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Single image rain and snow removal via guided L0 smoothing filter

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Abstract

Since no temporal information can be exploited, rain and snow removal from single image is a challenging problem. In this paper, an improved rain and snow removal method from single image is proposed by designing a guided L0 smoothing filter. The designed filter is inspired by the previous L0 gradient minimization. Then a coarse rain-free or snow-free image can be obtained with the proposed filter, and the final refined result is recovered by a further minimization operation depending on the observed image. Experimental results show that the proposed algorithm generates better or comparable outputs than the state-of-the-art algorithms in rain and snow removal task for single image.

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References

  1. Barnum P, Kanade T, Narasimhan SG (2007) Spatio-temporal frequency analysis for removing rain and snow from videos. In: Proceedings of the 1st international workshop on photometric analysis for computer vision-PACV, pp 8 p

  2. Barnum PC, Narasimhan S, Kanade T (2010) Analysis of rain and snow in frequency space. Int J Comput Vis 86(2-3):256–274

    Article  Google Scholar 

  3. Bossu J, Hautire N, Tarel JP (2011) Rain or snow detection in image sequences through use of a histogram of orientation of streaks. Int J Comput Vis 93(3):348–367

    Article  Google Scholar 

  4. Buades A, Coll B, Morel JM (2008) Nonlocal image and movie denoising. Int J Comput Vis 76(2):123–139

    Article  Google Scholar 

  5. Chen YL, Hsu CT (2013) A Generalized Low-Rank Appearance Model for Spatio-temporally Correlated Rain Streaks. In: Proceedings of 2013 IEEE international conference on computer vision, pp 1968–1975

  6. Chen DY, Chen CC, Kang LW (2014) Visual depth guided color image rain streaks removal using sparse coding. IEEE Trans Circ Syst Video Technol 24(8):1430–1455

    Article  Google Scholar 

  7. Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–3745

    Article  MathSciNet  Google Scholar 

  8. Garg K, Nayar SK (2007) Vision and rain. Int J Comput Vis 75(1):3–27

    Article  Google Scholar 

  9. Garg K, Nayar SK (2004) Detection and removal of rain from videos. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, vol 1, pp I-528-I-535

  10. He K, Sun J, Tang X (2010) Guided image filtering. In: Proceedings of European Conf Comput Vis, pp 1–14

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

    Article  Google Scholar 

  12. Kang LW, Lin CW, Fu YH (2012) Automatic single-image-based rain streaks removal via image decomposition. IEEE Trans Image Process 21(4):1742–1755

    Article  MathSciNet  Google Scholar 

  13. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of 6th international conference on computer vision, pp 839–846

  14. Xu J, Zhao W, Liu P, Tang X (2012) An improved guidance image based method to remove rain and snow in a single image. Comput Inf Sci 5(3):49

    Google Scholar 

  15. Xu L, Lu C, Xu Y, Jia J (2011) Image smoothing via L 0 gradient minimization. Proc ACM Trans Graph 30(6):174

    Google Scholar 

  16. Zheng X, Liao Y, Guo W, Fu X, Ding X (2013) Single-Image-Based Rain and Snow Removal Using Multi-guided Filter. In: Proceedings of neural information processing, pp 258–265

  17. Zhang X, Li H, Qi Y, Leow WK, Ng TK (2006) Rain removal in video by combining temporal and chromatic properties. In: Proceedings of 2006 IEEE international conference on multimedia and expo, pp 461–464

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Acknowledgments

The project is supported by the National Natural Science Foundation of China (No. 30900328, 61172179, 61103121, 81301278), the Natural Science Foundation of Fujian Province of China (No. 2012J05160), The National Key Technology R&D Program (2012BAI07B06), the Fundamental Research Funds for the Central Universities (No. 2011121051, 2013121023), the Research Fund for the Doctoral Program of Higher Education under Grant 20120121120043.

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Authors and Affiliations

  1. Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, China

    Xinghao Ding, Liqin Chen, Xianhui Zheng, Yue Huang & Delu Zeng

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  1. Xinghao Ding
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  2. Liqin Chen
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  3. Xianhui Zheng
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  4. Yue Huang
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  5. Delu Zeng
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Correspondence to Delu Zeng.

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Ding, X., Chen, L., Zheng, X. et al. Single image rain and snow removal via guided L0 smoothing filter. Multimed Tools Appl 75, 2697–2712 (2016). https://doi.org/10.1007/s11042-015-2657-7

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  • DOI: https://doi.org/10.1007/s11042-015-2657-7

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