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Joint learning of image detail and transmission map for single image dehazing

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Abstract

Single image haze removal is an important task in computer vision. However, haze removal is an extremely challenging problem due to its massively ill-posed, which is that at each pixel we must estimate the transmission and the global atmospheric light from a single color measurement. In this paper, we propose a new deep learning-based method for removing haze from single input image. First, we estimate a transmission map via joint estimation of clear image detail and transmission map, which is different from traditional methods only estimating a transmission map for a hazy image. Second, we use a global regularization method to eliminate the halos and artifacts. Experimental results on synthetic dataset and real-world images show our method outperforms the other state-of-the-art methods.

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References

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

    Article  Google Scholar 

  2. Li, K., He, F., Yu, H.: Robust visual tracking based on convolutional features with illumination and occlusion handing. J. Comput. Sci. Technol. 33(1), 223 (2018)

    Article  Google Scholar 

  3. Li, K., He, F., Yu, H., Chen, X.: A correlative classifiers approach based on particle filter and sample set for tracking occluded target. Appl. Math. A J. Chin. Univ. 32(3), 294 (2017)

    Article  MathSciNet  Google Scholar 

  4. Chen, X., He, F., Yu, H.: A matting method based on full feature coverage. Multimed. Tools Appl. (2018). https://doi.org/10.1007/s11042-018-6690-1

    Article  Google Scholar 

  5. Li, K., He, F., Yu, H., Chen, X.: A parallel and robust object tracking approach synthesizing adaptive Bayesian learning and improved incremental subspace learning. Front. Comput. Sci. (2018). https://doi.org/10.1007/s11704-018-6442-4

    Article  Google Scholar 

  6. Zhang, D., He, F., Han, S., Zou, L., Wu, Y., Chen, Y.: An efficient approach to directly compute the exact Hausdorff distance for 3D point sets. Integr. Comput. Aided Eng. 24(3), 261 (2017)

    Article  Google Scholar 

  7. Chen, Y., He, F., Wu, Y., Hou, N.: A local start search algorithm to compute exact Hausdorff distance for arbitrary point sets. Pattern Recognit. 67, 139 (2017)

    Article  Google Scholar 

  8. Fan, X., Wang, Y., Gao, R., Luo, Z.: Haze editing with natural transmission. Vis. Comput. 32(1), 137 (2016)

    Article  Google Scholar 

  9. Harald, K.: Theorie der horizontalen Sichtweite: Kontrast und Sichtweite. Keim & Nemnich, Munich (1924)

    Google Scholar 

  10. Tan, R.T.: Visibility in bad weather from a single image. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE, pp. 1–8 (2008)

  11. Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: IEEE international conference on computer vision. IEEE, pp. 2201–2208 (2009)

  12. Fattal, R.: Single image dehazing. ACM Trans. Graph. (TOG) 27(3), 1 (2008)

    Article  Google Scholar 

  13. Kratz, L., Nishino, K.: Factorizing scene albedo and depth from a single foggy image. IEEE Int. Conf. Comput. Vis. 30(2), 1701 (2009)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  15. Gibson, K.B., Nguyen, T.Q.: An analysis of single image defogging methods using a color ellipsoid framework. Eur. J. Image Video Process. 2013(4), 1 (2013)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  17. Li, Z., Zheng, J.: Edge-Preserving decomposition-based single image haze removal. IEEE Trans. Image Process. 24(12), 5432 (2015)

    Article  MathSciNet  Google Scholar 

  18. Khmag, A., Al-Haddad, S., Ramli, A.R., Kalantar, B.: Single image dehazing using second-generation wavelet transforms and the mean vector L2-norm. Vis. Comput. 34(5), 675 (2018)

    Article  Google Scholar 

  19. Tang, K., Yang, J., Wang, J.: Investigating haze-relevant features in a learning framework for image dehazing. In: IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp. 2995–3002 (2014)

  20. Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.H.: Single image dehazing via multi-scale convolutional neural networks. In: European conference on computer vision. Springer, pp. 154–169 (2016)

  21. Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: An end-to-end system for single image haze removal. arXiv preprint arXiv:1601.07661 (2016)

  22. Zhang, S., He, F., Yao, J.: Single image dehazing using deep convolution neural networks. In: Pacific Rim conference on multimedia. Springer, pp. 315–325 (2017)

  23. Xiao, C., Gan, J.: Fast image dehazing using guided joint bilateral filter. Vis. Comput. 28(6–8), 713 (2012)

    Article  Google Scholar 

  24. Ling, Z., Li, S., Wang, Y., Shen, H., Lu, X.: Adaptive transmission compensation via human visual system for efficient single image dehazing. Vis. Comput. 32(5), 653 (2016)

    Article  Google Scholar 

  25. Ju, M., Zhang, D., Wang, X.: Single image dehazing via an improved atmospheric scattering model. Vis. Comput. 33(12), 1613 (2018)

    Article  Google Scholar 

  26. Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: All-in-one dehazing network. IEEE International Conference on Computer Vision. 1(4), 4770–4778 (2017)

    Google Scholar 

  27. Meng, G., Wang, Y., Duan, J., Xiang, S., Pan, C.: Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE international conference on computer vision, pp. 617–624 (2013)

  28. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. In: ACM international conference on multimedia. ACM, pp. 675–678 (2014)

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

    Article  Google Scholar 

  30. Silberman, N., Hoiem, D., Kohli, P., Fergus, R.: Indoor segmentation and support inference from RGBD images. In: European conference on computer vision. Springer, pp. 746–760 (2012)

  31. Li, W., Saeedi, S., McCormac, J., Clark, R., Tzoumanikas, D., Ye, Q., Huang, Y., Tang, R., Leutenegger, S.: InteriorNet: mega-scale multi-sensor photo-realistic indoor scenes dataset. In: British machine vision conference (2018)

  32. Fattal, R.: Dehazing using color-lines. ACM Trans. Graph. (TOG) 34(1), 13 (2014)

    Article  Google Scholar 

  33. Berman, D., treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE conference on computer vision and pattern recognition (CVPR), pp. 1674–1682 (2016)

  34. Bui, T.M., Kim, W.: Single image dehazing using color ellipsoid prior. IEEE Trans. Image Process. 27(2), 999 (2018)

    Article  MathSciNet  Google Scholar 

  35. Sulami, M., Glatzer, I., Fattal, R., Werman, M.: Automatic recovery of the atmospheric light in hazy images. In: IEEE international conference on computational photography, pp. 1–11 (2014)

  36. Yu, H., He, F., Pan, Y.: A novel segmentation model for medical images with intensity inhomogeneity based on adaptive perturbation. multimedia tools and applications (2018). https://doi.org/10.1007/s11042-018-6735-5

    Article  Google Scholar 

  37. Yu, H., He, F., Pan, Y.: A novel region-based active contour model via local patch similarity measure for image segmentation. Multimed. Tools Appl. 77(18), 24097 (2018)

    Article  Google Scholar 

Download references

Acknowledgements

This study was funded by National Natural Science Foundation of China (Grant Numbers 61472289 and 41571436).

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Authors and Affiliations

  1. School of Computer Science, Wuhan University, Wuhan, China

    Shengdong Zhang & Fazhi He

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Wenqi Ren

  3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Jian Yao

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  1. Shengdong Zhang
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  2. Fazhi He
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  3. Wenqi Ren
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  4. Jian Yao
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Correspondence to Fazhi He.

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Zhang, S., He, F., Ren, W. et al. Joint learning of image detail and transmission map for single image dehazing. Vis Comput 36, 305–316 (2020). https://doi.org/10.1007/s00371-018-1612-9

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