Advertisement

Automatic Exposure Correction of Consumer Photographs

  • Lu Yuan
  • Jian Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7575)

Abstract

We study the problem of automatically correcting the exposure of an input image. Generic auto-exposure correction methods usually fail in individual over-/under-exposed regions. Interactive corrections may fix this issue, but adjusting every photograph requires skill and time. This paper will automate the interactive correction technique by estimating the image specific S-shaped non-linear tone curve that best fits the input image. Our first contribution is a new Zone-based region-level optimal exposure evaluation, which would consider both the visibility of individual regions and relative contrast between regions. Then a detail-preserving S-curve adjustment is applied based on the optimal exposure to obtain the final output. We show that our approach enables better corrections comparing with popular image editing tools and other automatic methods.

Keywords

Input Image Exposure Evaluation Histogram Equalization Zone Region Tone Mapping 
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.
    Russ, J.C.: Image Processing HandBook, 3rd edn. CRC Press (1998)Google Scholar
  2. 2.
    Zuiderveld, K.: Contrast limited adaptive histograph equalization. In: Graphic Gems IV, pp. 474–485. Academic Press Professional, San Diego (1994)Google Scholar
  3. 3.
    CS5, A.P.: Adjust color and tonality with curves, Adobe Systems Inc. San Jose, CA (2010)Google Scholar
  4. 4.
    Bychkovsky, V., Paris, S., Chan, E., Durand, F.: Learning photographic global tonal adjustments with a database of input/output image pairs. In: CVPR (2011)Google Scholar
  5. 5.
    Ansel, A.: The Negative. The Ansel Adams Photography Series (1981)Google Scholar
  6. 6.
    Battiato, S., Bosco, A., Castorina, A., Messina, G.: Automatic image enhancement by content dependent exposure correction. Journal on Applied Signal Processing 12, 1849–1860 (2004)CrossRefGoogle Scholar
  7. 7.
    Bhukhanwala, S.A., Ramabadran, T.V.: Automated global enhancement of digitized photographs. IEEE Trans. on. Consumer Electronics 40, 1–10 (1994)CrossRefGoogle Scholar
  8. 8.
    dong Lee, K., Kim, S., Kim, S.D.: Dynamic range compression based on statistical analysis. In: ICIP (2009)Google Scholar
  9. 9.
    Kang, S.B., Kapoor, A., Lischinski, D.: Personalization of image enhancement. In: CVPR (2010)Google Scholar
  10. 10.
    Sobol, R.: Improving the retinex algorithm for rendering wide dynamic range photographs. Journal of Electronic Imaging 13(1) (2004)Google Scholar
  11. 11.
    Chesnokov, V.: Dynamic range compression preserving local image contrast. GB Patent 2417381 (2006)Google Scholar
  12. 12.
    Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. on Image Processing 6, 451–462 (1997)CrossRefGoogle Scholar
  13. 13.
    Nayar, S.K., Branzoi, V.: Adaptive dynamic range imaging: Optical control of pixel exposures over space and time. In: ICCV (2003)Google Scholar
  14. 14.
    Shimizu, S., Kondo, T., Kohashi, T., Tsuruta, M., Komuro, T.: A new algorithm of exposure control based on fuzzy logic for video cameras. In: ICCE (1992)Google Scholar
  15. 15.
    Yang, M., Wu, Y., Crenshaw, J., Augustine, B., Mareachen, R.: Face detection for automatic exposure control in handheld camera. In: ICVS (2006)Google Scholar
  16. 16.
    Ilstrup, D., Manduchi, R.: One-Shot Optimal Exposure Control. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 200–213. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Brajovic, V.: Brightness perception, dynamic range and noise: a unifying model for adaptive image sensors. In: CVPR (2004)Google Scholar
  18. 18.
    Safonov, I.: Automatic correction of amateur photos damaged by backlighting. GraphiCon (2006)Google Scholar
  19. 19.
    Mukherjee, J., Mitra, S.K.: Enhancement of color images by scaling the dct coefficients. IEEE Trans. on Image Processing 17, 1783–1794 (2008)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Ovsiannikov, I.: Backlit subject detection in an image. US Patent 7813545 (2010)Google Scholar
  21. 21.
    Reinhard, E., Stark, M., Shirley, P., Ferwerda, J.: Photographic tone reproduction for digital images. SIGGRAPH (2002)Google Scholar
  22. 22.
    Mertens, T., Kautz, J., Reeth, F.V.: Exposure fusion. In: Pacific Conf. on Computer Graphics and Applications (2007)Google Scholar
  23. 23.
    Dale, K., Johnson, M.K., Sunkavalli, K., Matusik, W., Pfister, H.: Image restoration using online photo collections. In: ICCV (2009)Google Scholar
  24. 24.
    Guo, D., Cheng, Y., Zhuo, S., Sim, T.: Correcting over-exposure in photographs. In: Proc. IEEE CVPR, pp. 515–521 (2010)Google Scholar
  25. 25.
    Lischinski, D., Farbman, Z., Uyttendaele, M., Szeliski, R.: Interactive local adjustment of tonal values. ACM Trans. on Graph. 25(3), 646–653 (2006)CrossRefGoogle Scholar
  26. 26.
    Xue, S., Agarwala, A., Dorsey, J., Rushmeier, H.: Understanding and improving the realism of image composites. ACM Trans. Graph. (2012)Google Scholar
  27. 27.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. IJCV 59 (2004)CrossRefGoogle Scholar
  28. 28.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR (2001)Google Scholar
  29. 29.
    Tao, L., Yuan, L., Sun, J.: Skyfinder: Attribute-based sky image search. ACM Trans. Graph. 28(3), 68:1–68:5 (2009)Google Scholar
  30. 30.
    He, K., Sun, J., Tang, X.: Guided Image Filtering. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 1–14. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lu Yuan
    • 1
  • Jian Sun
    • 1
  1. 1.Microsoft Research AsiaChina

Personalised recommendations