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Applied Intelligence

, Volume 49, Issue 12, pp 4276–4293 | Cite as

Single image dehazing using gradient channel prior

  • Dilbag SinghEmail author
  • Vijay Kumar
  • Manjit Kaur
Article

Abstract

The dehazing techniques designed so far are not so-effective at preserving texture details, especially in case of a complex background and large haze gradient image. Therefore, the exploration of new alternatives for designing an effective prior is desirable. Thus, in this research work, Gradient profile prior (GPP) is designed to evaluate depth map from hazy images. The transmission map is also improved by utilizing Guided anisotropic diffusion and iterative learning based image filter (GADILF). The restoration model is also improved to reduce the effect of pixels saturation and color distortion from restored images. Performance analysis demonstrates that GPP can naturally restore the hazy image especially at the edges of sudden changes in the obtained depth map. Through extensive analysis, it has been found that GPP based dehazing can effectively suppress visual artefacts for hazy images and yield high-quality results as compared to the competitive dehazing techniques both quantitatively and qualitatively. Moreover, the relatively high computational speed of the proposed technique will facilitate it in real-time applications.

Keywords

Haze removal Gradient channel prior Restoration model Transmission map GADILF 

Notes

References

  1. 1.
    Chen B-H, Huang S-C, Li C-Y, Kuo S-Y (2018) Haze removal using radial basis function networks for visibility restoration applications. IEEE Transactions on Neural Networks and Learning Systems 29(8):3828–3838Google Scholar
  2. 2.
    Cai B, Xu X, Jia K, Qing C, Tao D (2016) Dehazenet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198MathSciNetzbMATHGoogle Scholar
  3. 3.
    Chen B-H, Huang S-C (2016) Edge collapse-based dehazing algorithm for visibility restoration in real scenes. J Disp Technol 12(9):964–970Google Scholar
  4. 4.
    Gibson KB, Vo DT, Nguyen TQ (2012) An investigation of dehazing effects on image and video coding. IEEE Trans Image Process 21(2):662–673MathSciNetzbMATHGoogle Scholar
  5. 5.
    Shi L-F, Chen B-H, Huang S-C, Larin AO, Seredin OS, Kopylov AV, Kuo S-Y (2018) Removing haze particles from single image via exponential inference with support vector data description. IEEE Trans Multimedia 20(9):2503–2512Google Scholar
  6. 6.
    Oakley JP, Bu H (2007) Correction of simple contrast loss in color images. IEEE Trans Image Process 16 (2):511–522MathSciNetGoogle Scholar
  7. 7.
    Narasimhan SG, Nayar SK (2000) Chromatic framework for vision in bad weather. In: Cvpr, IEEE, p 1598Google Scholar
  8. 8.
    He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353Google Scholar
  9. 9.
    Bala J, Lakhwani K (2019) Performance evaluation of various desmogging techniques for single smoggy images. Modern Physics Letters B, 1950056Google Scholar
  10. 10.
    Dong C, Loy CC, He K, Tang X (2014) Learning a deep convolutional network for image super-resolution. In: European Conference on Computer Vision, Springer, pp 184– 199Google Scholar
  11. 11.
    Fattal R (2014) Dehazing using color-lines. ACM Trans Graph (TOG) 34(1):13Google Scholar
  12. 12.
    Berman D, Avidan S et al (2016) Non-local image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1674–1682Google Scholar
  13. 13.
    Li B, Peng X, Wang Z, Xu J, Feng D (2017) Aod-net: All-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4770–4778Google Scholar
