Abstract

Dark Channel Prior (DCP) is one of the significant dehazing methods based upon the observation of the key features of the haze-free images. But it has disadvantages; high computational complexity, over-enhancement in the sky region, flickering artefacts in video processing, and poor dehazing. Therefore, we propose improved solutions to solve the aforementioned drawbacks. First, we adopt the fast one-dimensional filter, look-up table, and program optimization to reduce the computational complexity. Next, we follow by using a part of the guided filter for sky detection and to preserve the sky region from noise by avoiding over recovery. Then, we propose an airlight update strategy and adjust the radius of a guided filter to reduce the flickering artifacts and also propose an airlight estimation method to produce the better dehazing result as the final step of our algorithm. The improved results from our proposed algorithm are stable and are obtained from the real-time processing suitable for ADAS, surveillance, and monitoring systems. The implementation of the proposed algorithm has yielded a processing speed of 75 fps and 23 fps respectively on an NVIDIA Jetson TX1 embedded platform and Renesas R-Car M2, both on D1 (720x480) resolution videos.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
OSA Recommended Articles
Recovering of weather degraded images based on RGB response ratio constancy

Raúl Luzón-González, Juan L. Nieves, and Javier Romero
Appl. Opt. 54(4) B222-B231 (2015)

Methods for automatic target recognition by use of electro-optic sensors: introduction to the feature issue

Abhijit Mahalanobis and François Goudail
Appl. Opt. 43(2) 207-209 (2004)

Data-driven background representation method to video surveillance

Zhihui Li, Yingji Xia, and Zhaowei Qu
J. Opt. Soc. Am. A 34(2) 193-202 (2017)

References

  • View by:
  • |
  • |
  • |

  1. H. Koschmieder, “Theorie der horizontalen sichtweite,” Beitrage zur Physik der freien Atmosphare pp. 33–53 (1924).
  2. N. E. Boudette, “Autopilot cited in death of chinese driver,” The New York Times (2016).
  3. E. J. McCartney, “Optics of the atmosphere: scattering by molecules and particles,” New York, John Wiley Sons, Inc., 1976. 421 p. (1976).
  4. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011).
    [Crossref] [PubMed]
  5. X. Lv, W. Chen, and I.-f. Shen, “Real-time dehazing for image and video,” in Computer Graphics and Applications (PG), 2010 18th Pacific Conference on, (IEEE, 2010), pp. 62–69.
  6. K. He and J. Sun, 6. K. He and J. Sun, “Fast guided filter. arxiv 2015,” arXiv preprint arXiv:1505.0099.
  7. G. Wang, G. Ren, L. Jiang, and T. Quan, “Single image dehazing algorithm based on sky region segmentation,” Inf. Technol. J. 12(6), 1168–1175 (2013).
    [Crossref]
  8. J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” J. Vis. Commun. Image Represent. 24(3), 410–425 (2013).
    [Crossref]
  9. K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE transactions on pattern analysis & machine intelligence pp. 1397–1409 (2013).
  10. M. Wang, J. Mai, Y. Liang, R. Cai, T. Zhengjia, and Z. Zhang, “Component-based distributed framework for coherent and real-time video dehazing,” in Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference on, vol. 1 (IEEE, 2017), pp. 321–324.
    [Crossref]
  11. X. Xiang, Y. Cheng, and J. Tang, “A novel video dehazing method based on temporal visual coherence,” in Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, (ACM, 2015), p. 36.
    [Crossref]
  12. B. Cai, X. Xu, and D. Tao, “Real-time video dehazing based on spatio-temporal mrf,” in Pacific Rim Conference on Multimedia, (Springer, 2016), pp. 315–325.
    [Crossref]
  13. J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
    [Crossref]
  14. Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Trans. Image Process. 24(11), 3522–3533 (2015).
    [Crossref] [PubMed]
  15. S. Rakshit, A. Ghosh, and B. U. Shankar, “Fast mean filtering technique (fmft),” Pattern Recognit. 40(3), 890–897 (2007).
    [Crossref]
  16. D. Lemire, “Streaming maximum-minimum filter using no more than three comparisons per element,” arXiv preprint cs/0610046 (2006).
  17. S.-W. Hsu, G.-Y. Chen, P.-C. Shen, C.-Y. Cho, and J.-I. Guo, “Dynamic local contrast enhancement for advanced driver assistance system in harsh environments,” in Consumer Electronics-Taiwan (ICCE-TW), 2014 IEEE International Conference on, (IEEE, 2014), pp. 69–70.
    [Crossref]
  18. G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, (2013), pp. 617–624.
    [Crossref]
  19. J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
    [Crossref]
  20. IEEE Signal Processing Society, ideo and Image Processing Cup-2017,” https://ghassanalregib.com/cure-tsd/ .
  21. S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 28, 91–99 (2015).
  22. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

