Skip to main content
SpringerLink
Log in

Removal of rain video based on temporal intensity and chromatic constraint of raindrops

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

An improved algorithm of frame time difference is proposed and applied to raindrops removal of video image.This paper analyzes the temporal intensity waveform and chromatic constraint properties of raindrops, and the method is optimized by these two properties. We make use of the difference between rain and non-rain moving objects in the pixels’ intensity changes, which realized a broad classification between the rain and non-rain moving object pixel. The candidate raindrops pixels are optimized in combination with the chromatic constraint property. The experimental results show that the proposed algorithm has a better effect of rainy day in video image restoration than Garg’s, and it is simple and effective. The algorithm has a strong applicability, and it can be further used for many applications, such as air pollution control, management, outdoor surveillance, remote sensing and intelligent vehicles.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Kim J-H, Lee C, Sim J-Y, Kim C-S (2013) Single-image deraining using an adaptive nonlocal means filter. In: Proceedings of IEEE international conference on image processing (ICIP). IEEE, Melbourne, pp 914–917. https://doi.org/10.1109/ICIP.2013.6738189

  2. Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916

    Article  Google Scholar 

  3. Garg K, Nayar SK (2004) Detection and removal of rain from videos. IEEE Comput Soc Conf Comput Vis Pattern Recognit 1:528–535

    Google Scholar 

  4. Tripathi AK, Mukhopadhyay S (2012) A probabilistic approach for detection and removal of rain from videos. IETE J Res 57:82–91

    Article  Google Scholar 

  5. Tripathi AK, Mukhopadhyay S (2014) Removal of rain from videos: a review. Signal Image Video Process 8(8):1421–1430

    Article  Google Scholar 

  6. Li Y, Tan RT, Guo X, Lu J, Brown MS (2016) Rain streak removal using layer priors. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, pp 2736–2744. https://doi.org/10.1109/CVPR.2016.299

  7. Xu G, Xu J, Wang B et al (2014) Removal of rain in video based on motion and shape characteristics of raindrops. Int J Light Electron Opt 125(15):1–5

    Google Scholar 

  8. Kim HG, Seo SJ, Song BC (2015) Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences. J Vis Commun Image Represent 26:317–328

    Article  Google Scholar 

  9. 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 

  10. Malekshahi G, Ebahimnezhad H (2014) Detection and removal of rain from video using predominant direction of Gabor filters. J Inf Syst Telecommun 3(1):42–49

    Google Scholar 

  11. Liu C, Pang Y, Wang J, Yang A, Pan J (2014) Frequency domain directional filtering based rain streaks removal from a single color image. In: Huang DS, Bevilacqua V, Premaratne P (eds) International conference on intelligent computing, ICIC 2014. Lecture notes in computer science, vol 8588. Springer, Cham, pp 415–424

    Google Scholar 

  12. Chen D-Y, Chen C, Kang L-W (2014) Visual depth guided color image rain streaks removal using sparse coding. IEEE Trans Circuits Syst Video Technol 24(8):1430–1455

    Article  Google Scholar 

  13. Sun S-H, Fan S-P, Wang Y-CF (2014) Exploiting image structural similarity for single image rain removal. In: Proceedings of IEEE international conference on image processing (ICIP). IEEE, Paris, pp 4482–4486. https://doi.org/10.1109/ICIP.2014.7025909

  14. 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  MATH  Google Scholar 

  15. Santhaseelan V, Asari VK (2015) Utilizing local phase information to remove rain from video. Int J Comput Vis 112(1):71–89

    Article  Google Scholar 

  16. Kim J-H, Sim J-Y, Kim C-S (2015) Video deraining and desnowing using temporal correlation and low-rank matrix completion. IEEE Trans Image Process 24(9):2658–2670

    Article  MathSciNet  MATH  Google Scholar 

  17. Chen Y-L, Hsu C-T (2013) A generalized low-rank appearance model for spatio-temporally correlated rain streaks. In: Proceedings of IEEE international conference on computer vision. IEEE, Sydney, pp 1968–1975. https://doi.org/10.1109/ICCV.2013.247

  18. Fu X, Huang J, Ding X et al (2017) Clearing the skies: a deep network architecture for single-image rain removal. IEEE Trans Image Process 26(6):2944–2956

    Article  MathSciNet  MATH  Google Scholar 

  19. Nasreen S (2014) Detection and removal of rain in videos using modern approach. Int J Eng Res 3(2):60–63

    Google Scholar 

  20. Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of IEEE international conference on computer vision and pattern recognition (CVPR), pp 1646–1654

  21. Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH (2016) Single image dehazing via multi-scale convolutional neural networks. In: Leibe B, Matas J, Sebe N, Welling M (eds) European conference on computer vision—ECCV 2016, Lecture notes in computer science, vol 9906. Springer, Cham, pp 154–169

    Chapter  Google Scholar 

  22. Huang D-A, Kang L-W, Wang Y-CF, Lin C-W (2014) Self-learning based image decomposition with applications to single image denoising. IEEE Trans Multimedia 16(1):83–93

    Article  Google Scholar 

  23. Kang L-W, Chuang B-C, Hsu C-C, Lin C-W, Yeh C-H (2013) Self-learning-based single image super-resolution of a highly compressed image. In: Proceedings of IEEE international workshop on multimedia signal processing (MMSP). IEEE, Pula, pp 224–229. https://doi.org/10.1109/MMSP.2013.6659292

  24. You S, Tan RT, Kawakami R et al (2016) Adherent raindrop modeling, detectionand removal in video. IEEE Trans Pattern Anal Mach Intell 38(9):1721–1733

    Article  Google Scholar 

  25. 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 

  26. Tian Y, Narasimhan SG (2009) Seeing through water: image restoration using model-based tracking. In: Proceedings of IEEE international conference on computer vision. IEEE, Kyoto, pp 2303–2310. https://doi.org/10.1109/ICCV.2009.5459440

  27. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612

    Article  Google Scholar 

  28. Luo Y, Xu Y, Ji H (2015) Removing rain from a single image via discriminative sparse coding. In: Proceedings of IEEE international conference on computer vision (ICCV). IEEE, Santiago, pp 3397–3405. https://doi.org/10.1109/ICCV.2015.388

  29. Dong A, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38(2):295–307

    Article  Google Scholar 

Download references

Acknowledgements

This work was financially supported by Scientific and Technological Development Scheme of Jilin Province (No. 20170312021ZX) and S&T Major Project of the Science and Technology Ministry in China (No. 2015DFR10670).

Author information

Authors and Affiliations

  1. College of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022, China

    Jingfeng Zang, Guibin Ren, Jianning Dong & Yan Piao

  2. Department of Computer Science, Tabriz University, Tabriz, Iran

    Seio Jim

Authors
  1. Jingfeng Zang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guibin Ren
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jianning Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Seio Jim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingfeng Zang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zang, J., Ren, G., Dong, J. et al. Removal of rain video based on temporal intensity and chromatic constraint of raindrops. Evol. Intel. 12, 349–355 (2019). https://doi.org/10.1007/s12065-018-0185-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-018-0185-x

Keywords

Search

Navigation