Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 12, pp 15409–15430 | Cite as

Color and sharpness assessment of single image dehazing

  • Jessica El Khoury
  • Steven Le Moan
  • Jean-Baptiste Thomas
  • Alamin Mansouri
Article

Abstract

Image dehazing is the process of enhancing a color image of a natural scene that contains an undesirable veil of fog for visualization or as a pre-processing step for computer vision systems. In this work, we investigate the performances of eleven state-of-the-art image quality metrics in evaluating dehazed images, and discuss challenges in designing an efficient dehazing evaluation metric. This is done through a composite study based on the agreement between subjective and objective evaluations. Accordingly, we evaluate five state-of-the-art dehazing algorithms. We use two semi-indoor scenes, degraded with several levels of fog. One important aspect of these scenes is that the fog-free images are available and can therefore serve as ground-truth data for dehazing methods evaluation. This study shows that the best working dehazing method depends on the density of fog. There seems to be a clear distinction between what people perceive as good quality in terms of color restoration and in terms of sharpness restoration. Most metrics show limitations in providing proper quality prediction of dehazing. According to the introduction and analysis, a contribution of this work is to point out the flaws in the evaluation and development of dehazing methods. Our observations might be considered when designing efficient methods and metrics dedicated to image dehazing.

Keywords

Single image dehazing Color Sharpness Image quality assessment Objective assessment Psychometric experiment 

Notes

Acknowledgements

The authors thank the Open Food System project as well as the National Research Council of Norway for funding. Open Food System is a research project supported by Vitagora, Cap Digital, Imaginove, Aquimer, Microtechnique and Agrimip, funded by the French State and the Franche-Comté Region as part of The Investments for the Future Programme managed by Bpifrance, www.openfoodsystem.fr.

References

  1. 1.
    Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22(8):3271–3282CrossRefGoogle Scholar
  2. 2.
    Ancuti C, Ancuti CO, De Vleeschouwer C (2016) D-hazy: a dataset to evaluate quantitatively dehazing algorithms. In: 2016 IEEE international conference on image processing (ICIP). IEEE, pp 2226–2230Google Scholar
  3. 3.
    Anitharani M (2013) Haze removal of secure remote surveillance system. IOSR J Eng 3:10–17Google Scholar
  4. 4.
    Brown L, Li X (2005) Confidence intervals for two sample binomial distribution. J Stat Plan Inference 130(1):359–375MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Chandler D M (2013) Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Processing 2013Google Scholar
  6. 6.
    Chen Z, Jiang T, Tian Y (2014) Quality assessment for comparing image enhancement algorithms. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 3003–3010Google Scholar
  7. 7.
    CHIC (Color Hazy Image for Comparison). http://chic.u-bourgogne.fr
  8. 8.
    Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24(11):3888–3901MathSciNetCrossRefGoogle Scholar
  9. 9.
    Drews P, do Nascimento E, Moraes F, Botelho S, Campos M (2013) Transmission estimation in underwater single images. In: 2013 IEEE international conference on computer vision workshops (ICCVW). IEEE, pp 825–830Google Scholar
  10. 10.
    El Khoury J, Thomas J-B, Mansouri A (2014) Does dehazing model preserve color information? In: 2014 tenth international conference on signal-image technology and internet-based systems (SITIS). IEEE, pp 606–613Google Scholar
  11. 11.
    El Khoury J, Thomas J-B, Mansouri A (2015) Haze and convergence models: experimental comparison. In: AIC 2015Google Scholar
  12. 12.
    El Khoury J, Thomas J-B, Mansouri A (2016) A color image database for haze model and dehazing methods evaluation. In: International conference on image and signal processing. Springer, pp 109–117Google Scholar
  13. 13.
    Fang S, Yang J, Zhan J, Yuan H, Rao R (2011) Image quality assessment on image haze removal. In: Control and decision conference (CCDC), 2011 Chinese. IEEE, pp 610–614Google Scholar
  14. 14.
    Fleyeh H, Dougherty M (2005) Road and traffic sign detection and recognition. In: Proceedings of the 16th Mini-EURO conference and 10th meeting of EWGT, pp 644–653Google Scholar
  15. 15.
    Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M (2014) A variational framework for single image dehazing. In: Computer vision-ECCV 2014 workshops. Springer, pp 259–270Google Scholar
  16. 16.
    Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M (2015) Enhanced variational image dehazing. Appl Opt 27:25zbMATHGoogle Scholar
  17. 17.
    Guo F, Tang J, Cai Z (2014) Objective measurement for image defogging algorithms. J Cent South Univ 21:272–286CrossRefGoogle Scholar
  18. 18.
    Hautière N, Tarel J-P, Aubert D, Dumont E et al (2008) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol J 27(2):87–95MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    He K, Sun J , Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353CrossRefGoogle Scholar
  20. 20.
    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409CrossRefGoogle Scholar
  21. 21.
    Huang K-Q, Wang Q, Wu Z-Y (2006) Natural color image enhancement and evaluation algorithm based on human visual system. Comput Vis Image Underst 103 (1):52–63CrossRefGoogle Scholar
  22. 22.
    ITU-R BT.500-12. Recommendation: methodology for the subjective assessment of the quality of television pictures (1993)Google Scholar
  23. 23.
    Koschmieder H (1925) Theorie der horizontalen Sichtweite: Kontrast und Sichtweite, Keim & Nemnich, MunichGoogle Scholar
  24. 24.
    Le Moan S, Urban P (2014) Image-difference prediction: from color to spectral. IEEE Trans Image Process 23(5):2058–2068MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Le Moan S, Preiss J, Urban P (2015) Evaluating the multi-Scale iCID metric. In: Larabi M-C, Triantaphillidou S (eds) Image quality and system performance XII, vol 9396, San Francisco, February. SPIE, pp 9096–38Google Scholar
  26. 26.
    Lissner I, Preiss J, Urban P, Lichtenauer MS, Zolliker P (2013) Image-difference prediction: from grayscale to color. IEEE Trans Image Process 22(2):435–446MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Liu X, Hardeberg JY (2013) Fog removal algorithms: survey and perceptual evaluation. In: 2013 4th European workshop on visual information processing (EUVIP). IEEE, pp 118–123Google Scholar
  28. 28.
    Liu S, Rahman MA, Wong CY, Lin SCF, Jiang G, Kwok N (2015) Dark channel prior based image de-hazing: a review. In: 2015 5th international conference on information science and technology (ICIST). IEEE, pp 345–350Google Scholar
  29. 29.
    Lu H, Li Y, Nakashima S, Serikawa S (2016) Single image dehazing through improved atmospheric light estimation. Multimed Tools Appl 75(24):17081–17096CrossRefGoogle Scholar
  30. 30.
    Lüthen J, Wörmann J, Kleinsteuber M, Johannes SA (2017) A rgb/nir data set for evaluating dehazing algorithms. Electron Imaging 2017(12):79–87CrossRefGoogle Scholar
  31. 31.
    Ma K, Liu W, Wang Z (2015) Perceptual evaluation of single image dehazing algorithms. In: IEEE international conference on image processingGoogle Scholar
  32. 32.
    Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Eighth IEEE international conference on computer vision, 2001. ICCV 2001. Proceedings, vol 2. IEEE, pp 416– 423Google Scholar
  33. 33.
    Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyser. IEEE Signal Process Lett 22:209–212CrossRefGoogle Scholar
  34. 34.
    Narasimhan SG, Nayar SK (2000) Chromatic framework for vision in bad weather. In: IEEE conference on computer vision and pattern recognition, 2000. Proceedings, vol 1. IEEE, pp 598–605Google Scholar
  35. 35.
    Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724CrossRefGoogle Scholar
  36. 36.
    Nayar SK, Narasimhan SG (1999) Chromatic framework for vision in bad weather. In: The proceedings of the seventh IEEE international conference on computer vision, 1999, vol 2. IEEE, pp 820–827Google Scholar
  37. 37.
    Ngo KV, Storvik JJ, Dokkeberg CA, Farup I, Pedersen M (2015) Quickeval: a web application for psychometric scaling experiments. In: IS&T/SPIE electronic imaging. International Society for Optics and Photonics, pp 93960O–93960OGoogle Scholar
  38. 38.
    Pang J, Au OC, Guo Z (2011) Improved single image dehazing using guided filter. In: Asia-Pacific signal and information processing association annual summit and conference 2011Google Scholar
  39. 39.
    Pettersson N (2013) Gpu-accelerated real-time surveillance de-weatheringGoogle Scholar
  40. 40.
    Pierre F, Migerditichan P (2015) Débrumage variationnel. In: GRETSIGoogle Scholar
  41. 41.
    Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, Romeny BH, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368CrossRefGoogle Scholar
  42. 42.
    Sahu C, Sahu R (2014) Comparative study on fusion based image dehazing. Int J Adv Res Comput Commun Eng 3:7057–7060Google Scholar
  43. 43.
    Sathya R, Bharathi M, Dhivyasri G (2015) Underwater image enhancement by dark channel prior. In: 2015 2nd international conference on electronics and communication systems (ICECS). IEEE, pp 1119–1123Google Scholar
  44. 44.
    Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444CrossRefGoogle Scholar
  45. 45.
    Sheikh HR, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15 (11):3440–3451CrossRefGoogle Scholar
  46. 46.
    Shen W, Hao S, Qian J, Li L (2017) Blind quality assessment of dehazed images by analyzing information, contrast, and luminance. J Netw Intell 2(1):139–146Google Scholar
  47. 47.
    Song Y, Luo H, Lu R, Ma J (2017) Dehazed image quality assessment by haze-line theory. J Phys: Conf Ser 844(1):012045Google Scholar
  48. 48.
    Sun W (2013) A new single-image fog removal algorithm based on physical model. Optik-Int J Light Electron Opt 124(21):4770–4775CrossRefGoogle Scholar
  49. 49.
    Tarel J-P, Hautière N (2009) Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 2201–2208Google Scholar
  50. 50.
    Tarel J-P, Hautière N, Caraffa L, Cord A, Halmaoui H, Gruyer D (2012) Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell Transp Syst Mag 4(2):6–20CrossRefGoogle Scholar
  51. 51.
    Ullah E, Nawaz R, Iqbal J (2013) Single image haze removal using improved dark channel prior. In: 2013 Proceedings of international conference on modelling, identification & control (ICMIC). IEEE, pp 245–248Google Scholar
  52. 52.
    Wang Y, Wu B (2010) Improved single image dehazing using dark channel prior. In: 2010 IEEE international conference on intelligent computing and intelligent systems (ICIS), vol 2. IEEE, pp 789–792Google Scholar
  53. 53.
    Wang Z, Simoncelli EP, Bovik AC (2003) Multiscale structural similarity for image quality assessment. In: Conference record of the thirty-seventh Asilomar conference on signals, systems and computers, 2004, vol 2. IEEE, pp 1398–1402Google Scholar
  54. 54.
    Wang L, Xie W, Pei J (2015) Patch-based dark channel prior dehazing for RS multi-spectral image. Chin J Electron 24(3):573–578CrossRefGoogle Scholar
  55. 55.
    Wang W, Yuan X, Wu X, Liu Y (2017) Dehazing for images with large sky region. Neurocomputing 238:365–376CrossRefGoogle Scholar
  56. 56.
    Xu Z, Liu X, Ji N (2009) Fog removal from color images using contrast limited adaptive histogram equalization. In: 2nd international congress on image and signal processing, 2009. CISP’09. IEEE, pp 1–5Google Scholar
  57. 57.
    Xu H, Guo J, Liu Q, Ye L (2012) Fast image dehazing using improved dark channel prior. In: 2012 international conference on information science and technology (ICIST). IEEE, pp 663–667Google Scholar
  58. 58.
    Yendrikhovski SN, Blommaert FJJ, de Ridder H (1998) Perceptually optimal color reproduction. In: Photonics West’98 electronic imaging. International Society for Optics and Photonics, pp 274–281Google Scholar
  59. 59.
    Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386MathSciNetCrossRefzbMATHGoogle Scholar
  60. 60.
    Zhang H, Liu Q, Wu Y, Yang F (2013) Single image dehazing combining physics model based and non-physics model based methods. J Comput Inf Syst 9 (4):1623–1631Google Scholar
  61. 61.
    Zhang L, Shen Y, Li H (2014) VSI: a visual saliency-induced index for perceptual image quality assessment. IEEE Trans Image Process 23(10):4270–4281MathSciNetCrossRefzbMATHGoogle Scholar
  62. 62.
    Zhu Q, Mai J, Shao L (2014) Single image dehazing using color attenuation prior. In: Proceedings of the BMVCGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Jessica El Khoury
    • 1
  • Steven Le Moan
    • 2
  • Jean-Baptiste Thomas
    • 1
  • Alamin Mansouri
    • 1
  1. 1.LE2I LaboratoryUniversité de Bourgogne Franche-ComtéDijonFrance
  2. 2.The Norwegian Colour and Visual Computing LaboratoryNTNU in GjøvikGjøvikNorway

Personalised recommendations