http://iet.metastore.ingenta.com
1887

Single image dehazing and denoising combining dark channel prior and variational models

Single image dehazing and denoising combining dark channel prior and variational models

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Single image dehazing and denoising models can simultaneously remove haze and noise with high efficiency. Here, the authors propose three variational models combining the celebrated dark channel prior (DCP) and total variations (TV) models for image dehazing and denoising. The authors firstly estimate the transmission map associated with depth using DCP, then design three variational models for colour image dehazing and denoising based on this estimation and the layered total variation (LTV) regulariser, multichannel total variation (MTV) regulariser, and colour total variation (CTV) regulariser, respectively. In order to improve the computation efficiency of the three models, the authors design their fast split Bregman algorithms via introducing some auxiliary variables and the Bregman iterative parameters. Numerous experiments are presented to compare their denoising effects, edge-preserving properties, and computation efficiencies. To demonstrate the merits of the proposed models, the authors also conduct some comparisons with several existing state-of-the-art methods. Numerical results further prove that the LTV-based model is fastest, and the CTV model is the best for denoising with edge-preserving, and it also leads to the best visually haze-free and noise-free images.

References

    1. 1)
      • K. He , J. Sun , X. Tang .
        1. He, K., Sun, J., Tang, X.: ‘Single image haze removal using dark channel prior’, Trans. Pattern Anal. Mach. Intell., 2010, 33, (12), pp. 23412353.
        . Trans. Pattern Anal. Mach. Intell. , 12 , 2341 - 2353
    2. 2)
      • R. Fattal .
        2. Fattal, R.: ‘Single image dehazing’, ACM Trans. Graph., 2008, 72, (3), pp. 19.
        . ACM Trans. Graph. , 3 , 1 - 9
    3. 3)
      • L. Zeng , Y.Z.. Dai .
        3. Zeng, L., Dai, Y.Z..: ‘Single image dehazing based on combining dark channel prior and scene radiance constraint’, IET Image Process., 2016, 25, (6), pp. 11141120.
        . IET Image Process. , 6 , 1114 - 1120
    4. 4)
      • F. Fang , F. Li , X. Yang .
        4. Fang, F., Li, F., Yang, X., et al: ‘Single image dehazing and denoising with variational method’. IEEE Int. Conf. Image Analysis and Signal Processing (IASP), April 2010, pp. 911.
        . IEEE Int. Conf. Image Analysis and Signal Processing (IASP) , 9 - 11
    5. 5)
      • F. Fang , F. Li , T. Zeng .
        5. Fang, F., Li, F., Zeng, T.: ‘Single image dehazing and denoising: a fast variational approach’, SIAM J. Imag. Sci., 2014, 7, (2), pp. 969996.
        . SIAM J. Imag. Sci. , 2 , 969 - 996
    6. 6)
      • J. Yang , W. Yin , Y. Zhang .
        6. Yang, J., Yin, W., Zhang, Y., et al: ‘A fast algorithm for edge-preserving variational multichannel image restoration’, SIAM J. Imag. Sci., 2009, 2, (2), pp. 569592.
        . SIAM J. Imag. Sci. , 2 , 569 - 592
    7. 7)
      • X. Bresson , T. Chan .
        7. Bresson, X., Chan, T.: ‘Fast minimization of the vectorial total variation norm and applications to color image processing’, Inverse Probl. Imaging, 2008, 2, (4), pp. 455484.
        . Inverse Probl. Imaging , 4 , 455 - 484
    8. 8)
      • J. Aujol , S. Kang .
        8. Aujol, J., Kang, S.: ‘Color image decomposition and restoration’, J. Vis. Commun. Image Represent., 2006, 17, (4), pp. 916928.
        . J. Vis. Commun. Image Represent. , 4 , 916 - 928
    9. 9)
      • V. Duval , J. Aujol , L. Vese .
        9. Duval, V., Aujol, J., Vese, L.: ‘A projected gradient algorithm for color image decomposition’, J. Math. Imaging Vision., 2010, 37, (3), pp. 232248.
        . J. Math. Imaging Vision. , 3 , 232 - 248
    10. 10)
      • T. Chan , J. Shen . (2005)
        10. Chan, T., Shen, J.: ‘Image processing and analysis, variational, PDE, wavelet and stochastic methods’ (SIAM, Pennsylvania, USA, 2005) .
        .
    11. 11)
      • G. Aubert , P. Kornprobst . (2006)
        11. Aubert, G., Kornprobst, P.: ‘Mathematical problems in image processing: partial differential equations and the calculus of variations’ (Springer, New York, 2006, 2nd edn.).
        .
    12. 12)
      • A. Tikhonov .
        12. Tikhonov, A.: ‘Regularization of incorrectly posed problems’, Sov. Math. Doklady, 1963, 4, (6), pp. 16241627.
