Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3220162.3220165acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicmsspConference Proceedingsconference-collections
research-article

Haze Removal Algorithm Using Color Attenuation Prior and Guided Filter

Published: 28 April 2018 Publication History

Abstract

In this paper, we describe the formatting guidelines for ACM SIG Proceedings. In this paper, we mainly introduce the haze removal algorithm which using color attenuation prior. We also propose a novel solution in the case of particular situations. Moreover, in order to make the algorithm more accurate, we propose a hierarchical searching method based on the quad-tree subdivision for estimating atmospheric light. In addition, we employ the shiftable window scheme to improve the guided filter, so that it can refine the depth map while effectively protecting image edge. Experimental result shows that our proposed method not only can obviously remove the haze from the haze image, but also the performance is more reliable in the case of particular situations.

References

[1]
Woodell, G. A., Jobson, D. J., Rahman, Z. U., and Hines, G. 2006. Advanced image processing of aerial imagery. In Proceedings of the SPIE Conference on Visual Information Processing (Orlando(Kissimmee), Florida, United States, April 7-21, 2006). SPIE, 62460E.
[2]
Shao, L., Liu, L., and Li, X. 2014. Feature learning for image classification via multiobjective genetic programming. IEEE. T. Neurl. Networ. 25 (Jul. 2014), 1359--1371.
[3]
Han, J., Ji, X., and Hu, X. 2013. Representing and retrieving video shots in human-centric brain imaging space. IEEE. T. Image. Process. 22 (Jul. 2013), 2723--2736.
[4]
Han, J., Ngan, K., Li, M., and Zhang, H. J. 2005. A memory learning framework for effective image retrieval. IEEE. T. Image. Process. 14 (Apr. 2005), 511--524.
[5]
Tao, D., Tang, X., Li, X., and Wu, X. 2006. Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE. T. Pattern. Anal. 28 (Jul. 2006), 1088--1099,.
[6]
Han, J., Zhang, D., Cheng, G., Guo, L., and Ren, J. 2015. Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE. T. Geo. sci. Remote. 53 (Jun. 2015), 3325--3337.
[7]
Zhang, Z. and Tao, D. 2012. Slow feature analysis for human action recognition. IEEE. T. Pattern. Anal. 34 (Mar. 2012), 436--450.
[8]
Gonzalez, R. Woods, R. 2008. Digital Image Processing, Prentice-Hall.
[9]
Tan, R. T. 2008. Visibility in bad weather from a single image. In Proceedings of the IEEE Conference on Comput. Vis and Pattern Recognit. (Anchorage, AK, USA, June 23-28, 2008), 1--8.
[10]
Fattal, R. 2008 Single image dehazing. ACM. T. Graphic. 27 (Aug. 2008), 1--9.
[11]
Kratz, L. and Nishino, K. 2009. Factorizing scene albedo and depth from a single foggy image. IEEE. I. Conf. Comp. Vis. 30 (Oct. 2009), 1701--1708.
[12]
Nishino, K., Kratz, L., and Lombardi, S. 2012. Bayesian defogging. INT. J. Comput. Vision. 3 (Jul. 2012), 263--278.
[13]
He, K., Sun, J., and Tang, X. Single image haze removal using dark channel prior. IEEE. T. Pattern. Anal. 33 (Sep. 2011), 1956--1963.
[14]
Yu, J. and Q. Liao. 2011. Fast single image fog removal using edge-preserving smoothing. In Proceedings of the IEEE Conference on Acoustics, Speech and Signal Processing (Prague, Czech Republic, May 22-27, 2011). 1245--1248.
[15]
Ullah, E., Nawaz, R., Iqbal, J. 2013 Single image haze removal using improved dark channel prior. In Proceedings of the 5th International Conference on Modelling, Identification & Control(Cairo, Egypt, Aug 31 - Sept 2, 2013). 245--248.
[16]
Makkar D, Malhotra M. 2016. Review On Single Image Haze Removal Using Dark Channel Prior. INT. J. Electron. 6 (Dec. 2016), 39--42.
[17]
Zhu, Q., Mai, J., and Shao, L. 2015. A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior. IEEE. T. Image. Process. 24 (Nov. 2015), 3522--3533.
[18]
He K, Sun J, Tang X. 2013. Guided image filtering. IEEE. T. Pattern. Anal. 35 (June. 2013), 1397--1409.
[19]
Kim, J. H., Jang, W. D., and Sim, J. Y. 2013. Optimized contrast enhancement for real-time image and video dehazing. J, Vis. Commun. Image. R. 24 (Apr. 2013), 410--425.
[20]
Narasimhan, S. and Nayar, S. 2002. Vision and the atmosphere. Int. J. Comput. Vision. 48 (Jul. 2002), 233--254.
[21]
Tarel, J.P., Hautière, N., Caraffa, L., Cord, A., Halmaoui, H., and Gruyer, D. 2012. Vision enhancement in homogeneous and heterogeneous fog. IEEE. Intell. Transp. Syst. Mag. 4(Apr. 2012), 6--20.
[22]
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. 2004. Image quality assessment: From error visibility to structural similarity IEEE. T. Image. Process. 13 (Apr. 2004), 600--612.

Cited By

View all
  • (2023)A deep learning model to detect foggy images for vision enhancementThe Imaging Science Journal10.1080/13682199.2023.218542971:6(484-498)Online publication date: 9-Mar-2023
  • (2023)A transmission model based deep neural network for image dehazingMultimedia Tools and Applications10.1007/s11042-023-17010-483:13(39255-39281)Online publication date: 7-Oct-2023
  • (2021)Research on Hardware Implementation Technology of Dark Channel Priori Dehazing Algorithm Based on PYNQ2021 3rd International Conference on Applied Machine Learning (ICAML)10.1109/ICAML54311.2021.00016(37-42)Online publication date: Jul-2021

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICMSSP '18: Proceedings of the 3rd International Conference on Multimedia Systems and Signal Processing
April 2018
168 pages
ISBN:9781450364577
DOI:10.1145/3220162
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: 28 April 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. color attenuation prior
  2. guided filter
  3. haze removal
  4. quad-tree subdivision

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICMSSP '18

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)2
Reflects downloads up to 05 Mar 2025

Other Metrics

Citations

Cited By

View all
  • (2023)A deep learning model to detect foggy images for vision enhancementThe Imaging Science Journal10.1080/13682199.2023.218542971:6(484-498)Online publication date: 9-Mar-2023
  • (2023)A transmission model based deep neural network for image dehazingMultimedia Tools and Applications10.1007/s11042-023-17010-483:13(39255-39281)Online publication date: 7-Oct-2023
  • (2021)Research on Hardware Implementation Technology of Dark Channel Priori Dehazing Algorithm Based on PYNQ2021 3rd International Conference on Applied Machine Learning (ICAML)10.1109/ICAML54311.2021.00016(37-42)Online publication date: Jul-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media