Adaptive Image Defogging Algorithm Based on DCNN
Authors: 
Wuyunbilige, Licai Liu, Rui Wang, Shuai Gao, Ze Mao, Xiaohu TangAuthors Info & Claims
Wuyunbilige, Licai Liu, Rui Wang, Shuai Gao, Ze Mao, Xiaohu TangAuthors Info & ClaimsPages 42 - 46
Abstract
Under the influence of special weather conditions such as heavy fog, haze, sand and dust, the images taken outdoors are gray and white due to the reflection of light by cluttered particles in the air. Most existing image defogging algorithms are single-layer network feature extraction, feature information is seriously lost, and transmittance calculation is inaccurate. In view of the above problems, an adaptive image defogging algorithm based on deep convolutional neural network is proposed. The implementation of this algorithm is still based on the atmospheric scattering model. There are three kinds of fully convolutional networks: shallow extraction, parallel extraction and deep fusion, which extract the shallow features of the image, extract the deep features and fuse the deep and shallow features together to make the transmission image more accurate. After experimental testing, compared with the traditional defogging algorithm, this deep convolution defogging algorithm has a better defogging effect on outdoor outdoor fog images, especially the defogging effect of details.
References
[1]
C. Li, J. Guo, F. Porikli, et al. (2018) A Cascaded Convolutional Neural Network for Single Image Dehazing. IEEE Access, 6, 24877--24887.
[2]
H. Soyel, P.W. Mcowan. (2012) Automatic image enhancement using intrinsic geometrical information. Electronics Letters, 48(15), 917--919.
[3]
F. Wang, P.L. Li, Y.T. Liang, X. Zhang (2019) Improved image dehazing algorithm based on dark channel prior. Science Technology and Engineering, 19(33), 308--313.
[4]
D. Wu, Q.S. Zhu. (2015) Recent Research Progress of Image Dehazing. Journal of Automation, 41 (02), 221--239.
[5]
K.M. He, J. Sun, X.O. Tang. (2013) Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(6), 1397--1409.
[6]
H.B. Lu, Y.F. Zhao, Y.J. Zhao, SH. Wen, J.Y. Ma, H.K. Lam, H.B. Wang. (2018) Image dehazing based on the combination of bright and dark channels. Acta Optica Sinica, 38 (11), 233--240.
[7]
G.F. Meng, Y. Wang, J.Y. Duan, et al. (2013) Efficient image dehazing with boundary constraint and contextual regularization. 2013 IEEE International Conference on Computer Vision, December 1-8, 2013, Sydney, Australia. New York: IEEE, 617--624.
[8]
Y.H. Zhang, W.X. Zhou. (2019) Image dehazing algorithm based on full convolution regression network.Laser & Optoelectronics Progress, 56(20), 252--261.
[9]
F. Huang, J. Li, J. Cao. (2015) Interferogram interpolation method research on TSMFTIS based on kernel regression with relative deviation. Proceedings of SPIE-The International Society for Optical Engineering, 9449.
[10]
W.Q. Ren, S. Liu, H. Zhang, et al. (2016) Single image dehazing via multi-scale convolutional neural networks. Computer Visio -- ECCV 2016. Springer International Publishing. Cham: Springer, 9906, 154--169.
[11]
L.J. Cox. (1997). Optics of the atmosphere-scattering by molecules and particles[J]. Optica Acta: International Journal of Optics, 24(7): 779.
Index Terms
- Adaptive Image Defogging Algorithm Based on DCNN
Recommendations
A Novel Attention-DeblurGAN-Based Defogging Algorithm
Image and GraphicsAbstractWith the rapid development of machine learning, deep learning-based image defogging algorithms are receiving more and more attention from scholars compared to traditional image defogging method. A novel method for synthesizing high-quality haze ...
Review of single image defogging
With the great advance of computer vision technology, the application of images in daily production work is more and more extensive. However, fairly substantial images collected in foggy weather show significant degradation. Image defogging technology is ...
Image dehazing with an untrained neural network
EITCE '21: Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer EngineeringThe haze floating in the atmosphere scatters and refracts atmospheric light, which leads to degradation of image quality. Recently, many learning-based dehazing methods have been proposed, which use hazy-clean image pairs to train network models. ...
Comments
Information & Contributors
Information
Published In
July 2020
261 pages
ISBN:9781450376044
DOI:10.1145/3414274
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- Natl University of Singapore: National University of Singapore
- SKKU: SUNGKYUNKWAN UNIVERSITY
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 August 2020
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
DSIT 2020
DSIT 2020: 2020 3rd International Conference on Data Science and Information Technology
July 24 - 26, 2020
Xiamen, China
Acceptance Rates
DSIT 2020 Paper Acceptance Rate 40 of 97 submissions, 41%;
Overall Acceptance Rate 114 of 277 submissions, 41%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 54Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)0
Reflects downloads up to 04 Oct 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in