A cascaded approach for image defogging based on physical and enhancement models


In this paper, we propose a novel cascade strategy approach for visibility restoration in foggy images. The proposed cascade strategy is based on the combination of enhancement and physical models, the contrast limited adaptive histogram equalization (CLAHE) and no-black pixel constraint with planar assumption (NBPC \(+\) PA) methods. The use of CLAHE enhances the visibility of foggy image, but it produces color and edge distortion, boosts noise and creates halo effects. We overcome these shortcomings of CLAHE by feeding its output to the NBPC \(+\) PA. In order to improve the cascaded performance of the two methods, we determine the suitable parameters. The proposed cascading utilizes the individual strengths of the two approaches which in turn provides better defogging results for homogeneous as well as inhomogeneous fog. We present objective quality assessment and visibility enhancement of various foggy images. The experimental results verify the enhanced defogging capabilities of the proposed cascade strategy compared to the existing defogging algorithms.

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  1. 1.



  1. 1.

    Rong, Z., Jun, W.L.: Improved wavelet transform algorithm for single image dehazing. Opt. Int. J. Light Electron Opt. 125(13), 3064–3066 (2014)

    Article  Google Scholar 

  2. 2.

    Wang, W., Yuan, X.: Recent advances in image dehazing. IEEE/CAA J. Autom. Sinica 4(39), 410–436 (2017)

    MathSciNet  Article  Google Scholar 

  3. 3.

    Kim, J.Y., Kim, L.S., Hwang, S.H.: An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans. Circuits Syst. Video Technol. 11(4), 475–484 (2001)

    Article  Google Scholar 

  4. 4.

    Xu, Y., Wen, J., Fei, L., Zhang, Z.: Review of video and image defogging algorithms and related studies on image restoration and enhancement. IEEE Access 4, 165–188 (2016)

    Article  Google Scholar 

  5. 5.

    Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: ICCV, pp. 2201–2208 (2009)

  6. 6.

    Tarel, J.P., Hautiere, N., Caraffa, L., Cord, A., Halmaoui, H., Gruyer, D.: Vision enhancement in homogeneous and heterogeneous fog. IEEE Intell. Transp. Syst. Mag. 4(2), 6–20 (2012)

    Article  Google Scholar 

  7. 7.

    Zhang, L., Wang, S., Wang, X.: Saliency-based dark channel prior model for single image haze removal. IET Image Process. 12(6), 1049–1055 (2018)

    Article  Google Scholar 

  8. 8.

    Salazar-Colores, S., Ramos-Arreguín, J.M., Echeverri, C.J.O., Cabal-Yepez, E., Pedraza-Ortega, J.C., Rodriguez-Resendiz, J.: Image dehazing using morphological opening, dilation and Gaussian filtering. Signal Image Video Process. 12(7), 1329–1335 (2018)

    Article  Google Scholar 

  9. 9.

    Xiao, J., Zhu, L., Zhang, Y., Liu, E., Lei, J.: Scene-aware image dehazing based on sky-segmented dark channel prior. IET Image Process. 11(12), 1163–1171 (2017)

    Article  Google Scholar 

  10. 10.

    Zhu, Q., Mai, J., Shao, L.: A fast single image haze removal algorithm using color attenuation prior. IEEE Trans. Image Process. 24(11), 3522–3533 (2015)

    MathSciNet  Article  Google Scholar 

  11. 11.

    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. 24(10), 1814–1824 (2014)

    Article  Google Scholar 

  12. 12.

    Nishino, K., Kratz, L., Lombardi, S.: Bayesian defogging. Int. J. Comput. Vis. 98(3), 263–278 (2012)

    MathSciNet  Article  Google Scholar 

  13. 13.

    Petro, A.B., Sbert, C., Morel, J.M.: Multiscale retinex. Image Processing On Line 4, 71–88 (2014)

    Article  Google Scholar 

  14. 14.

    Fattal, R.: Single image dehazing. ACM Trans. Graph. (TOG) 27(3), 72 (2008)

    Article  Google Scholar 

  15. 15.

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

    Article  Google Scholar 

  16. 16.

    Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: ICCV, pp. 617–624 (2013)

  17. 17.

    Sun, W., Wang, H., Sun, C., Guo, B., Jia, W., Sun, M.: Fast single image haze removal via local atmospheric light veil estimation. Comput. Electr. Eng. 46, 371–383 (2015)

    Article  Google Scholar 

  18. 18.

    Ancuti, C.O., Ancuti, C., Hermans, C., Bekaert, P.: A fast semi-inverse approach to detect and remove the haze from a single image. In: ACCV, pp. 501–514 (2010)

  19. 19.

    Sulami, M., Glatzer, I., Fattal, R., Werman, M.: Automatic recovery of the atmospheric light in hazy images. In: IEEE International Conference on Computational Photography (ICCP), pp. 1–11 (2014)

  20. 20.

    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Heckbert, P. (ed.) Graphics gems IV, pp. 474–485. Academic Press (1994)

  21. 21.

    Wang, Zhou, Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

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Correspondence to Naeem Bhatti.

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Hassan, N., Ullah, S., Bhatti, N. et al. A cascaded approach for image defogging based on physical and enhancement models. SIViP 14, 867–875 (2020). https://doi.org/10.1007/s11760-019-01618-x

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  • Image defogging
  • Enhancement and physical models
  • NBPC \(+\) PA
  • Cascade approach