2015 (2)

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Trans. Image Process. 24(11), 3522–3533 (2015).
[Crossref] [PubMed]

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 28, 91–99 (2015).

2013 (2)

G. Wang, G. Ren, L. Jiang, and T. Quan, “Single image dehazing algorithm based on sky region segmentation,” Inf. Technol. J. 12(6), 1168–1175 (2013).
[Crossref]

J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” J. Vis. Commun. Image Represent. 24(3), 410–425 (2013).
[Crossref]

2012 (1)

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

2011 (2)

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref] [PubMed]

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

2007 (1)

S. Rakshit, A. Ghosh, and B. U. Shankar, “Fast mean filtering technique (fmft),” Pattern Recognit. 40(3), 890–897 (2007).
[Crossref]

Cai, B.

B. Cai, X. Xu, and D. Tao, “Real-time video dehazing based on spatio-temporal mrf,” in Pacific Rim Conference on Multimedia, (Springer, 2016), pp. 315–325.
[Crossref]

Cao, X.

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

Caraffa, L.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

Cheng, Y.

X. Xiang, Y. Cheng, and J. Tang, “A novel video dehazing method based on temporal visual coherence,” in Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, (ACM, 2015), p. 36.
[Crossref]

Cord, A.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

Duan, J.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, (2013), pp. 617–624.
[Crossref]

Ghosh, A.

S. Rakshit, A. Ghosh, and B. U. Shankar, “Fast mean filtering technique (fmft),” Pattern Recognit. 40(3), 890–897 (2007).
[Crossref]

Girshick, R.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 28, 91–99 (2015).

Gruyer, D.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

Halmaoui, H.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

Hautiere, N.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

He, K.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 28, 91–99 (2015).

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref] [PubMed]

Jang, W.-D.

J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” J. Vis. Commun. Image Represent. 24(3), 410–425 (2013).
[Crossref]

Jiang, L.

G. Wang, G. Ren, L. Jiang, and T. Quan, “Single image dehazing algorithm based on sky region segmentation,” Inf. Technol. J. 12(6), 1168–1175 (2013).
[Crossref]

Kim, C.-S.

J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” J. Vis. Commun. Image Represent. 24(3), 410–425 (2013).
[Crossref]

Kim, J.-H.

J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” J. Vis. Commun. Image Represent. 24(3), 410–425 (2013).
[Crossref]

Li, L.

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

Mai, J.

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Trans. Image Process. 24(11), 3522–3533 (2015).
[Crossref] [PubMed]

Meng, G.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, (2013), pp. 617–624.
[Crossref]

Pan, C.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, (2013), pp. 617–624.
[Crossref]

Quan, T.

G. Wang, G. Ren, L. Jiang, and T. Quan, “Single image dehazing algorithm based on sky region segmentation,” Inf. Technol. J. 12(6), 1168–1175 (2013).
[Crossref]

Rakshit, S.

S. Rakshit, A. Ghosh, and B. U. Shankar, “Fast mean filtering technique (fmft),” Pattern Recognit. 40(3), 890–897 (2007).
[Crossref]

Ren, G.

G. Wang, G. Ren, L. Jiang, and T. Quan, “Single image dehazing algorithm based on sky region segmentation,” Inf. Technol. J. 12(6), 1168–1175 (2013).
[Crossref]

Ren, S.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 28, 91–99 (2015).

Shankar, B. U.

S. Rakshit, A. Ghosh, and B. U. Shankar, “Fast mean filtering technique (fmft),” Pattern Recognit. 40(3), 890–897 (2007).
[Crossref]

Shao, L.

