research-article

Discrete Haze Level Dehazing Network

Authors:

Peng ChenAuthors Info & Claims

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

Pages 1828 - 1836

https://doi.org/10.1145/3394171.3413876

Published: 12 October 2020 Publication History

Abstract

In contrast to traditional dehazing methods, deep learning based single image dehazing (SID) algorithms have achieved better performances by creating a mapping function from haze to haze-free images. Usually, the images taken from the natural scenes have different haze levels, but deep SID algorithms only process the hazy images as one group. It makes the deep SID algorithms difficult to deal with the image set with some images having specific haze density. In this paper, a Discrete Haze Level Dehazing network (DHL-Dehaze), a very effective method to dehaze multiple different haze level images, is proposed. The proposed approach considers a single image dehazing problem as a multi-domain image-to-image translation, instead of grouping all hazy images into the same domain. DHL-Dehaze provides computational derivation to describe the role of different haze levels for image translation. To verify the proposed approach, we synthesize two largescale datasets with multiple haze level images based on the NYU-Depth and DIML/CVL datasets. The experiments show that DHL-Dehaze can obtain excellent quantitative and qualitative dehazing results, especially when the haze concentration is high.

Supplementary Material

ZIP File (mmfp0933aux.zip)

Please cite our paper: Xiaofeng Cong, Jie Gui, Kai-Chao Miao, Jun Zhang, Bing Wang, and Peng Chen.

Download
9.53 MB

MP4 File (3394171.3413876.mp4)

Presentation Video.

Download
13.29 MB

References

[1]

Codruta O Ancuti, Cosmin Ancuti, Radu Timofte, and Christophe De Vleeschouwer. O-haze: a dehazing benchmark with real hazy and haze-free outdoor images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 754--762, 2018.

[2]

Cosmin Ancuti, Codruta O Ancuti, and Christophe De Vleeschouwer. D-hazy: A dataset to evaluate quantitatively dehazing algorithms. In IEEE International Conference on Image Processing, pages 2226--2230. IEEE, 2016.

[3]

Cosmin Ancuti, Codruta O Ancuti, and Radu Timofte. Ntire 2018 challenge on image dehazing: Methods and results. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 891--901, 2018.

[4]

Cosmin Ancuti, Codruta O Ancuti, Radu Timofte, and Christophe De Vleeschouwer. I-haze: a dehazing benchmark with real hazy and haze-free indoor images. In International Conference on Advanced Concepts for Intelligent Vision Systems, pages 620--631. Springer, 2018.

[5]

Dana Berman, Shai Avidan, and Shai Avidan. Non-local image dehazing. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1674--1682, 2016.

[6]

Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11):5187--5198, 2016.

Digital Library

[7]

Jaehoon Cho, Dongbo Min, Youngjung Kim, and Kwanghoon Sohn. A large rgb-d dataset for semi-supervised monocular depth estimation. arXiv preprint arXiv:1904.10230, 2019.

[8]

Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In IEEE Conference on Computer Vision and Pattern Recognition, pages 8789--8797, 2018.

[9]

Jia Deng, Wei Dong, Richard Socher, Li Jia Li, and Fei Fei Li. Imagenet: a large-scale hierarchical image database. In 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20--25 June 2009, Miami, Florida, USA, 2009.

[10]

Deniz Engin, Anil Gencc, and Hazim Kemal Ekenel. Cycle-dehaze: Enhanced cyclegan for single image dehazing. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 825--833, 2018.

[11]

Reiner Eschbach and Bernd W Kolpatzik. Image-dependent color saturation correction in a natural scene pictorial image, September 12 1995. US Patent 5,450,217.

[12]

Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Advances in Neural Information Processing Systems, 3:2672--2680, 2014.

Digital Library

[13]

Jie Gui, Zhenan Sun, Yonggang Wen, Dacheng Tao, and Jieping Ye. A review on generative adversarial networks: Algorithms, theory, and applications. arXiv preprint arXiv: 2001.06937, 2020.

[14]

Kaiming He, Jian Sun, and Xiaoou Tang. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(12):2341--2353, 2010.

Digital Library

[15]

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 770--778, 2016.

[16]

Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision, pages 694--711. Springer, 2016.

[17]

Tae Keun Kim, Joon Ki Paik, and Bong Soon Kang. Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Transactions on Consumer Electronics, 44(1):82--87, 1998.

Digital Library

[18]

Harald Koschmieder. Theorie der horizontalen sichtweite. Beitrage zur Physik der freien Atmosphare, pages 33--53, 1924.

[19]

Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436--444, 2015.

[20]

Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. In IEEE Conference on Computer Vision and Pattern Recognition, pages 4681--4690, 2017.

[21]

Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. An all-in-one network for dehazing and beyond. arXiv preprint arXiv:1707.06543, 2017.

[22]

Earl J McCartney. Optics of the atmosphere: scattering by molecules and particles. New York, John Wiley and Sons, Inc., 1976. 421 p., 1976.

[23]

Srinivasa G Narasimhan and Shree K Nayar. Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence, (6):713--724, 2003.

[24]

Shree K Nayar and Srinivasa G Narasimhan. Vision in bad weather. In IEEE International Conference on Computer Vision, pages 820--827. IEEE, 1999.

Digital Library

[25]

Guojun Qi. Loss-sensitive generative adversarial networks on lipschitz densities. International Journal of Computer Vision, pages 1--23, 2019.

[26]

Guojun Qi, Liheng Zhang, Hao Hu, Marzieh Edraki, Jingdong Wang, and Xiansheng Hua. Global versus localized generative adversarial nets. pages 1517--1525, 2018.

[27]

Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. Single image dehazing via multi-scale convolutional neural networks. In European Conference on Computer Vision, pages 154--169. Springer, 2016.

[28]

Daniel Scharstein, Heiko Hirschmüller, York Kitajima, Greg Krathwohl, Nera Nevs ić, Xi Wang, and Porter Westling. High-resolution stereo datasets with subpixel-accurate ground truth. In German Conference on Pattern Recognition, pages 31--42. Springer, 2014.

[29]

Gaurav Sharma, Wencheng Wu, and Edul N Dalal. The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research & Application: Endorsed by Inter-Society Color Council, The Colour Group (Great Britain), Canadian Society for Color, Color Science Association of Japan, Dutch Society for the Study of Color, The Swedish Colour Centre Foundation, Colour Society of Australia, Centre Francc ais de la Couleur, 30(1):21--30, 2005.

[30]

Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In European Conference on Computer Vision, pages 746--760. Springer, 2012.

Digital Library

[31]

Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.

[32]

J Alex Stark. Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Transactions on Image Processing, 9(5):889--896, 2000.

Digital Library

[33]

Jinhui Tang, Xiangbo Shu, Zechao Li, Guojun Qi, and Jingdong Wang. Generalized deep transfer networks for knowledge propagation in heterogeneous domains. 12(4):68, 2016.

[34]

Jean-Philippe Tarel, Nicolas Hautiere, Aurélien Cord, Dominique Gruyer, and Houssam Halmaoui. Improved visibility of road scene images under heterogeneous fog. In IEEE Intelligent Vehicles Symposium, pages 478--485. IEEE, 2010.

[35]

Zhou Wang, Alan C Bovik, Hamid R Sheikh, Eero P Simoncelli, et al. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600--612, 2004.

Digital Library

[36]

Xitong Yang, Zheng Xu, and Jiebo Luo. Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In AAAI conference on artificial intelligence, pages 7485--7492, 2018.

[37]

He Zhang and Vishal M Patel. Densely connected pyramid dehazing network. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3194--3203, 2018.

[38]

Hang Zhao, Orazio Gallo, Iuri Frosio, and Jan Kautz. Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 3(1):47--57, 2016.

[39]

Yiru Zhao, Zhongming Jin, Guojun Qi, Hongtao Lu, and Xiansheng Hua. An adversarial approach to hard triplet generation. pages 508--524, 2018.

[40]

Qingsong Zhu, Jiaming Mai, and Ling Shao. A fast single image haze removal algorithm using color attenuation prior. IEEE Transactions on Image Processing, 24(11):3522--3533, 2015.

Digital Library

Cited By

Gui JCong XHe LTang YKwok J(2024)Illumination Controllable Dehazing Network based on Unsupervised Retinex EmbeddingIEEE Transactions on Multimedia10.1109/TMM.2023.332688126(4819-4830)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3326881
Yuan SChen JLi JJiang WGuo SEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)LHNet: A Low-cost Hybrid Network for Single Image DehazingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612594(7706-7717)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612594
Shen HZhao ZZhang YZhang ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Mutual Information-driven Triple Interaction Network for Efficient Image DehazingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612299(7-16)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612299
Show More Cited By

Index Terms

Discrete Haze Level Dehazing Network
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision representations
        Image representations
  2. Computer graphics
    1. Image manipulation
      1. Image processing

Recommendations

Dense spatially-weighted attentive residual-haze network for image dehazing
Abstract
Haze severely affects computer vision algorithms by degrading the quality of captured images and results in image data loss. With several available approaches for dehazing, single image dehazing is most preferred and challenging. We proposed a ...
Does Haze Removal Help CNN-Based Image Classification?
Computer Vision – ECCV 2018
Abstract
Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the ...
GGADN: Guided generative adversarial dehazing network
Abstract
Image dehazing has always been a challenging topic in image processing. The development of deep learning methods, especially the generative adversarial networks (GAN), provides a new way for image dehazing. In recent years, many deep learning ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '20: Proceedings of the 28th ACM International Conference on Multimedia

October 2020

4889 pages

ISBN:9781450379885

DOI:10.1145/3394171

General Chairs:
Chang Wen Chen
Chinese University of Hong Kong, Shenzhen, China
,
Rita Cucchiara
UNIMORE, Italy
,
Xian-Sheng Hua
Alibaba Group, China
,
Program Chairs:
Guo-Jun Qi
Futurewei Technologies, USA
,
Elisa Ricci
UNITN & Fondazione Bruno Kessler, Italy
,
Zhengyou Zhang
Tencent, China
,
Roger Zimmermann
National University of Singapore, Singapore

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]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 October 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Educational Commission of Anhui Province
National Natural Science Foundation of China

Conference

MM '20

Sponsor:

SIGMM

MM '20: The 28th ACM International Conference on Multimedia

October 12 - 16, 2020

WA, Seattle, USA

Acceptance Rates

Overall Acceptance Rate 995 of 4,171 submissions, 24%

Upcoming Conference

MM '24

Sponsor:
sigmm

The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
327
Total Downloads

Downloads (Last 12 months)51
Downloads (Last 6 weeks)7

Reflects downloads up to 18 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Gui JCong XHe LTang YKwok J(2024)Illumination Controllable Dehazing Network based on Unsupervised Retinex EmbeddingIEEE Transactions on Multimedia10.1109/TMM.2023.332688126(4819-4830)Online publication date: 2024
https://doi.org/10.1109/TMM.2023.3326881
Yuan SChen JLi JJiang WGuo SEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)LHNet: A Low-cost Hybrid Network for Single Image DehazingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612594(7706-7717)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612594
Shen HZhao ZZhang YZhang ZEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Mutual Information-driven Triple Interaction Network for Efficient Image DehazingProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612299(7-16)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3581783.3612299
Gui JCong XCao YRen WZhang JZhang JCao JTao D(2023)A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep LearningACM Computing Surveys10.1145/357691855:13s(1-37)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3576918
Niu LZhao XZhang BZhang L(2023)Fine-grained Visible Watermark Removal2023 IEEE/CVF International Conference on Computer Vision (ICCV)10.1109/ICCV51070.2023.01173(12724-12733)Online publication date: 1-Oct-2023
https://doi.org/10.1109/ICCV51070.2023.01173
Sun ZZhang YBao FWang PYao XZhang C(2022)SADnet: Semi-supervised Single Image Dehazing Method Based on an Attention MechanismACM Transactions on Multimedia Computing, Communications, and Applications10.1145/347845718:2(1-23)Online publication date: 16-Feb-2022
https://doi.org/10.1145/3478457
J. SSuresh AJ.S. NGopi V(2022)Rich feature distillation with feature affinity module for efficient image dehazingOptik10.1016/j.ijleo.2022.169656267(169656)Online publication date: Oct-2022
https://doi.org/10.1016/j.ijleo.2022.169656
Liang JNiu LGuo FLong TZhang LShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Visible Watermark Removal via Self-calibrated Localization and Background RefinementProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475592(4426-4434)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475592

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.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents