research-article

An Advanced Visibility Restoration Algorithm for Single Hazy Images

Authors:

Shih-Chia HuangAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 11, Issue 4

Article No.: 53, Pages 1 - 21

https://doi.org/10.1145/2726947

Published: 02 June 2015 Publication History

Abstract

Haze removal is the process by which horizontal obscuration is eliminated from hazy images captured during inclement weather. Images captured in natural environments with varied weather conditions frequently exhibit localized light sources or color-shift effects. The occurrence of these effects presents a difficult challenge for hazy image restoration, with which many traditional restoration methods cannot adequately contend. In this article, we present a new image haze removal approach based on Fisher's linear discriminant-based dual dark channel prior scheme in order to solve the problems associated with the presence of localized light sources and color shifts, and thereby achieve effective restoration. Experimental restoration results via qualitative and quantitative evaluations show that our proposed approach can provide higher haze-removal efficacy for images captured in varied weather conditions than can the other state-of-the-art approaches.

References

[1]

P. N. Belhumeur, J. P. Hespanha, and D. Kriegman. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 7, 711--720.

Digital Library

[2]

Bo-Hao Chen and Shih-Chia Huang. 2013. Improved visibility of single hazy images captured in inclement weather conditions. In Proceedings of the IEEE International Symposium on Multimedia. 267--270.

Digital Library

[3]

Raanan Fattal. 2008. Single image dehazing. ACM Trans. Graph. 27, 3, Article 72. org/10.1145/1360612.1360671

Digital Library

[4]

Nicolas Hautire, Jean Philippe Tarel, Didier Aubert, and Ric Dumont. 2011. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis & Stereology 27, 2. http://www.ias-iss.org/ojs/IAS/article/view/834

[5]

Kaiming He, Jian Sun, and Xiaoou Tang. 2011. Single Image Haze Removal Using Dark Channel Prior. IEEE Trans. Pattern Anal. Mach. Intell. 33, 12, 2341--2353.

Digital Library

[6]

M. Anwar Hossain, Pradeep K. Atrey, and Abdulmotaleb El Saddik. 2011. Modeling and assessing quality of information in multisensor multimedia monitoring systems. ACM Trans. Multimedia Comput. Commun. Appl. 7, 1, Article 3.

Digital Library

[7]

Shih-Chia Huang, Bo-Hao Chen, and Yi-Jui Cheng. 2014a. An efficient visibility enhancement algorithm for road scenes captured by intelligent transportation systems. IEEE Trans. Intell. Transp. Syst. 15, 5, 2321--2332.

[8]

Shih-Chia Huang, Bo-Hao Chen, and Wei-Jheng Wang. 2014b. Visibility restoration of single hazy images captured in real-world weather conditions. IEEE Trans. Circuits Syst. Video Technol. 24, 10, 1814--1824.

[9]

Anya Hurlbert. 1986. Formal connections between lightness algorithms. J. Opt. Soc. Amer. A 3, 10, 1684--1693.

[10]

Wenbo Jin, Zengyuan Mi, Xiaotian Wu, Yue Huang, and Xinghao Ding. 2012. Single image de-haze based on a new dark channel estimation method. In Proceedings of the IEEE International Conference on Computer Science and Automation Engineering. Vol. 2, 791--795.

[11]

Johannes Kopf, Boris Neubert, Billy Chen, Michael Cohen, Daniel Cohen-Or, Oliver Deussen, Matt Uyttendaele, and Dani Lischinski. 2008. Deep photo: Model-based photograph enhancement and viewing. ACM Trans. Graph. 27, 5, Article 116.

Digital Library

[12]

E. Y. Lam. 2005. Combining gray world and retinex theory for automatic white balance in digital photography. In Proceedings of the 9th International Symposium on Consumer Electronics. 134--139.

[13]

Edwin H. Land. 1986. An alternative technique for the computation of the designator in the retinex theory of color vision. Proc Natl Acad Sci USA.

[14]

A. Levin, D. Lischinski, and Y. Weiss. 2008. A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 30, 2, 228--242.

Digital Library

[15]

Xiaotao Liu, Mark Corner, and Prashant Shenoy. 2009. SEVA: Sensor-enhanced video annotation. ACM Trans. Multimedia Comput. Commun. Appl. 5, 3, Article 24.