  14. 14.
    Santra S, Chanda B (2016) Day/night unconstrained image dehazing. In: 2016 23rd International Conference on Pattern Recognition (ICPR), IEEE, pp 1406-1411Google Scholar
  15. 15.
    Koschmieder H (1938) Luftlicht und sichtweite. Naturwissenschaften 26(32):521–528Google Scholar
  16. 16.
    McCartney EJ (1976) Optics of the Atmosphere: Scattering by Molecules and Particles. Wiley, New York, p 421Google Scholar
  17. 17.
    Kushwaha AKS, Srivastava R (2015) Framework for dynamic background modeling and shadow suppression for moving object segmentation in complex wavelet domain. J Electron Imaging 24(5):051005Google Scholar
  18. 18.
    Song Y, Li J, Wang X, Chen X Single image dehazing using ranking convolutional neural network. IEEE Transactions on MultimediaGoogle Scholar
  19. 19.
    Santra S, Mondal R, Chanda B (2018) Learning a patch quality comparator for single image dehazing. IEEE Trans Image Process 27(9):4598–4607.  https://doi.org/10.1109/TIP.2018.2841198 MathSciNetCrossRefGoogle Scholar
  20. 20.
    Liu Q, Gao X, He L, Lu W (2018) Single image dehazing with depth-aware non-local total variation regularization. IEEE Trans Image Process 27(10):5178–5191.  https://doi.org/10.1109/TIP.2018.2849928 MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Li J, Zhang H, Yuan D, Sun M (2015) Single image dehazing using the change of detail prior. Neurocomputing 156:1–11Google Scholar
  22. 22.
    Zhao H, Xiao C, Yu J, Xu X (2015) Single image fog removal based on local extrema. IEEE/CAA Journal of Automatica Sinica 2(2):158–165MathSciNetGoogle Scholar
  23. 23.
    Ge G, Wei Z, Zhao J (2015) Fast single-image dehazing using linear transformation. Optik-International Journal for Light and Electron Optics 126(21):3245–3252Google Scholar
  24. 24.
    Ding M, Wei L (2015) Single-image haze removal using the mean vector l2-norm of rgb image sample window. Optik-International Journal for Light and Electron Optics 126(23):3522– 3528Google Scholar
  25. 25.
    Li Z, Zheng J, Zhu Z, Yao W, Wu S (2015) Weighted guided image filtering. IEEE Trans Image Process 24(1):120–129MathSciNetzbMATHGoogle Scholar
  26. 26.
    Papari G, Idowu N, Varslot T (2016) Fast bilateral filtering for denoising large 3d images. IEEE Trans Image Process 26(1):251–261MathSciNetzbMATHGoogle Scholar
  27. 27.
    Chaudhury KN, Sage D, Unser M (2011) Fast bilateral filtering using trigonometric range kernels. IEEE Trans Image Process 20(12):3376–3382MathSciNetzbMATHGoogle Scholar
  28. 28.
    Anwar MI, Khosla A (2017) Vision enhancement through single image fog removal. Engineering Science and Technology, an International Journal 20(3):1075–1083Google Scholar
  29. 29.
    Cui T, Tian J, Wang E, Tang Y (2017) Single image dehazing by latent region-segmentation based transmission estimation and weighted l1-norm regularisation. IET Image Process 11(2):145–154Google Scholar
  30. 30.
    Wang W, Yuan X, Wu X, Liu Y (2017) Fast image dehazing method based on linear transformation. IEEE Trans Multimedia PP(99):1–1Google Scholar
  31. 31.
    Gao Y, Hu H, Li B, Guo Q, Pu S (2019) Detail preserved single image dehazing algorithm based on airlight refinement. IEEE Trans Multimedia 21(2):351–362.  https://doi.org/10.1109/TMM.2018.2856095 CrossRefGoogle Scholar
  32. 32.
    Baig N, Riaz MM, Ghafoor A, Siddiqui AM (2016) Image dehazing using quadtree decomposition and entropy-based contextual regularization. IEEE Signal Process Lett 23(6):853–857.  https://doi.org/10.1109/LSP.2016.2559805 CrossRefGoogle Scholar
