Advertisement

One-Shot Optimal Exposure Control

  • David Ilstrup
  • Roberto Manduchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6311)

Abstract

We introduce an algorithm to estimate the optimal exposure parameters from the analysis of a single, possibly under- or over-exposed, image. This algorithm relies on a new quantitative measure of exposure quality, based on the average rendering error, that is, the difference between the original irradiance and its reconstructed value after processing and quantization. In order to estimate the exposure quality in the presence of saturated pixels, we fit a log-normal distribution to the brightness data, computed from the unsaturated pixels. Experimental results are presented comparing the estimated vs. “ground truth” optimal exposure parameters under various illumination conditions.

Keywords

Exposure Control Automatic Exposure Control Photon Noise Optimal Exposure Saturated Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Muramatsu, M.: Photometry device for a camera (1997)Google Scholar
  2. 2.
    Johnson, B.K.: Photographic exposure control system and method (1997)Google Scholar
  3. 3.
    Kremens, R., Sampat, N., Venkataraman, S., Yeh, T.: System implications of implementing auto-exposure on consumer digital cameras. In: Proceedings of the SPIE Electronic Imaging Conference, vol. 3650 (1999)Google Scholar
  4. 4.
    Shimizu, S., Kondo, T., Kohashi, T., Tsuruta, M., Komuro, T.: A new algorithm for exposure control based on fuzzy logic for video cameras. IEEE Transactions on Consumer Electronics 38, 617–623 (1992)CrossRefGoogle Scholar
  5. 5.
    Yang, M., Crenshaw, J., Augustine, B., Mareachen, R., Wu, Y.: Face detection for automatic exposure control in handheld camera. In: IEEE International Conference on Computer Vision Systems (2006)Google Scholar
  6. 6.
    Nuske, S., Roberts, J., Wyeth, G.: Extending the range of robotic vision. In: IEEE International Conference on Robotics and Automation (2006)Google Scholar
  7. 7.
    Nourani-Vatani, N., Roberts, J.: Automatic exposure control. In: Australasian Conference on Robotics and Automation (2007)Google Scholar
  8. 8.
    Kuno, T., Matoba, N.: A new automatic exposure system for digital still cameras. IEEE Transactions on Consumer Electronics 44, 192–199 (1998)CrossRefGoogle Scholar
  9. 9.
    Nayar, S., Branzoi, V.: Adaptive dynamic range imaging: optical control of pixel exposures over space and time. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2, pp. 1168–1175 (2003)Google Scholar
  10. 10.
    Schulz, S., Grimm, M., Grigat, R.R.: Optimum auto exposure based on high-dynamic-range histogram. In: Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation (ISPRA’07), Stevens Point, Wisconsin, USA, pp. 269–274. World Scientific and Engineering Academy and Society, WSEAS (2007)Google Scholar
  11. 11.
    Grossberg, M., Nayar, S.: High dynamic range from multiple images: Which exposures to combine? In: Proceedings of the ICCV Workshop on Color and Photometric Methods in Computer Vision, CPMCV (2003)Google Scholar
  12. 12.
    Barakat, N., Hone, A., Darcie, T.: Minimal-bracketing sets for high-dynamic-range image capture. IEEE Transactions on Image Processing 17, 1864–1875 (2008)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Mitsunaga, T., Nayar, S.K.: Radiometric self calibration. Proceedings of the IEEE Computer Vision and Pattern Recognition 1, 1374 (1999)Google Scholar
  14. 14.
    Matsushita, Y., Lin, S.: Radiometric calibration from noise distributions. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR ’07 (2007)Google Scholar
  15. 15.
    Gersho, A., Gray, R.: Vector quantization and signal compression. Kluwer Academic Publishers, Norwell (1991)Google Scholar
  16. 16.
    Anon, E., Grey, T.: Photoshop for Nature Photographers: A Workshop in a Book. Wiley, Chichester (2005)Google Scholar
  17. 17.
    Miller, R., Gong, G., Muñoz, A.: Survival analysis. Wiley, New York (1981)zbMATHGoogle Scholar
  18. 18.
    Gross, Shulamith, T., LaiTze, L.: Nonparametric estimation and regression analysis with left-truncated and right-censored data. Journal of the American Statistical Association 91, 1166–1180 (1996)zbMATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    Richards, W.: Lightness scale from image intensity distributions. Applied Optics 21, 2569–2582 (1982)CrossRefGoogle Scholar
  20. 20.
    Ruderman, D.: The statistics of natural images. Network: computation in neural systems 5, 517–548 (1994)zbMATHCrossRefGoogle Scholar
  21. 21.
    Point Grey Research, Inc. Point Grey Dragonfly2 Technical Specification (2007)Google Scholar
  22. 22.
    Chen, T., El Gamal, A.: Optimal scheduling of capture times in a multiple capture imaging system. In: Proc. SPIE, vol. 4669, pp. 288–296 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • David Ilstrup
    • 1
  • Roberto Manduchi
    • 1
  1. 1.University of CaliforniaSanta Cruz

Personalised recommendations