
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    [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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