Skip to main content
SpringerLink
Log in

A new Artificial Vision Method for Bad Atmospheric Conditions

  • Conference paper
  • First Online:
Advanced Techniques in Computing Sciences and Software Engineering

  • 2341 Accesses

Abstract

We propose in this paper a method for enhancing vision through fog, based on Blind Source Separation (BSS). BSS method recovers independent source signals from a set of their linear mixtures, where the mixing matrix is unknown. The mixtures are represented in our work by the natural logarithm of the degraded image at different wavelength. These provide an additive mixture of transmittivity coefficient (related to fog) and reflectivity coefficient (related to each point of the scene).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. A. Belouchrani, K. Abdel-Meraim, J.-F. Cardoso, E. Moulines, “A blind source separation technique using second-order statistics”, IEEE Trans. Signal Processing, vol. 45, pp. 434–444, 1997.

    Article  Google Scholar 

  2. K. Tan and J.P. Oakley, “Enhancement of Color Images in Poor Visibility Conditions,” Proc. Int’l Conf. Image Processing, vol. 2, Sept. 2000.

    Google Scholar 

  3. A. Cichocki , S. Amari, 2002, “Adaptive Blind Signal and Image Processing. Learning Algorithms and Applications”, John Wiley and Sons, Ltd Baffins Lane, Chichester West Sussex, PO19 1UD.

    Google Scholar 

  4. D. L. Donoho, “De-noising by soft-thresholding”, IEEE Trans. on Inf. Theory, 41, 3, pp. 613–627, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  5. E. Dumont, E. Follin, G. Gallee, 2001, “Improved fog rendering for driving simulation”, Rapport technique LCPC (2001), http://www.lcpc.fr/LCPC, dumont@lcpc.fr.

  6. S. G. Narasimhan, S. K. Nayar, “Chromatic Framework for Vision in Bad Weather”, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol.1, pp.598–605, Jun, 2000

    Google Scholar 

  7. J. P. Oakley, B. L. Satherley, Improving Image Quality in Poor Visibility Conditions Using a Physical Model for Contrast Degradation, IEEE Trans. Image Process., vol. 7, pp. 167–179, 1998.

    Article  Google Scholar 

  8. http://imk-isys.fzk.de/isys-public/Software-tools/Modtran/science/modrept.ps.

  9. http://orbit-net.nesdis.noaa.gov/arad/fpdt/fog.html.

  10. http://www.bom.gov.au/info/wwords

  11. Website. http://www.cs.columbia.edu/cave/research/publications/vision weather.html. 2000.

  12. Y.Y. Schechner, S.G. Narasimhan and S.K. Nayar, “Instant Dehazing of Images using Polarization,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Vol.I, pp.325–332, Dec, 2001

    Google Scholar 

  13. Y.Y. Schechner, S.G. Narasimhan and S.K. Nayar, “Polarization-Based Vision through Haze”, Applied Optics, Special issue, Vol.42, No.3, pp.511–525, Jan, 2003.

    Google Scholar 

  14. Alex Rav-Acha, Shmuel Peleg, “Two motion-blurred images are better than one”, Elsevier , Pattern Recognition Letters 26 , pp. 311–317, 2005

    Google Scholar 

  15. J.G. Walker, P.C.Y. Chang, and K.I. Hopcraft, “Visibility Depth Improvement in Active Polarization Imaging in Scattering Media”, Applied Optics, vol. 39, 1995.

    Google Scholar 

  16. L.L. Grewe and R.R. Brooks, “Atmospheric Attenuation Reduction through Multisensor Fusion Sensor Fusion: Architectures, Algorithms, and Applications II”, Proc. SPIE, vol. 3376, Apr. 1998.

    Google Scholar 

  17. H. Farid and E. H. Adelson, “Separating reflections and lighting using independent components analysis”, Proc. CVPR, pp. 262–267, 1999.

    Google Scholar 

  18. L.B. Wolff, T.A. Mancini, P. Pouliquen, and A.G. Andreou. “Liquid crystal polarization camera”, IEEE Transactions on Robotics and Automation, 13(2):195–203, 1997.

    Article  Google Scholar 

  19. P. Premaratne, I. Burnett, and C.D. Liyanage, “Blur retrieval via separation of zeros sheets from noisy blurred images”, Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech, pp. 559–562, 20–22 October, 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Environmental Protection, University of Oradea, 26, Gen. Magheru Street, Oradea, România

    M. Curilă

  2. Faculty of Electrical Engineering and Information Technology, University of Oradea, str. Universitatii nr. 1, zip code 410087, Oradea, România

    S. Curilă, O. Novac & Mihaela Novac

Authors
  1. M. Curilă
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Curilă
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. O. Novac
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mihaela Novac
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Curilă .

Editor information

Editors and Affiliations

  1. School of Engineering, University of Bridgeport, University Avenue 221, Bridgeport, 06604, U.S.A.

    Khaled Elleithy

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer Science+Business Media B.V.

About this paper

Cite this paper

Curilă, M., Curilă, S., Novac, O., Novac, M. (2010). A new Artificial Vision Method for Bad Atmospheric Conditions. In: Elleithy, K. (eds) Advanced Techniques in Computing Sciences and Software Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3660-5_38

Download citation

  • DOI: https://doi.org/10.1007/978-90-481-3660-5_38

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-3659-9

  • Online ISBN: 978-90-481-3660-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation