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

您当前的位置：

首页 >

文章列表页 >

双注意力门融合网络的水下图像增强方法

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
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 39, Issue 6, Pages: 696-706(2022)
- 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：
- DOI：10.13568/j.cnki.651094.651316.2021.11.02.0003
  CLC： TP391.41
- Published：2022
- 稿件说明：
移动端阅览
[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

GHANI A, ISA M. Underwater image quality enhancement through integrated color model with Rayleigh distribution[J]. Applied Soft Computing, 2014, 27:219-230.

FU X, FAN Z, MEI L, et al. Two-step approach for single underwater image enhancement[C]//2017 International Symposium on Intelligent Signal Processing and Communication Systems(ISPACS). New York:IEEE, 2017.

ZHANG S, WANG T, DONG J, et al. Underwater image enhancement via extended multi-scale retinex[J]. Neurocomputing, 2017,245(5):1-9.

HE K, SUN J, TANG X. Single image haze removal using dark channel prior[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. New York:IEEE, 2009.

WANG Y, LIU H, CHAU L P. Single underwater image restoration using adaptive attenuation-curve prior[J]. IEEE Transactions on Circuits&Systems I Regular Papers, 2018, 65(3):992-1002.

HOU M, LIU R, FAN X, et al. Joint residual learning for underwater image enhancement[C]//2018 25th IEEE International Conference on Image Processing(ICIP). New York:IEEE, 2018.

LU H M, WANG D, LI Y J, et al. CONet:a cognitive ocean network[J]. IEEE Wireless Communications, 2019, 26(3):90-96.

LI J, SKINNER K A, EUSTICE R M, et al. WaterGAN:unsupervised generative network to enable real-time color correction of monocular underwater images[J]. IEEE Robotics and Automation Letters, 2018, 3(1):387-394.

LI C Y, GUO J C, REN W Q, et al. An underwater image enhancement benchmark dataset and beyond[J]. IEEE Transactions on Image Processing, 2020, 29:4376-4389.

HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). New York:IEEE, 2016.

JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks[C]//Proceedings of the 28th International Conference on Neural Information Processing Systems-Volume 2. Cambridge:MIT Press, 2015.

JIE H, LI S, GANG S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2020, 42(8):2011-2023.

ZHOU Y, YAN K, LI X. Underwater image enhancement via physical-feedback adversarial transfer learning[J]. IEEE Journal of Oceanic Engineering, 2022, 47(1):76-87.

MCGLAMERY B L. A computer model for underwater camera systems[J]. Ocean Optics VI, 1980, 208(208):221-232.

AKKAYNAK D, TREIBITZ T. Sea-Thru:a method for removing water from underwater images[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). New York:IEEE, 2019.

PENG Y T, COSMAN P C. Underwater image restoration based on image blurriness and light absorption[J]. IEEE Transactions on Image Processing, 2017, 26(4):1579-1594.

DREWS J P, NASCIMENTO E, MORAES F, et al. Transmission estimation in underwater single images[C]//2013 IEEE International Conference on Computer Vision Workshops. New York:IEEE, 2013.

IQBAL M, RIAZ M M, ALI S S, et al. Underwater image enhancement using Laplace decomposition[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19:1500105.

BRAINARD D H, WANDELL B A. Analysis of the retinex theory of color vision[J]. Journal of the Optical Society of America A, 1986, 3(10):1651-1661.

JOBSON D J, RAHMAN Z, WOODELL G A. A multiscale retinex for bridging the gap between color images and the human observation of scenes[J]. IEEE Transactions on Image Processing, 1997, 6(7):965-976.

刘柯,李旭健.水下和微光图像的去雾及增强方法[J].光学学报, 2020, 40(19):73-85.

SETHI R, INDU S. Fusion of underwater image enhancement and restoration[J]. International Journal of Pattern Recognition and Artificial Intelligence, 2020, 34(3):2054007.

颜阳,王颖,丁雪妍,等.基于图像融合的自适应水下图像增强[J].计算机工程与设计, 2021, 42(1):161-166.

RONNEBERGER O, FISCHER P, BROX T. U-Net:convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention 2015(MICCAI 2015). Cham:Springer, 2015.

刘超,张晓晖,胡清平.超低照度下微光图像增强神经网络损失函数设计分析[J].国防科技大学学报, 2018, 40(4):67-73.

JOHNSON J, ALAHI A, LI F F. Perceptual losses for real-time style transfer and super-resolution[C]//European Conference on Computer Vision 2016(ECCV 2016). Cham:Springer, 2016.

ZHOU W, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612.

FABBRI C, ISLAM M J, SATTAR J. Enhancing underwater imagery using generative adversarial networks[C]//2018 IEEE International Conference on Robotics and Automation(ICRA). New York:IEEE, 2018.

付青青,景春雷,裴彦良,等.基于非锐化掩模引导滤波的水下图像细节增强算法研究[J].海洋学报, 2020, 42(7):130-138.

YANG M, SOWMYA A. An underwater color image quality evaluation metric[J]. IEEE Transactions on Image Processing, 2015,24(12):6062-6071.

Views

152

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

结合多尺度特征融合和注意力机制的肺腺癌病理图像分类胶囊网络

具有互补特征学习框架和注意力特征融合模块的语音情感识别模型

Related Author

李思雨

高静

王云玲

帕力旦·吐尔逊

马玉花

黄佩瑶

程慧慧

唐小煜

Related Institution

新疆临床基因检测与生物医学信息重点实验室

新疆克拉玛依市消化系统肿瘤精准医学临床研究中心

新疆克拉玛依市中心医院病理科

新疆医科大学第一附属医院影像中心

新疆师范大学计算机科学技术学院

⁰