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Trans. Image Process. 24(11), 3522–3533 (2015).
[Crossref] [PubMed]

Sim, J.-Y.

J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” J. Vis. Commun. Image Represent. 24(3), 410–425 (2013).
[Crossref]

Sun, J.

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 28, 91–99 (2015).

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref] [PubMed]

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

Tang, J.

X. Xiang, Y. Cheng, and J. Tang, “A novel video dehazing method based on temporal visual coherence,” in Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, (ACM, 2015), p. 36.
[Crossref]

Tang, X.

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref] [PubMed]

Tao, D.

B. Cai, X. Xu, and D. Tao, “Real-time video dehazing based on spatio-temporal mrf,” in Pacific Rim Conference on Multimedia, (Springer, 2016), pp. 315–325.
[Crossref]

Tarel, J.-P.

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

Wang, G.

G. Wang, G. Ren, L. Jiang, and T. Quan, “Single image dehazing algorithm based on sky region segmentation,” Inf. Technol. J. 12(6), 1168–1175 (2013).
[Crossref]

Wang, Y.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, (2013), pp. 617–624.
[Crossref]

Xiang, S.

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, (2013), pp. 617–624.
[Crossref]

Xiang, X.

X. Xiang, Y. Cheng, and J. Tang, “A novel video dehazing method based on temporal visual coherence,” in Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, (ACM, 2015), p. 36.
[Crossref]

Xu, X.

B. Cai, X. Xu, and D. Tao, “Real-time video dehazing based on spatio-temporal mrf,” in Pacific Rim Conference on Multimedia, (Springer, 2016), pp. 315–325.
[Crossref]

Yang, G.

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

Zhang, J.

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

Zhang, Y.

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

Zhu, Q.

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Trans. Image Process. 24(11), 3522–3533 (2015).
[Crossref] [PubMed]

Adv. Neural Inf. Process. Syst. (1)

S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Adv. Neural Inf. Process. Syst. 28, 91–99 (2015).

IEEE Intell. Transp. Syst. Mag. (1)

J.-P. Tarel, N. Hautiere, L. Caraffa, A. Cord, H. Halmaoui, and D. Gruyer, “Vision enhancement in homogeneous and heterogeneous fog,” IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012).
[Crossref]

IEEE Trans. Image Process. (1)

Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Trans. Image Process. 24(11), 3522–3533 (2015).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011).
[Crossref] [PubMed]

Inf. Technol. J. (1)

G. Wang, G. Ren, L. Jiang, and T. Quan, “Single image dehazing algorithm based on sky region segmentation,” Inf. Technol. J. 12(6), 1168–1175 (2013).
[Crossref]

J. Vis. Commun. Image Represent. (1)

J.-H. Kim, W.-D. Jang, J.-Y. Sim, and C.-S. Kim, “Optimized contrast enhancement for real-time image and video dehazing,” J. Vis. Commun. Image Represent. 24(3), 410–425 (2013).
[Crossref]

Pattern Recognit. (1)

S. Rakshit, A. Ghosh, and B. U. Shankar, “Fast mean filtering technique (fmft),” Pattern Recognit. 40(3), 890–897 (2007).
[Crossref]

Vis. Comput. (1)

J. Zhang, L. Li, Y. Zhang, G. Yang, X. Cao, and J. Sun, “Video dehazing with spatial and temporal coherence,” Vis. Comput. 27(6-8), 749–757 (2011).
[Crossref]

Other (14)

D. Lemire, “Streaming maximum-minimum filter using no more than three comparisons per element,” arXiv preprint cs/0610046 (2006).

S.-W. Hsu, G.-Y. Chen, P.-C. Shen, C.-Y. Cho, and J.-I. Guo, “Dynamic local contrast enhancement for advanced driver assistance system in harsh environments,” in Consumer Electronics-Taiwan (ICCE-TW), 2014 IEEE International Conference on, (IEEE, 2014), pp. 69–70.
[Crossref]

G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan, “Efficient image dehazing with boundary constraint and contextual regularization,” in Proceedings of the IEEE international conference on computer vision, (2013), pp. 617–624.
[Crossref]

IEEE Signal Processing Society, ideo and Image Processing Cup-2017,” https://ghassanalregib.com/cure-tsd/ .

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556 (2014).

