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

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

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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)
      • 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.
    2. 2)
      • 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.
    3. 3)
      • 10. Chan, T., Shen, J.: ‘Image processing and analysis, variational, PDE, wavelet and stochastic methods’ (SIAM, Pennsylvania, USA, 2005) .
    4. 4)
      • 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.
    5. 5)
      • 16. Kimmel, R., Elad, M., Shaked, D., et al: ‘A variational framework for Retinex’, Int. J. Comput. Vis., 2003, 52, (1), pp. 723.
    6. 6)
      • 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.
    7. 7)
      • 23. Goldstein, T., O'Donoghue, B., Setzer, S., et al: ‘Fast alternating direction optimization methods’, SIAM J. Imaging Sci., 2014, 7, (3), pp. 15881623.
    8. 8)
      • 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.
    9. 9)
      • 12. Tikhonov, A.: ‘Regularization of incorrectly posed problems’, Sov. Math. Doklady, 1963, 4, (6), pp. 16241627.
    10. 10)
      • 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.
    11. 11)
      • 15. Goldstein, T., Osher, S.: ‘The split Bregman algorithm for L1 regularized problems’, SIAM J. Imaging Sci., 2009, 2, (2), pp. 323343.
    12. 12)
      • 2. Fattal, R.: ‘Single image dehazing’, ACM Trans. Graph., 2008, 72, (3), pp. 19.
    13. 13)
      • 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.
    14. 14)
      • 13. Rudin, L., Osher, S., Fatemi, F.: ‘Nonlinear total variation based noise removal algorithms’, Physica D: Nonlinear Phenomena, 1992, 60, (1–4), pp. 259268.
    15. 15)
      • 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.
    16. 16)
      • 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.
    17. 17)
      • 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.
    18. 18)
      • 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.
    19. 19)
      • 8. Aujol, J., Kang, S.: ‘Color image decomposition and restoration’, J. Vis. Commun. Image Represent., 2006, 17, (4), pp. 916928.
    20. 20)
      • 9. Duval, V., Aujol, J., Vese, L.: ‘A projected gradient algorithm for color image decomposition’, J. Math. Imaging Vision., 2010, 37, (3), pp. 232248.
    21. 21)
      • 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.
    22. 22)
      • 20. Chambolle, A.: ‘An algorithm for total variation minimization and applications’, J. Math. Imaging Vis., 2004, 20, (1–2), pp. 8997.
    23. 23)
      • 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.
    24. 24)
      • 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.
    25. 25)
      • 11. Aubert, G., Kornprobst, P.: ‘Mathematical problems in image processing: partial differential equations and the calculus of variations’ (Springer, New York, 2006, 2nd edn.).
    26. 26)
      • 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.
    27. 27)
      • 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.
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