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Visibility distance estimation in foggy situations and single image dehazing based on transmission computation model

Visibility distance estimation in foggy situations and single image dehazing based on transmission computation model

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The existing visibility distance estimation algorithms in foggy situations use the region growing method to extract the vertical position of inflection point of image intensity changing. These algorithms have lower inflection point location accuracy for an image with a non-homogeneous road surface. To deal with these problems, this study presents a novel visibility distance measuring technique under foggy weather conditions. This method combines two major models: inflection point estimation (IPE) model and transmission refining (TR) model. The proposed IPE model based on transmission computation model derives a very useful relation between the transmission value of inflection points and the constant . In order to acquire the more accurate transmission map and vertical position of each inflection point, this study establishes an effective TR model. This model exploits the edge information of input images, in order to significantly reduce the effects of artefact. The proposed algorithm provides more accurate visibility distance estimation of an image with a non-homogeneous road surface than the well-known algorithm through qualitative evaluations in experiments. The experimental results also show that the TR model has better outcomes than the guided filter approach through qualitative and quantitative evaluations.

References

    1. 1)
      • 1. Hautiere, N., Tarel, J.P., Lavenant, J.: ‘Automatic fog detection and estimation of visibility distance through use of an onboard camera’, Mach. Vis. Appl., 2008, 17, (1), pp. 820.
    2. 2)
      • 2. Negru, M., Nedevschi, S.: ‘Image based fog detection and visibility estimation for driving assistance systems’. Proc. IEEE Int. Conf. on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania, September, 2013, pp. 163168.
    3. 3)
      • 3. Bronte, S., Bergasa, L.M.: ‘Fog detection system based on computer vision techniques’. Proc. IEEE Int. Conf. on Intelligent Transportation Systems, MO, USA, November, 2009, pp. 16.
    4. 4)
      • 4. Wang, J.Q., Yagi, Y.: ‘Shape priors extraction and application for geodesic distance transforms in images and videos’, Pattern Recognit. Lett., 2013, 34, pp. 13861393.
    5. 5)
      • 5. Gallen, R., Cord, A., Aubert, D.: ‘Towards night fog detection through use of in-vehicle multipurpose camera’. Proc. IEEE Conf. on Intelligent Vehicles Symp., Baden-Baden, Germany, July, 2011, pp. 399404.
    6. 6)
      • 6. Pavlic, M., Belzner, H, Rigoll, G.: ‘Image based fog detection in vehicles’. Proc. IEEE Int. Conf. on Intelligent Vehicles Symp., Alcala de Henares, Spain, July, 2012, pp. 11321137.
    7. 7)
      • 7. Narasimhan, S.G., Nayar, S.K.: ‘Vision and the atmosphere’, Int. J. Comput. Vis., 2002, 48, (3), pp. 233254.
    8. 8)
      • 8. He, K.M., Sun, J., Tang, X.O.: ‘Single image haze removal using dark channel prior’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (12), pp. 22412253.
    9. 9)
      • 9. Tarel, J.P., Hautiere, N., Caraffa, L., et al: ‘Vision enhancement in homogeneous and heterogeneous fog’, IEEE Intell. Transp. Syst. Mag., 2012, 4, (2), pp. 620.
    10. 10)
      • 10. Hautiere, N., Tarel, J.P., Aubert, D.: ‘Mitigation of visibility loss for advanced camera-based driver assistance’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (2), pp. 474484.
    11. 11)
      • 11. Negru, M., Nedevschi, S., Peter, R.I.: ‘Exponential contrast restoration in fog conditions for driving assistance’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 22572268.
    12. 12)
      • 12. Xu, Y., Wen, J., Fei, L.K., et al: ‘Review of video and image defogging algorithms and related studies on image restoration and enhancement’, IEEE Access, 2016, 4, pp. 165188.
    13. 13)
      • 13. Yu, T., Riaz, I., Piao, J.C., et al: ‘Real-time single image dehazing using block-to-pixel interpolation and adaptive dark channel prior’, IET Image Process., 2015, 9, (9), pp. 725734.
    14. 14)
      • 14. Wang, D., Zhu, J.B.: ‘Fast smoothing technique with edge preservation for single de-hazing’, IET Comput. Vis., 2015, 9, (6), pp. 950959.
    15. 15)
      • 15. Huang, S.C., Chen, B.H., Wang, W.J.: ‘Visibility restoration of single hazy images captured in real-world weather conditions’, IEEE Trans. Circuits Syst. Video Technol., 2014, 24, (10), pp. 18141824.
    16. 16)
      • 16. He, K.M., Sun, J., Tang, X.O.: ‘Guided image filtering’, IEEE Trans. Pattern Anal. Mach. Intell.’, 2013, 35, (6), pp. 13971409.
    17. 17)
      • 17. Liu, J.L., Feng, D.Z.: ‘Two-dimensional multi-pixel anisotropic Gaussian filter for edge-line segment (ELS) detection’, Image Vis. Comput., 2014, 32, pp. 3753.
    18. 18)
      • 18. Paris, S., Durand, F.: ‘A fast approximation of the bilateral filter using a signal processing approach’, Int. J. Comput. Vis., 2009, 81, (1), pp. 2452.
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