Skip to main content
SpringerLink
Log in

Real-time smoke removal for the surveillance images under fire scenario

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

When a fire happens in a building, internal closed-circuit television system becomes less effective under the influences of hot smoke. The fire scenario is very similar to the environments with bad weather conditions such as haze, rain, and snow. Comparing those bad weather conditions, the fire scenarios are much complicated with difficulties in processing the images. This can be reflected by two important aspects: The lighting condition changes frequently inside the building, and the smoke is always in black while the particles under bad weather conditions are generally white. So a fast image restoration method (GL-MSR method) based on the multi-scale Retinex (MSR) was developed in this study to improve the detection accuracy under complicated fire or the similar situations. For the proposed GL-MSR method, the Gaussian pyramid was used to replace the Gaussian convolution where a lookup table was built to reduce the calculation time of the logarithmic algorithm. Compared with the traditional methods such as histogram equalization, the GL-MSR method shows a better result than the others and its operation time was found only 198 ms, almost 1/26 of the traditional processing time.

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
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Hasikin, K., Mat Isa, N.A.: Adaptive fuzzy intensity measure enhancement technique for non-uniform illumination and low contrast images. Signal Image Video Process. 9(6), 1419–1442 (2015)

    Article  Google Scholar 

  2. Wang, X., Chen, L.: Contrast enhancement using feature-preserving bi-histogram equalization. Signal Image Video Process. 12(4), 685–692 (2018)

    Article  MathSciNet  Google Scholar 

  3. Jin, W., Gong, F., Zeng, X., Fu, R.: Illumination robust face recognition using random projection and sparse representation. Signal Image Video Process. 12(4), 721–729 (2018)

    Article  Google Scholar 

  4. Jha, R.K., Biswas, P.K., Chatterji, B.N.: Contrast enhancement of dark images using stochastic resonance. IET Image Process. 6(3), 230–237 (2012)

    Article  MathSciNet  Google Scholar 

  5. Land, E.H.: The Retinex theory of color vision. Sci. Am. 237(6), 108–128 (1997)

    Article  Google Scholar 

  6. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. 33(12), 2341–2353 (2011)

    Article  Google Scholar 

  7. Kiss, Á., Szirányi, T.: Reconstructing static scene viewed through smoke using video. In: 18th IEEE International Conference on Image Processing, Brussels, Belgium, pp. 3461-3464 (2011)

  8. Lv, X., Chen, W., Shen, I.: Real-time dehazing for image and video. In: 18th Pacific Conference on Computer Graphics and Applications, Hangzhou, China, pp. 62–69 (2010)

  9. Wang, W., Li, B., Zheng, J., et al.: A fast multi-scale Retinex algorithm for color image enhancement. In: International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, China, pp. 80–85 (2008)

  10. Jang, C., Lim, J.H., Kim, Y.H.: A fast multi-scale Retinex algorithm using dominant SSR in weights selection. In: International SoC Design Conference, Jeju Island, Korea, pp. 37–40 (2012)

  11. Wang, L., Horiuchi, T., Kotera, H.: High dynamic range image compression by fast integrated surround Retinex model. J. Imaging Sci. Technol. 51(1), 34–43 (2007)

    Article  Google Scholar 

  12. Keller, A., Loepfe, M., Nebiker, P., et al.: On-line determination of the optical properties of particles produced by test fires. Fire Saf. J. 41(4), 266–273 (2006)

    Article  Google Scholar 

  13. Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)

    Article  Google Scholar 

  14. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)

    Article  Google Scholar 

  15. Rahman, Z., Jobson, D.J., Woodell, G.A.: Multiscale Retinex for color rendition and dynamic range compression. In: Applications of Digital Image Processing XIX, SPIE 2847, pp. 183–191 (1996)

  16. Suplata, M., Ravas, R.: Gaussian pyramid based acceleration of optical flow reconstruction. In: 19th International Conference Radioelektronika, Bratislava, Slovakia Republic, pp. 169–171 (2009)

  17. Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: The Seventh IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 820–827 (1999)

  18. Parthasarathy, S., Sankaran, P.: A RETINEX based haze removal method. In: International Conference on Industrial and Information Systems, Chennai, India, pp. 1–6 (2012)

Download references

Acknowledgements

The authors thank Xiaoge Liang for preparing the experimental installation. This work was supported by the National Science Foundation for Young Scientists of China (51504219), Science and Technology Research Project of Henan Province (152102210349), and Ph.D. Research Fund of Zhengzhou University of Light Industry (2014BSJJ020). The authors appreciate the supports.

Author information

Authors and Affiliations

  1. School of Building Environment Engineering, Zhengzhou University of Light Industry, 5 Dongfeng Road, Zhengzhou, 450002, Henan, People’s Republic of China

    Sen Li, Dan Zhang & Chunyong Feng

  2. School of Information and Mechatronics Engineering, Zhengzhou Chenggong University of Finance and Economics, 136 Zijing Road, Gongyi, 451200, Henan, People’s Republic of China

    Shuyan Wang

  3. Civil and Infrastructure Engineering Discipline, School of Engineering, RMIT University, Melbourne, VIC, 3030, Australia

    Long Shi

Authors
  1. Sen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuyan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chunyong Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Long Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dan Zhang.

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

Li, S., Wang, S., Zhang, D. et al. Real-time smoke removal for the surveillance images under fire scenario. SIViP 13, 1037–1043 (2019). https://doi.org/10.1007/s11760-019-01442-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01442-3

Keywords

Search

Navigation