        . Sov. Math. Doklady , 6 , 1624 - 1627
    13. 13)
      • L. Rudin , S. Osher , F. Fatemi .
        13. Rudin, L., Osher, S., Fatemi, F.: ‘Nonlinear total variation based noise removal algorithms’, Physica D: Nonlinear Phenomena, 1992, 60, (1–4), pp. 259268.
        . Physica D: Nonlinear Phenomena , 259 - 268
    14. 14)
      • P. Blomgren , T. Chan .
        14. Blomgren, P., Chan, T.: ‘Color TV: total variation methods for restoration of vector-valued images’, IEEE Trans. Image Process., 1998, 7, (3), pp. 304309.
        . IEEE Trans. Image Process. , 3 , 304 - 309
    15. 15)
      • T. Goldstein , S. Osher .
        15. Goldstein, T., Osher, S.: ‘The split Bregman algorithm for L1 regularized problems’, SIAM J. Imaging Sci., 2009, 2, (2), pp. 323343.
        . SIAM J. Imaging Sci. , 2 , 323 - 343
    16. 16)
      • R. Kimmel , M. Elad , D. Shaked .
        16. Kimmel, R., Elad, M., Shaked, D., et al: ‘A variational framework for Retinex’, Int. J. Comput. Vis., 2003, 52, (1), pp. 723.
        . Int. J. Comput. Vis. , 1 , 7 - 23
    17. 17)
      • H. Liu , J. Yang , Z. Wu .
        17. Liu, H., Yang, J., Wu, Z., et al: ‘A fast single image dehazing method based on dark channel prior and Retinex theory’, Acta Autom. Sin., 2015, 41, (7), pp. 12641273.
        . Acta Autom. Sin. , 7 , 1264 - 1273
    18. 18)
      • Z. Rao , T. Xu , H. Wang .
        18. Rao, Z., Xu, T., Wang, H.: ‘Mission-critical monitoring based on surround suppression variational Retinex enhancement for non-uniform illumination images’, Eurasip J. Wirel. Commun. Netw., Com Netw. (2017), 2017, 88, pp. 112.
        . Eurasip J. Wirel. Commun. Netw., Com Netw. (2017) , 1 - 12
    19. 19)
      • M. Bertalmío .
        19. Bertalmío, M.: ‘Connections between Retinex, neural models and variational methods’. IS&T Int. Symp. on Electronic Imaging Science and Technology, San Francisco, USA, February 2016, pp. 1418.
        . IS&T Int. Symp. on Electronic Imaging Science and Technology , 14 - 18
    20. 20)
      • A. Chambolle .
        20. Chambolle, A.: ‘An algorithm for total variation minimization and applications’, J. Math. Imaging Vis., 2004, 20, (1–2), pp. 8997.
        . J. Math. Imaging Vis. , 89 - 97
    21. 21)
      • A. Chambolle , A. Pock .
        21. Chambolle, A., Pock, A.: ‘A first-order primal-dual algorithm for convex problems with applications to imaging’, J. Math. Imaging Vis., 2011, 40, (1), pp. 120145.
        . J. Math. Imaging Vis. , 1 , 120 - 145
    22. 22)
      • C. Wu , X. Tai .
        22. Wu, C., Tai, X.: ‘Augmented lagrangian method, dual methods, and split Bregman iteration for ROF, vectorial TV, and high order models’, SIAM J. Imaging Sci., 2010, 3, (3), pp. 300339.
        . SIAM J. Imaging Sci. , 3 , 300 - 339
    23. 23)
      • T. Goldstein , B. O'Donoghue , S. Setzer .
        23. Goldstein, T., O'Donoghue, B., Setzer, S., et al: ‘Fast alternating direction optimization methods’, SIAM J. Imaging Sci., 2014, 7, (3), pp. 15881623.
        . SIAM J. Imaging Sci. , 3 , 1588 - 1623
    24. 24)
      • Y. Yu , Z. Pan , W. Wei .
        24. Yu, Y., Pan, Z., Wei, W., et al: ‘Comparison of the edge preserving capabilities of different variational models for vectorial image denoising’, J. Image Graph., 2011, 16, (12), pp. 22232230.
        . J. Image Graph. , 12 , 2223 - 2230
    25. 25)
      • G. Meng , Y. Wang , J. Duan .
        25. Meng, G., Wang, Y., Duan, J., et al: ‘Efficient image dehazing with boundary constraint and contextual regularization’. Proc. of the IEEE Int. Conf. on Computer Vision, 2013.
        . Proc. of the IEEE Int. Conf. on Computer Vision
    26. 26)
      • L. Choi , J. You , C. Alan .
        26. Choi, L., You, J., Alan, C.: ‘Referenceless prediction of perceptual fog density and perceptual image defogging’, IEEE Trans. Image Process., 2015, 24, (11), pp. 38883901.
        . IEEE Trans. Image Process. , 11 , 3888 - 3901
    27. 27)
      • N. Hautière , J. Tarel , D. Aubert .
        27. Hautière, N., Tarel, J., Aubert, D., et al: ‘Blind contrast enhancement assessment by gradient ratioing at visible edges’, Image Anal. Stereol., 2011, 27, (2), pp. 8795.
        . Image Anal. Stereol. , 2 , 87 - 95
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2017.0318
Loading

Related content

content/journals/10.1049/iet-cvi.2017.0318
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address