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Structure–texture decomposition-based dehazing of a single image with large sky area

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Abstract

Traditional dehazing methods based on restoration are prone to color distortion and noise amplification when dealing with hazy image with large sky area. To improve dehazing effect, we propose a dehazing algorithm based on image structure–texture decomposition and reconstruction. Hazy image is decomposed into high-frequency texture layer and low-frequency structure layer by total variation. Discrete cosine transform is used to generate an image mask to separate sky area and non-sky area. The texture layer is denoised by the mask, and the structure layer is dehazed by dark channel prior. The media transmission is corrected by color attenuation prior. Finally, the denoised texture layer and the dehazed structure layer are reconstructed to obtain the dehazed image. A no-reference image quality assessment is also proposed to evaluate the dehazed images. Experiment results show that, compared with the state-of-the-art methods, our algorithm has better dehazing effect on non-sky area, and the sky area after dehazing is smooth without color distortion and noise.

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References

  1. 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). https://doi.org/10.1109/76.915354

    Article  Google Scholar 

  2. Arici, T., Dikbas, S., Altunbasak, Y.: A histogram modification framework and its application for image contrast enhancement. IEEE Trans. Image Process. 18(9), 1921–1935 (2009). https://doi.org/10.1109/TIP.2009.2021548

    Article  MathSciNet  MATH  Google Scholar 

  3. Gao, Y., Hu, H.M., Wang, S., et al.: A fast image dehazing algorithm based on negative correction. Signal Process. 103(10), 380–398 (2014). https://doi.org/10.1016/j.sigpro.2014.02.016

    Article  Google Scholar 

  4. Lian, X., Pang, Y., Yang, A.: Learning intensity and detail mapping parameters for dehazing. Multimed. Tools Appl. 77(12), 15695–15720 (2018). https://doi.org/10.1007/s11042-017-5142-7

    Article  Google Scholar 

  5. Wang, Y., Wang, H., Yin, C., et al.: Biologically inspired image enhancement based on retinex. Neurocomputing 177, 373–384 (2016). https://doi.org/10.1016/j.neucom.2015.10.124

    Article  Google Scholar 

  6. Galdran A, Alvarez-Gila A, Bria A, et al, On the duality between retinex and image dehazing, In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 8212–8221.

  7. Ancuti, C.O., Ancuti, C.: Single image dehazing by multi-scale fusion. IEEE Trans. Image Process. 22(8), 3271–3282 (2013). https://doi.org/10.1109/TIP.2013.2262284

    Article  Google Scholar 

  8. Choi, L.K., You, J., Bovik, A.C.: Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans. Image Process. 24(11), 3888–3901 (2015). https://doi.org/10.1109/TIP.2015.2456502

    Article  MathSciNet  MATH  Google Scholar 

  9. Galdran, A., Vazquez-Corral, J., Pardo, D., et al.: Fusion-based variational image dehazing. IEEE Signal Process. Lett. 24(2), 151–155 (2017). https://doi.org/10.1109/LSP.2016.2643168

    Article  MATH  Google Scholar 

  10. Wang, J.B., He, N., Zhang, L.L., Lu, K.: Single image dehazing with a physical model and dark channel prior. Neurocomputing 149, 718–728 (2015). https://doi.org/10.1016/j.neucom.2014.08.005

    Article  Google Scholar 

  11. 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). https://doi.org/10.1109/TIP.2015.2446191

    Article  MathSciNet  MATH  Google Scholar 

  12. Lai, Y.H., Chen, Y.L., Chiou, C.J., et al.: Single-image dehazing via optimal transmission map under scene priors. IEEE Trans. Circuits Syst. Video Technol. 25(1), 1–14 (2014). https://doi.org/10.1109/TCSVT.2014.2329381

    Article  Google Scholar 

  13. Berman D, Avidan S, Non-local image dehazing, In: Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, 2016, pp. 1674–1682.

  14. Chen, C., Do, M.N., Wang, J.: Robust image and video dehazing with visual artifact suppression via gradient residual minimization. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II, pp. 576–591. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_36

    Chapter  Google Scholar 

  15. Ju, M., Zhang, D., Wang, X.: Single image dehazing via an improved atmospheric scattering model. Vis. Comput. 33(12), 1613–1625 (2017). https://doi.org/10.1007/s00371-016-1305-1

    Article  Google Scholar 

  16. Bui, T.M., Kim, W.: Single image dehazing using color ellipsoid prior. IEEE Trans. Image Process. 27(2), 999–1009 (2017). https://doi.org/10.1109/TIP.2017.2771158

    Article  MathSciNet  MATH  Google Scholar 

  17. Salazarcolores, S., Cabalyepez, E., Ramosarreguin, J.M., et al.: A fast image dehazing algorithm using morphological reconstruction. IEEE Trans. Image Process. 28(5), 2357–2366 (2018). https://doi.org/10.1109/TIP.2018.2885490

    Article  MathSciNet  Google Scholar 

  18. Shiliang, G., Yuanyuan, et al.: Detail preserved single image dehazing algorithm based on airlight refinement. IEEE Trans. Multimed. 21(2), 351–362 (2019). https://doi.org/10.1109/TMM.2018.2856095

    Article  Google Scholar 

  19. Wu, Q., Zhang, J., Ren, W., et al.: Accurate transmission estimation for removing haze and noise from a single image. IEEE Trans. Image Process. 29, 2583–2597 (2020). https://doi.org/10.1109/TIP.2019.2949392

    Article  Google Scholar 

  20. Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: Dehazenet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016). https://doi.org/10.1109/TIP.2016.2598681

    Article  MathSciNet  MATH  Google Scholar 

  21. Ren, W., Liu, S., Zhang, H., et al.: Single image dehazing via multi-scale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II, pp. 154–169. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_10

    Chapter  Google Scholar 

  22. Li B, Peng X, Wang Z, Xu J, Feng D, Aod-net: All-in-one dehazing network, In: Proceedings of the IEEE international conference on computer vision,Venice, Italy, 2017, pp. 4770–4778.

  23. Park, J., Han, D.K., Ko, H.: Fusion of heterogeneous adversarial networks for single image dehazing. IEEE Trans. Image Process. 29, 4721–4732 (2020). https://doi.org/10.1109/TIP.2020.2975986

    Article  Google Scholar 

  24. Hautiere, N., Tarel, J.P., Aubert, D., et al.: Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal. Stereol. 27(2), 87–95 (2008). https://doi.org/10.5566/ias.v27.p87-95

    Article  MathSciNet  MATH  Google Scholar 

  25. Hashim, A.R., et al.: No reference image quality measure for hazy images. Int. J. Intell. Eng. Syst. 13(6), 460–471 (2020). https://doi.org/10.22266/ijies2020.1231.41

    Article  Google Scholar 

  26. Min, X., et al.: Quality evaluation of image dehazing methods using synthetic hazy images. IEEE Trans. Multimed. 21(9), 2319–2333 (2019). https://doi.org/10.1109/TMM.2019.2902097

    Article  Google Scholar 

  27. Qin, M., Xie, F., Jiang, Z.: No reference assessment of image visibility for dehazing. In: Zhao, Y., Kong, X., Taubman, D. (eds.) Image and graphics: 9th international conference, ICIG 2017, Shanghai, China, September 13-15, 2017, Revised Selected Papers, Part I, pp. 664–674. Springer International Publishing, Cham (2017). https://doi.org/10.1007/978-3-319-71607-7_58

    Chapter  Google Scholar 

  28. Ren X, Tang C, Wang B, et al: Single Image with Large Sky Area Dehazing Based on Structure-texture Decomposition, In: IEEE 6th International Conference on Computer and Communications (ICCC). IEEE, Chengdu, China, 2020,pp. 415–419.

  29. McCartney, E.J.: Optics of the atmosphere: scattering by molecules and particles. Optica Acta Int. J. Optics 24(7), 779–779 (1977)

    Google Scholar 

  30. 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). https://doi.org/10.1109/TPAMI.2010.168

    Article  Google Scholar 

  31. Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition: modeling, algorithms, and parameter selection. Int. J. Comput. Vis. 67(1), 111–136 (2006). https://doi.org/10.1007/s11263-006-4331-z

    Article  MATH  Google Scholar 

  32. Wang, Y., Yang, J., Yin, W., Zhang, Y.: A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imaging Sci. 1(3), 248–272 (2008). https://doi.org/10.1137/080724265

    Article  MathSciNet  MATH  Google Scholar 

  33. Li, Y., Guo, F., Tan, R.T., Brown, M.S.: A contrast enhancement framework with JPEG artifacts suppression. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer vision:ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II, pp. 174–188. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_12

    Chapter  Google Scholar 

  34. Li, X., Cewu, L., Yi, X., Jia, J.: Image smoothing via L0 gradient minimization. ACM Trans. Graphics 30(6), 1–12 (2011). https://doi.org/10.1145/2070781.2024208

    Article  Google Scholar 

  35. Li, X., Chen, M., Nie, F., Wang, Q.: A multiview-based parameter free framework for group detection. Proc. AAAI Conf. Artif. Intell. (2017). https://doi.org/10.1609/aaai.v31i1.11208

    Article  Google Scholar 

  36. Panetta, K., Agaian, S., Zhou, Y., et al.: Parameterized logarithmic framework for image enhancement. IEEE Trans. Syst. Man Cyber. Part B 41(2), 460–473 (2011). https://doi.org/10.1109/TSMCB.2010.2058847

    Article  Google Scholar 

  37. Meng G, Wang Y, Duan J, et al: Efficient Image Dehazing with Boundary Constraint and Contextual Regularization, In: IEEE International Conference on Computer Vision, Sydney, NSW, Australia, 2013, pp. 617–624.

  38. L. K. Choi, J. You, and A. C. Bovik, "LIVE Image Defogging Database," Online: http://live.ece.utexas.edu/research/fog/fade_defade.html, 2015.

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Acknowledgements

This work was supported by the Key Research and Development Programs of Jiangsu Province (BE2018720), and the Open project of Engineering Center of Ministry of Education (NJ2020004).

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  1. College of Automatic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Chaoying Tang, Ru Jia, Xue Ren, Yun Cui & Biao Wang

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  1. Chaoying Tang
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Correspondence to Chaoying Tang.

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Tang, C., Jia, R., Ren, X. et al. Structure–texture decomposition-based dehazing of a single image with large sky area. Machine Vision and Applications 33, 72 (2022). https://doi.org/10.1007/s00138-022-01321-x

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