基于SK注意力残差网络的水下图像增强
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作者单位:

1.南京信息工程大学 自动化学院;2.南京信息工程大学江苏省大气环境与装备技术协同创新中心

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TP399

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国家自然科学基金(61302189)


Underwater image enhancement based on SK attention residual network
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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

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    摘要:

    针对水下图像颜色失真、关键信息模糊和细节特征丢失的问题,提出一种基于SK注意力残差网络的水下图像增强方法。改进生成对抗网络中的生成器结构,引入残差模块,减少编码器和解码器之间的特征丢失,增强了图像细节和颜色。为了使网络能适应不同尺度的特征图提取图像关键信息,在残差模块后添加SK注意力机制。同时,采用参数修正线性单元来提高网络的拟合能力。将本文方法分别在真实和合成的水下图像数据集中进行验证,采用传统方法和深度学习的方法进行主客观评价。在主观效果分析中发现,本文方法增强后的图像颜色、关键信息和细节特征方面都有很大提升。在客观评价指标中发现,本文方法指标值均高于现有的水下图像增强算法,表明该算法的有效性。

    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.

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历史
  • 收稿日期:2022-06-21
  • 最后修改日期:2022-07-08
  • 录用日期:2022-07-22
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