OSA's Digital Library

Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 20, Iss. 15 — Jul. 16, 2012
  • pp: 16584–16595

Image degradation and recovery based on multiple scattering in remote sensing and bad weather condition

Shuyin Tao, Huajun Feng, Zhihai Xu, and Qi Li  »View Author Affiliations


Optics Express, Vol. 20, Issue 15, pp. 16584-16595 (2012)
http://dx.doi.org/10.1364/OE.20.016584


View Full Text Article

Enhanced HTML    Acrobat PDF (3835 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

The radiance received by the sensor is influenced by the atmospheric interaction including the effects of absorption and scattering. Based on the analysis of the radiance along the transmission path, we propose an image degradation model and a recovery method for remote sensing and bad weather condition in which the effect of multiple scattering cannot be ignored. Several real outdoor images are restored to verify the effectiveness of the proposed model and method. The results turn out to be significantly improved in contrast and sharpness.

© 2012 OSA

OCIS Codes
(100.3020) Image processing : Image reconstruction-restoration
(280.1310) Remote sensing and sensors : Atmospheric scattering
(010.5620) Atmospheric and oceanic optics : Radiative transfer

ToC Category:
Remote Sensing

History
Original Manuscript: April 26, 2012
Revised Manuscript: June 22, 2012
Manuscript Accepted: June 25, 2012
Published: July 9, 2012

Citation
Shuyin Tao, Huajun Feng, Zhihai Xu, and Qi Li, "Image degradation and recovery based on multiple scattering in remote sensing and bad weather condition," Opt. Express 20, 16584-16595 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-15-16584


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. A. Berk, G. P. Anderson, P. K. Acharya, J. H. Chetwynd, L. S. Bernstein, E. P. Shettle, M. W. Matthew, and S. M. Adler-Golden, “Modtran4 user’s manual,” Air Force Research Laboratory, 1999.
  2. J. P. Oakley and B. L. Satherley, “Improving image quality in poor visibility conditions using a physical model for contrast degradation,” IEEE Trans. Image Process.7(2), 167–179 (1998). [CrossRef] [PubMed]
  3. K. K. Tan and J. P. Oakley, “Physics-based approach to color image enhancement in poor visibility conditions,” J. Opt. Soc. Am. A18(10), 2460–2467 (2001). [CrossRef] [PubMed]
  4. J. Kopf, B. Neubert, B. Chen, M. Cohen, D. Cohen-Or, O. Deussen, M. Uyttendaele, and D. Lischinski, “Deep photo: model-based photograph enhancement and viewing,” ACM Trans. Graph.27, 116 (2008).
  5. S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” IEEE Trans. Pattern Anal. Mach. Intell.25(6), 713–724 (2003). [CrossRef]
  6. Y. Y. Schechner, S. G. Narasimhan, and S. K. Nayar, “Instant dehazing of images using polarization,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2001), 325–332.
  7. R. T. Tan, “Visibility in bad weather from a single image,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2008), 1–8.
  8. S. Shwartz, E. Namer, and Y. Y. Schechner, “Blind haze separation,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), 1984–1991.
  9. R. Fattal, “Single image dehazing,” ACM Trans. Graph.27(3), 72 (2008). [CrossRef]
  10. K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2009), 1956–1963.
  11. S. G. Narasimhan and S. K. Nayar, “Chromatic framework for vision in bad weather,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), 598–605.
  12. S. G. Narasimhan and S. K. Nayar, “Shedding light on the weather,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2003), 665–672.
  13. S. Metari and F. Desch, ênes, “A new convolution kernel for atmospheric point spread function applied to computer vision,” in Proceedings of IEEE Conference on Computer Vision (IEEE, 2007), 1–8.
  14. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Second Edition, (Publishing House of Electronics Industry, 2002), Chap. 5.
  15. M. Bertero and P. Boccacci, Introduction to Inverse Problems in Imaging (IOP, 1998), Chap. 5.
  16. A. Levin, D. Lischinski, and Y. Weiss, “A closed form solution to natural image matting,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2006), 61–68.
  17. W. Dong, Y. Chen, Z. Xu, H. Feng, and Q. Li, “Image stabilization with support vector machine,” J. Zhejiang Univ.-Sci. C Comput. & Electron.12(6), 478–485 (2011). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited