DRHA-UIE：基于双重残差混合注意力模块的水下图像增强方法

王欣; 石慧

doi:10.12263/DZXB.20211619

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DRHA-UIE：基于双重残差混合注意力模块的水下图像增强方法

学术论文 | 更新时间：2025-12-08

- DRHA-UIE：基于双重残差混合注意力模块的水下图像增强方法
- DRHA-UIE: An Underwater Image Enhancement Method Based on Dual Residual Hybrid Attention Block
- 电子学报 2023年51卷第9期页码：2398-2407
- 作者机构：
  
  吉林大学计算机科学与技术学院，吉林长春 130000
- 作者简介：
  
  [ "王欣女，1975年9月出生于吉林省白山市.现为吉林大学计算机科学与技术学院副教授、硕士生导师.在国内外发表学术论文40余篇. E-mail: w_x@jlu.edu.cn" ]
  [ "石慧女，1997年1月出生于山东省德州市.现为吉林大学计算机科学与技术学院硕士.主要研究方向为数字图像处理.E-mail: shihui19@mails.jlu.edu.cn" ]
- 基金信息：
  
  吉林省教育厅科学技术研究项目(JJKH20211100KJ)
- DOI：10.12263/DZXB.20211619
  中图分类号： TP391
- 收稿：2021-12-03，
  
  修回：2022-04-09，
  
  纸质出版：2023-09-25
- 稿件说明：
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王欣,石慧.DRHA-UIE：基于双重残差混合注意力模块的水下图像增强方法[J].电子学报,2023,51(09):2398-2407.

WANG Xin,SHI Hui.DRHA-UIE: An Underwater Image Enhancement Method Based on Dual Residual Hybrid Attention Block[J].ACTA ELECTRONICA SINICA,2023,51(09):2398-2407.
王欣,石慧.DRHA-UIE：基于双重残差混合注意力模块的水下图像增强方法[J].电子学报,2023,51(09):2398-2407. DOI： 10.12263/DZXB.20211619.

WANG Xin,SHI Hui.DRHA-UIE: An Underwater Image Enhancement Method Based on Dual Residual Hybrid Attention Block[J].ACTA ELECTRONICA SINICA,2023,51(09):2398-2407. DOI： 10.12263/DZXB.20211619.

摘要

在水下环境中，悬浮的颗粒会对光造成散射和波长相关的衰减，使水下图像呈现出颜色失真、对比度低等问题．针对上述问题，本文提出一种基于双重残差混合注意力模块的水下图像增强方法（Dual Residual Hybrid Attention Underwater Image Enhancement method，DRHA-UIE）．该方法采用改进的双重残差块完成特征学习，并通过在双重残差块中引入混合注意力模块，沿通道和空间两个维度对特征进行注意力权重推断，以捕获显著特征．本文设计结合像素级损失、结构相似性损失和内容感知损失的联合特征损失函数，以获得具有更精细纹理的增强图像．此外，本文应用水下图像形成模型（Image Formation Model，IFM）对水下图像基准数据集（Underwater Image Enhancement Benchmark dataset，UIEB）进行优化，获得了具有更高视觉质量的N-UIEB（New-UIEB）数据集，实验表明，相较于UIEB数据集，基于N-UIEB训练得到的增强图像具有更自然的颜色和更清晰的细节．为验证本文方法的有效性，将本文方法与主流的10种方法进行测试和比较，结果表明，DRHA-UIE方法有效提高了图像对比度并恢复了图像颜色，在与水下图像增强算法的定量比较中获得了最优的性能．

Abstract

In seawater

light suffers from scattering and wavelength-related attenuation

which makes underwater images exhibit color distortion and low contrast. In this paper

we propose a robust method called dual residual hybrid attention underwater image enhancement method (DRHA-UIE). The proposed method uses an improved dual residual block for features learning. To capture the significant features

a hybrid attention mechanism is introduced in the dual residual block

which infers the attention weights on the features along the channel and spatial dimensions. To train the proposed method end-to-end

a joint feature loss function that consists of pixel-level loss

structural similarity loss

and content-aware loss is designed

then enhanced images with finer textures are obtained. In addition

a revised underwater image formation model (IFM) is applied to optimize the underwater image enhancement benchmark dataset (UIEB)

and a dataset with higher visual quality

new-underwater image enhancement benchmark dataset (N-UIEB)

is obtained. Experiments show that the enhanced images trained by N-UIEB have more natural colors and sharper details than those trained by the UIEB dataset. The proposed model is extensively evaluated on the above two datasets. Results show that the DRHA-UIE method effectively enhances the image quality subjectively and outperforms the other 10 state-of-the-art methods in quantitative comparisons.

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references

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