  33. 33.
    Santra S, Mondal R, Chanda B (2018) Learning a patch quality comparator for single image dehazing. IEEE Trans Image Process 27(9):4598–4607MathSciNetGoogle Scholar
  34. 34.
    Liu Q, Gao X, He L, Lu W (2018) Single image dehazing with depth-aware non-local total variation regularization. IEEE Trans Image Process 27(10):5178–5191MathSciNetzbMATHGoogle Scholar
  35. 35.
    Ancuti C, Ancuti CO (2014) Effective contrast-based dehazing for robust image matching. IEEE Geosci Remote Sens Lett 11(11):1871–1875.  https://doi.org/10.1109/LGRS.2014.2312314 CrossRefGoogle Scholar
  36. 36.
    Schechner YY, Narasimhan SG, Nayar SK (2003) Polarization-based vision through haze. Appl Opt 42 (3):511–525Google Scholar
  37. 37.
    Kopf J, Neubert B, Chen B, Cohen M, Cohen-Or D, Deussen O, Uyttendaele M, Lischinski D (2008) Deep photo: Model-based photograph enhancement and viewing, Vol. 27 ACMGoogle Scholar
  38. 38.
    Tan RT Visibility in bad weather from a single imageGoogle Scholar
  39. 39.
    Fattal R (2008) Single image dehazing. ACM Trans Graph (TOG) 27(3):72Google Scholar
  40. 40.
    Tarel J-P, Hautiere N (2009) Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference on Computer Vision (ICCV), IEEE, pp 2201–2208Google Scholar
  41. 41.
    Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24(11):3522–3533MathSciNetzbMATHGoogle Scholar
  42. 42.
    Middleton W (1957) Vision through the atmosphere in geophysik ii/geophysics iiGoogle Scholar
  43. 43.
    Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE International Conference on Computer Vision, pp 617–624Google Scholar
  44. 44.
    Omer I, Werman M (2004) Color lines: image specific color representation. In: Null, IEEE, pp 946–953Google Scholar
  45. 45.
    Shu Q, Wu C, Liu RW, Chui KT, Xiong S (2018) Two-phase transmission map estimation for robust image dehazing. In: International Conference on Neural Information Processing, Springer, pp 529–541Google Scholar
  46. 46.
    Chen C, Do MN, Wang J (2016) Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In: European Conference on Computer Vision, Springer, pp 576–591Google Scholar
  47. 47.
    Liu Y, Li H, Wang M (2017) Single image dehazing via large sky region segmentation and multiscale opening dark channel model. IEEE Access 5:8890–8903Google Scholar
  48. 48.
    Zhang L, Wang S, Wang X (2018) Saliency-based dark channel prior model for single image haze removal. IET Image Process 12(6):1049–1055Google Scholar
  49. 49.
    Li J, Hu Q, Ai M Haze and thin cloud removal via sphere model improved dark channel prior. IEEE Geoscience and Remote Sensing LettersGoogle Scholar
  50. 50.
    Li C, Guo J, Porikli F, Fu H, Pang Y (2018) A cascaded convolutional neural network for single image dehazing. IEEE Access 6:24877–24887Google Scholar
  51. 51.
    Yang D, Sun J (2018) Proximal dehaze-net: a prior learning-based deep network for single image dehazing. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 702–717Google Scholar
  52. 52.
    Ren W, Liu S, Zhang H, Pan J, Cao X, Yang M-H (2016) Single image dehazing via multi-scale convolutional neural networks. In: European Conference on Computer Vision, Springer, pp 154–169Google Scholar
  53. 53.
    Ren W, Ma L, Zhang J, Pan J, Cao X, Liu W, Yang M-H (2018) Gated fusion network for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3253–3261Google Scholar
  54. 54.
    Su A, Sun X, Zhang Y, Yu Q (2016) Efficient rotation-invariant histogram of oriented gradient descriptors for car detection in satellite images. IET Comput Vis 10(7):634–640.  https://doi.org/10.1049/iet-cvi.2015.0333 CrossRefGoogle Scholar
  55. 55.
    Singh D, Kumar V (2019) Image dehazing using moore neighborhood-based gradient profile prior. Signal Process Image Commun 70:131–144Google Scholar
  56. 56.
    Liu G, Zhou Z, Zhong H, Xie S (2014) Gradient descent with adaptive momentum for active contour models. IET Comput Vis 8(4):287–298.  https://doi.org/10.1049/iet-cvi.2013.0089 CrossRefGoogle Scholar
  57. 57.
    Kiani A, Sahebi MR (2015) Edge detection based on the shannon entropy by piecewise thresholding on remote sensing images. IET Comput Vis 9(5):758–768.  https://doi.org/10.1049/iet-cvi.2013.0192 CrossRefGoogle Scholar
  58. 58.
    Dong H, Dong S (2014) Image-based surface deformation for multi-view three-dimensional facial reconstruction. IET Comput Vis 8(6):498–509.  https://doi.org/10.1049/iet-cvi.2013.0188 CrossRefGoogle Scholar
  59. 59.
    Lee WY, Li CY, Yen JY (2017) Integrating wavelet transformation with markov random field analysis for the depth estimation of light-field images. IET Comput Vis 11(5):358–367.  https://doi.org/10.1049/iet-cvi.2016.0151 CrossRefGoogle Scholar
  60. 60.
    Srivastava R, Prakash O, Khare A (2016) Local energy-based multimodal medical image fusion in curvelet domain. IET Comput Vis 10(6):513–527.  https://doi.org/10.1049/iet-cvi.2015.0251 CrossRefGoogle Scholar
  61. 61.
    Li Z, Zheng J (2015) Edge-preserving decomposition-based single image haze removal. IEEE Trans Image Process 24(12):5432–5441MathSciNetzbMATHGoogle Scholar
  62. 62.
    Zuo W, Zhang L, Song C, Zhang D, Gao H (2014) Gradient histogram estimation and preservation for texture enhanced image denoising. IEEE Trans Image Process 23(6):2459–2472MathSciNetzbMATHGoogle Scholar
  63. 63.
    Caye Daudt R, Le Saux B, Boulch A, Gousseau Y (2019) Guided anisotropic diffusion and iterative learning for weakly supervised change detection. In: Computer Vision and Pattern Recognition WorkshopsGoogle Scholar
  64. 64.
    Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z Reside: A benchmark for single image dehazing. arXiv:1712.04143
  65. 65.
    Tarel J-P, Hautiere N, Cord A, Gruyer D, Halmaoui H (2010) Improved visibility of road scene images under heterogeneous fog. In: Intelligent vehicles symposium (IV), 2010 IEEE, Citeseer, pp 478–485Google Scholar
  66. 66.
    Tarel J-P, Hautiere N, Caraffa L, Cord A, Halmaoui H, Gruyer D (2012) Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell Transp Syst Mag 4(2):6–20Google Scholar
  67. 67.
    Kede Ma WL, Wang Z (2015) Perceptual evaluation of single image dehazing algorithms. In: Image Processing, Proc, IEEE Citeseer, pp 3600–3604Google Scholar
  68. 68.
    Cosmin Ancuti CDV, Ancuti CO (2016) D-hazy: A dataset to evaluate quantitatively dehazing algorithms. In: IEEE International Conference on Image Processing (ICIP), ICIP’16, pp 2226–2230Google Scholar
  69. 69.
    Hautiere N, Tarel J-P, Aubert D, Dumont E (2011) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis & Stereology 27(2):87–95MathSciNetzbMATHGoogle Scholar
  70. 70.
    Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24(11):3888–3901MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering Department, Apex Institute of TechnologyChandigarh UniversityMohali (Pb.)India
  2. 2.Computer Science and Engineering DepartmentThapar Institute of Engineering and TechnologyPatiala (Pb.)India

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