双注意力门融合网络的水下图像增强方法

刘毅; 田生伟

doi:10.13568/j.cnki.651094.651316.2021.11.02.0003

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双注意力门融合网络的水下图像增强方法

信息科学与技术 | 更新时间：2026-01-21

- 双注意力门融合网络的水下图像增强方法
- 双注意力门融合网络的水下图像增强方法
- 新疆大学学报（自然科学版中英文） 2022年39卷第6期页码：696-706
- 作者机构：
  
  1. 新疆大学软件学院
  2. 新疆大学软件工程技术重点实验室
- 作者简介：
- 基金信息：
  
  委托项目水下特定目标实时自主识别项目（2019750001）;新疆维吾尔自治区重点研发项目（2021B01002）
- DOI：10.13568/j.cnki.651094.651316.2021.11.02.0003
  中图分类号： TP391.41
- 纸质出版：2022
- 稿件说明：
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[1]刘毅,田生伟.双注意力门融合网络的水下图像增强方法[J].新疆大学学报(自然科学版)(中英文),2022,39(06):696-706.
[1]刘毅,田生伟.双注意力门融合网络的水下图像增强方法[J].新疆大学学报(自然科学版)(中英文),2022,39(06):696-706. DOI： 10.13568/j.cnki.651094.651316.2021.11.02.0003.

DOI：10.13568/j.cnki.651094.651316.2021.11.02.0003.

摘要

针对水下图像存在的对比度下降、细节丢失、颜色失真、全局色彩偏移等问题，提出了一种双注意力门融合网络的水下图像增强方法．该方法采用加入了空间注意力机制的U型网络处理输入图像来生成去除噪声且突出特征细节的置信度图，并利用加入了通道注意力机制的卷积神经网络优化图像特征得到有效纠正了色彩偏移的特征图；最后将置信度图与特征图融合实现图像增强．在合成数据集和真实水下图像数据集上的实验结果表明：与现有方法相比，该方法取得了更优的水下图像增强效果且具有更好的泛化能力．

Abstract

An underwater image enhancement method that based on double attention mechanism and gate fusion network is proposed to overcome the problems of local detail loss

low image contrast

color cast and global color deviation in underwater image. In this method

the U-structure network with spatial attention mechanism is used to process the input image to generate a confidence map that removed noise and highlighting feature details

and the convolution neural network with channel attention mechanism is used to refine image features to generate a feature map that effectively correct color cast. Finally

the confidence map and the feature map are fused to achieve image enhancement. The experimental results on the synthetic dataset and the real underwater image dataset show that compared with the existing methods

the method proposed in this paper has better underwater image enhancement performance and generalization ability.

关键词

Keywords

references

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