Digital Library

[16]

Tao Mei, Lin-Xie Tang, Jinhui Tang, and Xian-Sheng Hua. 2013. Near-lossless semantic video summarization and its applications to video analysis. ACM Trans. Multimedia Comput. Commun. Appl. 9, 3, Article 16.

Digital Library

[17]

S. G. Narasimhan and S. K. Nayar. 2003a. Contrast restoration of weather degraded images. IEEE Trans. Pattern Anal. Mach. Intell. 25, 6, 713--724.

Digital Library

[18]

Srinivasa G. Narasimhan and Shree Nayar. 2003b. Interactive deweathering of an image using physical models. In Proceedings of the IEEE Workshop on Color and Photometric Methods in Computer Vision in Conjunction with ICCV.

[19]

Ko Nishino, Louis Kratz, and Stephen Lombardi. 2012. Bayesian defogging. Int. J. Comput. Vision 98, 3, 263--278.

Digital Library

[20]

J. P. Oakley and B. L. Satherley. 1998. Improving image quality in poor visibility conditions using a physical model for contrast degradation. IEEE Trans. Image Process. 7, 2, 167--179. org/10.1109/83.660994

Digital Library

[21]

S. Shwartz, E. Namer, and Y. Y. Schechner. 2006. Blind haze separation. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2, 1984--1991.

Digital Library

[22]

Lauro Snidaro, Ingrid Visentini, and Gian Luca Foresti. 2012. Fusing multiple video sensors for surveillance. ACM Trans. Multimedia Comput. Commun. Appl. 8, 1, Article 7.

Digital Library

[23]

Shen-Chuan Tai, Tzu-Wen Liao, Yi-Ying Chang, and Chih Pei Yeh. 2012. Automatic White Balance algorithm through the average equalization and threshold. In Proceedings of the 8th International Conference on Information Science and Digital Content Technology. Vol. 3, 571--576.

[24]

R. T. Tan. 2008. Visibility in bad weather from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1--8.

[25]

J. P Tarel and N. Hautiere. 2009. Fast visibility restoration from a single color or gray level image. In Proceedings of the IEEE 12th International Conference on Computer Vision. 2201--2208.

[26]

Michael A. Webster. 1996. Human colour perception and its adaptation. Network: Computation in Neural Systems 7, 4, 587--634.

[27]

Gerhard West and Michael H. Brill. 1982. Necessary and sufficient conditions for Von Kries chromatic adaptation to give color constancy. J. Math. Biology 15, 2, 249--258.

[28]

Junwen Wu and Mohan M. Trivedi. 2010. An eye localization, tracking and blink pattern recognition system: Algorithm and evaluation. ACM Trans. Multimedia Comput. Commun. Appl. 6, 2, Article 8, 23 pages.

Digital Library

[29]

Bin Xie, Fan Guo, and Zixing Cai. 2010. Improved single image dehazing using dark channel prior and multi-scale Retinex. In Proceedings of the International Conference on Intelligent System Design and Engineering Application, Vol. 1. 848--851.

Digital Library

[30]

Haoran Xu, Jianming Guo, Qing Liu, and Lingli Ye. 2012. Fast image dehazing using improved dark channel prior. In Proceedings of the International Conference on Information Science and Technology. 663--667.

[31]

Jing Yu and Qingmin Liao. 2011. Fast single image fog removal using edge-preserving smoothing. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. 1245--1248.

Cited By

Zhang YCai YYan DLin R(2024)Real-World Scene Image Enhancement with Contrastive Domain Adaptation LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/369497320:12(1-23)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3694973
Guo QZhang ZZhou MYue HPu HLuo J(2023)Image Defogging Based on Regional Gradient Constrained PriorACM Transactions on Multimedia Computing, Communications, and Applications10.1145/361783420:3(1-17)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617834
Zhou MLeng HFang BXiang TWei XJia W(2023)Low-light Image Enhancement via a Frequency-based Model with Structure and Texture DecompositionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/359096519:6(1-23)Online publication date: 31-May-2023
https://dl.acm.org/doi/10.1145/3590965
Show More Cited By

Index Terms

An Advanced Visibility Restoration Algorithm for Single Hazy Images
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Reconstruction
  2. Computer graphics
    1. Image manipulation
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Computation of transforms

