Skip to main content
SpringerLink
Log in

Color and sharpness assessment of single image dehazing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22(8):3271–3282

    Article  Google Scholar 

  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 2226–2230

  3. Anitharani M (2013) Haze removal of secure remote surveillance system. IOSR J Eng 3:10–17

    Google Scholar 

  4. Brown L, Li X (2005) Confidence intervals for two sample binomial distribution. J Stat Plan Inference 130(1):359–375

    Article  MathSciNet  MATH  Google Scholar 

  5. Chandler D M (2013) Seven challenges in image quality assessment: past, present, and future research. ISRN Signal Processing 2013

  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 3003–3010

  7. CHIC (Color Hazy Image for Comparison). http://chic.u-bourgogne.fr

  8. Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24(11):3888–3901

    Article  MathSciNet  Google Scholar 

  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 825–830

  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 606–613

  11. El Khoury J, Thomas J-B, Mansouri A (2015) Haze and convergence models: experimental comparison. In: AIC 2015

  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 109–117

  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 610–614

  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 644–653

  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 259–270

  16. Galdran A, Vazquez-Corral J, Pardo D, Bertalmío M (2015) Enhanced variational image dehazing. Appl Opt 27:25

    MATH  Google Scholar 

  17. Guo F, Tang J, Cai Z (2014) Objective measurement for image defogging algorithms. J Cent South Univ 21:272–286

    Article  Google Scholar 

  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):87–95

    Article  MathSciNet  MATH  Google Scholar 

  19. He K, Sun J , Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353

    Article  Google Scholar 

  20. He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  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):52–63

    Article  Google Scholar 

  22. ITU-R BT.500-12. Recommendation: methodology for the subjective assessment of the quality of television pictures (1993)

  23. Koschmieder H (1925) Theorie der horizontalen Sichtweite: Kontrast und Sichtweite, Keim & Nemnich, Munich

  24. Le Moan S, Urban P (2014) Image-difference prediction: from color to spectral. IEEE Trans Image Process 23(5):2058–2068

    Article  MathSciNet  MATH  Google Scholar 

  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 9096–38

  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):435–446

    Article  MathSciNet  MATH  Google Scholar 

  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 118–123

  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 345–350

  29. Lu H, Li Y, Nakashima S, Serikawa S (2016) Single image dehazing through improved atmospheric light estimation. Multimed Tools Appl 75(24):17081–17096

    Article  Google Scholar 

  30. Lüthen J, Wörmann J, Kleinsteuber M, Johannes SA (2017) A rgb/nir data set for evaluating dehazing algorithms. Electron Imaging 2017(12):79–87

    Article  Google Scholar 

  31. Ma K, Liu W, Wang Z (2015) Perceptual evaluation of single image dehazing algorithms. In: IEEE international conference on image processing

  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– 423

  33. Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyser. IEEE Signal Process Lett 22:209–212

    Article  Google Scholar 

  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 598–605

  35. Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell 25(6):713–724

    Article  Google Scholar 

  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 820–827

  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–93960O

  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 2011

  39. Pettersson N (2013) Gpu-accelerated real-time surveillance de-weathering

  40. Pierre F, Migerditichan P (2015) Débrumage variationnel. In: GRETSI

  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):355–368

    Article  Google Scholar 

  42. Sahu C, Sahu R (2014) Comparative study on fusion based image dehazing. Int J Adv Res Comput Commun Eng 3:7057–7060

    Google Scholar 

  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 1119–1123

  44. Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15(2):430–444

    Article  Google Scholar 

  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):3440–3451

    Article  Google Scholar 

  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):139–146

    Google Scholar 

  47. Song Y, Luo H, Lu R, Ma J (2017) Dehazed image quality assessment by haze-line theory. J Phys: Conf Ser 844(1):012045

    Google Scholar 

  48. Sun W (2013) A new single-image fog removal algorithm based on physical model. Optik-Int J Light Electron Opt 124(21):4770–4775

    Article  Google Scholar 

  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 2201–2208

  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):6–20

    Article  Google Scholar 

  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 245–248

  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 789–792

  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 1398–1402

  54. Wang L, Xie W, Pei J (2015) Patch-based dark channel prior dehazing for RS multi-spectral image. Chin J Electron 24(3):573–578

    Article  Google Scholar 

  55. Wang W, Yuan X, Wu X, Liu Y (2017) Dehazing for images with large sky region. Neurocomputing 238:365–376

    Article  Google Scholar 

  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 1–5

  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 663–667

  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 274–281

  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):2378–2386

    Article  MathSciNet  MATH  Google Scholar 

  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):1623–1631

    Google Scholar 

  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):4270–4281

    Article  MathSciNet  MATH  Google Scholar 

  62. Zhu Q, Mai J, Shao L (2014) Single image dehazing using color attenuation prior. In: Proceedings of the BMVC

Download references

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.

Author information

Authors and Affiliations

  1. LE2I Laboratory, Université de Bourgogne Franche-Comté, Dijon, France

    Jessica El Khoury, Jean-Baptiste Thomas & Alamin Mansouri

  2. The Norwegian Colour and Visual Computing Laboratory, NTNU in Gjøvik, Gjøvik, Norway

    Steven Le Moan

Authors
  1. Jessica El Khoury
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Steven Le Moan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean-Baptiste Thomas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alamin Mansouri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jessica El Khoury.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Khoury, J., Le Moan, S., Thomas, JB. et al. Color and sharpness assessment of single image dehazing. Multimed Tools Appl 77, 15409–15430 (2018). https://doi.org/10.1007/s11042-017-5122-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-5122-y

Keywords

Search

Navigation