Fast defogging algorithm based on improved Ghost module

SU Zhi-ming; WANG Lie

Chinese

您当前的位置：

首页 >

文章列表页 >

Fast defogging algorithm based on improved Ghost module

更新时间：2026-09-04

- Fast defogging algorithm based on improved Ghost module
- Vol. 38, Issue 6, Pages: 27-32(2021)
- 作者机构：
  
  广西大学计算机与电子信息学院,广西,南宁,530004
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2021
- 稿件说明：
移动端阅览
苏志明, 王烈. 基于改进Ghost模块的快速去雾算法[J]. 微电子学与计算机, 2021, 38(6): 27-32.

SU Zhi-ming, WANG Lie. Fast defogging algorithm based on improved Ghost module[J]. 2021, 38(6): 27-32.
苏志明, 王烈. 基于改进Ghost模块的快速去雾算法[J]. 微电子学与计算机, 2021, 38(6): 27-32. DOI：

SU Zhi-ming, WANG Lie. Fast defogging algorithm based on improved Ghost module[J]. 2021, 38(6): 27-32. DOI：

摘要

最近基于卷积神经网络的端到端学习算法在去雾方面取得了进展，但这些算法大都使用过深过大的网络去拟合含雾图像数据，从而导致去雾速度偏慢.为解决此问题，本文提出了一个用于单幅图像快速去雾的高效去雾模型.该模型基于改进的Ghost模块构建轻量级神经网络; 将金字塔池化和注意力机制结合获取图像不同区域的上下文信息，从而提高了网络获取含雾图像全局信息的能力.该网络在RESIDE的室外数据集进行训练和测试.实验结果表明：相比较DCP、MSCNN、AODNet等先进算法，所提出的方法具有令人满意的去雾质量和速度.

Abstract

Recently

end-to-end learning algorithms based on convolutional neural networks have made progress in defogging

but most of them use networks that are too deep and too large to fit foggy image data

resulting in slow defogging speed. To solve this problem

an efficient defogging model is proposed in this paper

which can be used to quickly defog a single image. The model is based on the improved GHOST module to build a lightweight neural network.Pyramid pooling and attention mechanism are combined to obtain the context information of different areas of the image

thus improving the networks ability to obtain the global information of foggy images. The network RESIDE in Donatellas outdoor data set for training and testing. Experimental results show that

compared with DCP

MSCNN

AODNet and other advanced algorithms

the proposed method has satisfactory defogging quality and speed.

关键词

Keywords

references

ZHAO H S, SHI J P, QI X J, et al. Pyramid scene parsing network[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2881-2890. DOI: 10.1109/CVPR.2017.660.

HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353. DOI: 10.1109/TPAMI.2010.168.

LI B Y, PENG X L, WANG Z Y, et al. AOD-Net: all-in-one dehazing network[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4770-4778. DOI: 10.1109/ICCV.2017.511.

HAN K, WANG Y H, TIAN Q, et al. GhostNet: more features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 1580-1589. DOI: 10.1109/CVPR42600.2020.00165.

REN W Q, LIU S, ZHANG H, et al. Single image dehazing via multi-scale convolutional neural networks[C]//Proceedings of the14th European Conference European Conference on Computer Vision. Amsterdam: Springer, 2016: 154-169. DOI: 10.1007/978-3-319-46475-6_10.

HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. arXiv preprint arXiv: 1704.04861, 2017.

LI B Y, REN W Q, FU D P, et al. Benchmarking single-image dehazing and beyond[J]. IEEE Transactions on Image Processing, 2019, 28(1): 492-505. DOI: 10.1109/TIP.2018.2867951.

吴迪, 朱青松. 图像去雾的最新研究进展[J]. 自动化学报, 2015, 41(2): 221-239. DOI: 10.16383/j.aas.2015.c131137. WU D, ZHU Q S. The latest research progress of image dehazing[J]. Acta Automatica Sinica, 2015, 41(2): 221-239. DOI: 10.16383/j.aas.2015.c131137.

CAI B L, XU X M, JIA K, et al. DehazeNet: An end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198. DOI: 10.1109/TIP.2016.2598681.

CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of the 2017 IEEEConference onComputer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 1800-1807. DOI: 10.1109/CVPR.2017.195.

QIN X, WANG Z L, BAI Y C, et al. FFA-Net: Feature Fusion Attention Network for Single Image Dehazing[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 11908-11915. DOI: 10.1609/aaai.v34i07.6865.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Steganography model guided by attention mechanism and improved dense convolution

A review of large model development: technological evolution from BERT to Deepseek

A high-efficiency charge pump for ultra-low voltage boost systems

Design of ultra-low power real-time clock circuits

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Related Author

No data

Related Institution

No data

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