K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE transactions on pattern analysis & machine intelligence pp. 1397–1409 (2013).

M. Wang, J. Mai, Y. Liang, R. Cai, T. Zhengjia, and Z. Zhang, “Component-based distributed framework for coherent and real-time video dehazing,” in Computational Science and Engineering (CSE) and Embedded and Ubiquitous Computing (EUC), 2017 IEEE International Conference on, vol. 1 (IEEE, 2017), pp. 321–324.
[Crossref]

X. Xiang, Y. Cheng, and J. Tang, “A novel video dehazing method based on temporal visual coherence,” in Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, (ACM, 2015), p. 36.
[Crossref]

B. Cai, X. Xu, and D. Tao, “Real-time video dehazing based on spatio-temporal mrf,” in Pacific Rim Conference on Multimedia, (Springer, 2016), pp. 315–325.
[Crossref]

X. Lv, W. Chen, and I.-f. Shen, “Real-time dehazing for image and video,” in Computer Graphics and Applications (PG), 2010 18th Pacific Conference on, (IEEE, 2010), pp. 62–69.

K. He and J. Sun, 6. K. He and J. Sun, “Fast guided filter. arxiv 2015,” arXiv preprint arXiv:1505.0099.

H. Koschmieder, “Theorie der horizontalen sichtweite,” Beitrage zur Physik der freien Atmosphare pp. 33–53 (1924).

N. E. Boudette, “Autopilot cited in death of chinese driver,” The New York Times (2016).

E. J. McCartney, “Optics of the atmosphere: scattering by molecules and particles,” New York, John Wiley Sons, Inc., 1976. 421 p. (1976).

Supplementary Material (2)

NameDescription
» Visualization 1       Comparison of different approaches on video using (a) Input hazy frames. (b) DLCE [17]. (c) DCP [4]. (d) OCE [8]. (e) MRF [12]. (f) Our proposed method. 4. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE transa
» Visualization 2       Comparison of different approaches on video using (a) Input hazy frames. (b) DLCE [17]. (c) DCP [4]. (d) OCE [8]. (e) MRF [12]. (f) Our proposed method. 4. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” IEEE transa

Cited By

OSA participates in Crossref's Cited-By Linking service. Citing articles from OSA journals and other participating publishers are listed here.

Alert me when this article is cited.


Figures (25)