Recommendations

Improved Visibility of Single Hazy Images Captured in Inclement Weather Conditions
ISM '13: Proceedings of the 2013 IEEE International Symposium on Multimedia

Haze removal is the process by which horizontal obscuration is eliminated from hazy images captured during inclement weather. Sandstorms present a particularly challenging condition, images captured during sandstorms often exhibit color-shift effects ...
A Novel Visibility Restoration Algorithm for Single Hazy Images
SMC '13: Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics

The visibility of outdoor images captured in inclement weather will become degraded due to the presence of haze, fog, mist, and so on. Poor visibility caused by atmospheric phenomenon in turn causes failure in computer vision applications, such as ...
Hazy Image Restoration by Bi-Histogram Modification
Regular Papers and Special Section on Intelligent Healthcare Informatics

Visibility restoration techniques are widely used for information recovery of hazy images in many computer vision applications. Estimation of haze density is an essential task of visibility restoration techniques. However, conventional visibility ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 11, Issue 4

April 2015

231 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/2788342

Editor:
Ralf Steinmetz
Technische Universität Darmstadt, Germany

Issue’s Table of Contents

Copyright © 2015 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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 June 2015

Accepted: 01 January 2015

Revised: 01 January 2015

Received: 01 May 2014

Published in TOMM Volume 11, Issue 4

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Funding Sources

Ministry of Science and Technology of the Republic of China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

27
Total Citations
View Citations
483
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang YCai YYan DLin R(2024)Real-World Scene Image Enhancement with Contrastive Domain Adaptation LearningACM Transactions on Multimedia Computing, Communications, and Applications10.1145/369497320:12(1-23)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3694973
Guo QZhang ZZhou MYue HPu HLuo J(2023)Image Defogging Based on Regional Gradient Constrained PriorACM Transactions on Multimedia Computing, Communications, and Applications10.1145/361783420:3(1-17)Online publication date: 23-Oct-2023
https://dl.acm.org/doi/10.1145/3617834
Zhou MLeng HFang BXiang TWei XJia W(2023)Low-light Image Enhancement via a Frequency-based Model with Structure and Texture DecompositionACM Transactions on Multimedia Computing, Communications, and Applications10.1145/359096519:6(1-23)Online publication date: 31-May-2023
https://dl.acm.org/doi/10.1145/3590965
Sharma NKhera ASayal DSingh AKansal I(2023)Dataset for Face-mask Recognition in Poor Visibility Conditions based upon IoT enabled Robotics2023 2nd Edition of IEEE Delhi Section Flagship Conference (DELCON)10.1109/DELCON57910.2023.10127304(1-4)Online publication date: 24-Feb-2023
https://doi.org/10.1109/DELCON57910.2023.10127304
Cheng BLi JChen YZeng T(2023)Snow Mask Guided Adaptive Residual Network for Image Snow RemovalComputer Vision and Image Understanding10.1016/j.cviu.2023.103819236(103819)Online publication date: Nov-2023
https://doi.org/10.1016/j.cviu.2023.103819
Amato DLo Bosco GGiancarlo R(2023)Neural networks as building blocks for the design of efficient learned indexesNeural Computing and Applications10.1007/s00521-023-08841-135:29(21399-21414)Online publication date: 21-Jul-2023
https://dl.acm.org/doi/10.1007/s00521-023-08841-1
Karoon KNemer Z(2022)A Review of Methods of Removing Haze from An ImageInternational Journal of Electrical and Electronics Research10.37391/ijeer.10035410:3(742-746)Online publication date: 30-Sep-2022
https://doi.org/10.37391/ijeer.100354
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
S. BGeorge S(2022)TTV Regularized LRTA Technique for the Estimation of Haze Model Parameters in Video DehazingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/346545418:1(1-22)Online publication date: 27-Jan-2022
https://dl.acm.org/doi/10.1145/3465454
Reddy GDasari KKumar BSridhar BSainath BPrasad S(2022)Advanced Object Enhancement in an Image2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI)10.1109/IATMSI56455.2022.10119238(1-5)Online publication date: 21-Dec-2022
https://doi.org/10.1109/IATMSI56455.2022.10119238
Show More Cited By

View Options

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 Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents