Multimedia Tools and Applications

, Volume 76, Issue 2, pp 2019–2038 | Cite as

Rain streak removal by multi-frame-based anisotropic filtering

Article

Abstract

Dynamic weather conditions, such as rain and snow, often produce strong intensity discontinuity among frames, thus seriously degrade their visual or compression performance. How to remove these artifacts is a challenging task and has been intensively studies recently. The state-of-the-art algorithms detect these scratches before removing them from the scene. Visual effect of rain or snow is complex and difficult to be distinguished from the background; hence the precision of its detection and segmentation by hard decision is usually unsatisfactory. As an anisotropic filter performs well in structural noise removal, such as linear, planar as well as isotropic noise, it is utilized in this paper to analyze image content and suppress scratch noise simultaneously. Compared with the state-of-the-art algorithms, the proposed algorithm is better and more robust in dynamic scenes.

Keywords

Structural noise Anisotropic filter Rain removal 

References

  1. 1.
    Aharon M, Elad M, Bruckstein A (2006) svd: an algorithm for designing overcomplete dictionaries for sparse representation. Signal Process, IEEE Trans 54(11):4311–4322CrossRefGoogle Scholar
  2. 2.
    Bossu J, Hautiμere 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):348–367CrossRefGoogle Scholar
  3. 3.
    Brewer N, Liu N (2008) Using the shape characteristics of rain to identify and remove rain from video. Struct, Syntact Stat Pattern Recognit 451–458Google Scholar
  4. 4.
    Buades A, Coll B, Morel JM et al (2005) A review of image denoising algorithms, with a new one,“SIAM. J Multiscale Model Simulat 4(2):490–530CrossRefMATHGoogle Scholar
  5. 5.
    Chen R, Chang FL, Li Z, Ferraro R, Weng F (2007) Impact of the vertical variation of cloud droplet size on the estimation of cloud liquid water path and rain detection. J Atmos Sci 64(11):3843–3853CrossRefGoogle Scholar
  6. 6.
    Chen DY, Chen CC, Kang LW (2014) Visual depth guided color image rain streaks removal using sparse coding, circuits and systems for video technology. IEEE Trans on 24(8):1430–1454Google Scholar
  7. 7.
    Dean N, Raftery A (2005) Normal uniform mixture differential gene expression detection for cdna microarrays. BMC Bioinform 6(1):173CrossRefGoogle Scholar
  8. 8.
    Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. Imag Process, IEEE Trans 15(12):3736–3745MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fadili JM, Starck JL, Elad M, Donoho DL (2010) Mcalab: Reproducible research in signal and image decomposition and inpainting. Comput Sci Eng 12(1):44–63CrossRefGoogle Scholar
  10. 10.
    Fernández JJ, Li S (2003) An improved algorithm for anisotropic nonlinear diffusion for denoising cryo-tomograms. J Struct Biol 144(1):152–161CrossRefGoogle Scholar
  11. 11.
    Frangakis AS, Stoschek A, Hegerl R (2001) Wavelet transform filtering and nonlinear anisotropic diffusion assessed for signal reconstruction performance on multidimensional biomedical data. Biomed Eng, IEEE Trans 48(2):213–222CrossRefGoogle Scholar
  12. 12.
    Garg K, Nayar SK (2004) Detection and removal of rain from videos. CVPR, Proc 2004 I.E. Comput Soc Conf IEEE 1:528–535Google Scholar
  13. 13.
    Garg K, Nayar SK (2005) When does a camera see rain? Proc IEEE Int Conf Comput Vis 2:1067–1074CrossRefGoogle Scholar
  14. 14.
    Garg K, Nayar SK (2006) Photorealistic rendering of rain streaks. ACM Trans Graph (TOG) ACM 25:996–1002CrossRefGoogle Scholar
  15. 15.
    Garg K, Nayar SK (2007) Vision and rain. Int J Comput Vis 75(1):3–27CrossRefGoogle Scholar
  16. 16.
    Hase H, Miyake K, Yoneda M (1999) Real-time snowfall noise elimination. ICIP Proc 1999 Int Conf IEEE 2:406–409Google Scholar
  17. 17.
    Kang L, Lin C, Fu Y (2011) Automatic single-image-based rain streaks removal via image decomposition. Image Process, IEEE Trans 99:1–1Google Scholar
  18. 18.
    Mairal J, Elad M, Sapiro G (2008) Sparse representation for color image restoration. Imag Process IEEE Trans 17(1):53–69MathSciNetCrossRefMATHGoogle Scholar
  19. 19.
    Starck JL, Moudden Y, Bobin J et al. (2005) Morphological component analysis in Proceedings of the SPIE conference wavelets. Citeseer 5914Google Scholar
  20. 20.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images,” in Computer Vision, 1998. Sixth International Conference on. IEEE 839–846Google Scholar
  21. 21.
    Weickert J (1998) Anisotropic diffusion in image processing[M]. Teubner, StuttgartMATHGoogle Scholar
  22. 22.
    Yao C, Wang C, Hong LJ, Cheng YF (2014) A Bayesian probabilistic framework for rain detection. Entropy 16(6):3302–3314CrossRefGoogle Scholar
  23. 23.
    Zhang M, Gunturk BK (2008) Multiresolution bilateral filtering for image denoising. Imag Process IEEE Trans 17(12):2324–2333MathSciNetCrossRefGoogle Scholar
  24. 24.
    Zhang X., Li H., Qi Y et al. (2006) Rain removal in video by combining temporal and chromatic properties, in Multimedia and Expo, 2006 I.E. International Conference on. IEEE 461–464Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and Software EngineeringEast China Normal UniversityShanghaiChina
  2. 2.School of Software EngineeringSouth China University of TechnologyGuangzhouChina
  3. 3.The Third Research Institute of Ministry of Public SecurityShangahaiChina
  4. 4.Shanghai Key Laboratory of Digital Media Processing and TransmissionShangahaiChina

Personalised recommendations