Fig. 1
Fig. 1 Atmospheric Scattering Model.
Fig. 2
Fig. 2 The flowchart of the proposed DCP based dehazing algorithm.
Fig. 3
Fig. 3 The proposed airlight estimation.
Fig. 4
Fig. 4 Comparison between Dehazing result images. (a) Input hazy image, (b) θairlight = 10, (c) θairlight = 50 (d) DCP generated image
Fig. 5
Fig. 5 The result of proposed sky estimation method.
Fig. 6
Fig. 6 The result of dark channel estimation. (a) Input hazy image, (b) Result image.
Fig. 7
Fig. 7 The result of coarse transmission estimation. (a) Dark channel image. (b) Result image.
Fig. 8
Fig. 8 The refined transmission result. (a) Coarse Transmission Result. (b) Refined Transmission Result.
Fig. 9
Fig. 9 The effect of adopting proposed refined transmission. (a) Without refined transmission. (b) With refined transmission.
Fig. 10
Fig. 10 The procedure and its result image of DCP. (a) Input hazy image. (b) Dark channel. (c) Coarse transmission. (d) Refined transmission. (e) Recovered scene.
Fig. 11
Fig. 11 The comparison of different airlight update strategies. (a) Input hazy images at frame #171, #172, #173, and #484. (b) Update airlight independently. (c) Airlight update strategy of [11]. (d) Our Proposed airlight update strategy.
Fig. 12
Fig. 12 The line charts by different airlight update strategies. (a) Update airlight frame by frame. (b) Airlight update strategy in [10]. (c) Our proposed airlight update strategy.
Fig. 13
Fig. 13 The single-image dehazing result with adopting minimum filter. (a) Input hazy image. (b) Coarse transmission map. (c) Dehazing result. (d) Coarse transmission after minimum filter. (e) Dehazing result.
Fig. 14
Fig. 14 Spatial incoherence issue and its solutions. (a) Input hazy images at frame #399, #400, and #401. (b) Spatial incoherence. (c) Reduce spatial incoherence result by minimum filter. (d) Reduce spatial incoherence by enlarging the radius of the mean filter.
Fig. 15
Fig. 15 Comparison of the results from different approaches on single hazy image. (a) Input hazy image. (b) DLCE [17]. (c) DCP [4]. (d) Tarel et al.’s [19]. (e) Meng et al.’s [18]. (f) Our proposed method.
Fig. 16
Fig. 16 Comparison of the results from different approaches on single hazy image. (a) Input hazy image. (b) DLCE 1 [17]. (c) DCP [4]. (d) Tarel et al.’s [19]. (e) Meng et al.’s [18]. (f) Our proposed method.
Fig. 17
Fig. 17 Comparison of results from different approaches on single hazy image. (a) Input hazy image. (b) DLCE [17]. (c) DCP [4]. (d) Tarel et al.’s [19]. (e) Meng et al.’s [18]. (f) Our proposed method.
Fig. 18
Fig. 18 Comparison of the results different approaches on single hazy image. (a) Input hazy image. (b) DLCE [17]. (c) DCP [4]. (d) Tarel et al.’s [19]. (e) Meng et al.’s [18]. (f) Our proposed method.
Fig. 19
Fig. 19 Comparison of the results from different approaches on single hazy image. (a) Input hazy image. (b) DLCE [17]. (c) DCP [4]. (d) Tarel et al.’s [19]. (e) Meng et al.’s [18]. (f) Our proposed method.
Fig. 20
Fig. 20 Comparison of the results from different methods on single hazy image. (a) Input hazy image. (b) DLCE [17]. (c) DCP [4]. (d) Tarel et al.’s [19]. (e) Meng et al.’s [18]. (f) Our proposed method.
Fig. 21
Fig. 21 Comparison of different approaches on Visualization 1 and Visualization 2. (a) Input hazy frames. (b) DLCE [17]. (c) DCP [4]. (d) OCE [8]. (e) MRF [12]. (f) Our proposed method.
Fig. 22
Fig. 22 (a) The input video frame from one of the automated driver-assist systems. (b) The corresponding dehazed video frame of (a).
Fig. 23
Fig. 23 (a) The input video frame from the VIP Data set. (b) The corresponding dehazed video frame of (a).
Fig. 24
Fig. 24 (a) The input video frame from a random camcorder. (b) The corresponding dehazed video frame of (a).
Fig. 25
Fig. 25 (a) Detection result of the original video. (b) Detection result of video processed by the proposed method.

Tables (8)

Tables Icon

Table 1 Computer specification of NVIDIA Jetson TX1 and Renesas R-Car M2

Tables Icon

Table 2 Algorithm 1. The guided filter [9].

Tables Icon

Table 2 Processing performance comparison of TX1 and PC (Unit: fps)

Tables Icon

Table 4 Algorithm 2. The proposed algorithm for the sky estimation.

Tables Icon

Table 3 Processing performance comparison of NVIDIA Jetson TX1 and Renesas R-Car M2 (Unit: fps)

Tables Icon

Table 6 Algorithm 3. The rest part of the guided filter.

Tables Icon

Table 4 Comparison of the processing speed on Intel i7-6900K (Unit: fps)

Tables Icon

Table 8 Algorithm 4. The rest part of the guided filter.

Equations (9)

Equations on this page are rendered with MathJax. Learn more.

I( x )=J( x )t( x )+A( 1t( x ) )
t( x )= e βd( x )
J dark ( x )= min c{ r,g,b } ( min yΩ( x ) J c ( y ) )
min yΩ( x ) ( I c ( y ) )= t ˜ ( x )  min yΩ( x ) ( J c ( y ) )+( 1 t ˜ ( x ) ) A c
t ˜ ( x )=1 ωmin c ( min yΩ( x ) ( I c ( y ) A c ) )
J( x )= I( x )A max( t( x ), t 0 ) +A
t( x )= 1 w( x ) yΩ( x ) G σ s ( xy ) G σ r ( | E x E y | ) t ˜ ( x )
w( x )= yΩ( x ) G σ s ( xy ) G σ r ( | E x E y | )
A new =λ A t1 +( 1λ ) A t