Exposure Stacks of Live Scenes with Hand-Held Cameras

  • Jun Hu
  • Orazio Gallo
  • Kari Pulli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7572)


Many computational photography applications require the user to take multiple pictures of the same scene with different camera settings. While this allows to capture more information about the scene than what is possible with a single image, the approach is limited by the requirement that the images be perfectly registered. In a typical scenario the camera is hand-held and is therefore prone to moving during the capture of an image burst, while the scene is likely to contain moving objects. Combining such images without careful registration introduces annoying artifacts in the final image. This paper presents a method to register exposure stacks in the presence of both camera motion and scene changes. Our approach warps and modifies the content of the images in the stack to match that of a reference image. Even in the presence of large, highly non-rigid displacements we show that the images are correctly registered to the reference.


Reference Image Camera Motion High Dynamic Range Camera Phone High Dynamic Range Image 
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.


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  1. 1.
    Ward, G.: Fast, robust image registration for compositing high-dynamic range photographs from handheld exposures. Journal of Graphics Tools 8, 17–30 (2003)CrossRefGoogle Scholar
  2. 2.
    Tzimiropoulos, G., Argyriou, V., Zafeiriou, S., Stathaki, T.: Robust FFT-based scale-invariant image registration with image gradients. IEEE Trans. on PAMI 32, 1899–1906 (2010)CrossRefGoogle Scholar
  3. 3.
    Tomaszewska, A., Mantiuk, R.: Image registration for multi-exposure high dynamic range image acquisition. In: Proc. of International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, pp. 49–56 (2007)Google Scholar
  4. 4.
    Jacobs, K., Loscos, C., Ward, G.: Automatic high-dynamic range image generation for dynamic scenes. IEEE CG&A 28, 84–93 (2008)Google Scholar
  5. 5.
    Gallo, O., Gelfand, N., Chen, W., Tico, M., Pulli, K.: Artifact-free high dynamic range imaging. In: Proc. of IEEE ICCP, pp. 1–7 (2009)Google Scholar
  6. 6.
    Reinhard, E., Heidrich, W., Pattanaik, S., Debevec, P., Ward, G., Myszkowski, K.: High Dynamic Range Imaging: Acquisition, Display and Image-Based Lighting. Morgan Kaufmann (2010)Google Scholar
  7. 7.
    Mann, S., Picard, R.: Being ‘undigital’ with digital cameras: Extending dynamic range by combining differently exposed pictures. In: Proc. of IS&T, pp. 422–428 (1995)Google Scholar
  8. 8.
    Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. In: ACM ToG (Proceedings of SIGGRAPH), pp. 369–378 (1997)Google Scholar
  9. 9.
    Akyüz, A.O., Reinhard, E.: Noise reduction in high dynamic range imaging. Journal of Visual Communication and Image Representation 18, 366–376 (2007)CrossRefGoogle Scholar
  10. 10.
    Hasinoff, S.W., Durand, F., Freeman, W.T.: Noise-optimal capture for high dynamic range photography. In: Proc. of IEEE CVPR, pp. 553–560 (2010)Google Scholar
  11. 11.
    Granados, M., Ajdin, B., Wand, M., Theobalt, C., Seidel, H., Lensch, H.: Optimal HDR reconstruction with linear digital cameras. In: Proc. of IEEE CVPR, pp. 215–222 (2010)Google Scholar
  12. 12.
    Robertson, M.A., Borman, S., Stevenson, R.L.: Estimation-theoretic approach to dynamic range enhancement using multiple exposures. Journal of Electronic Imaging 12, 219–228 (2003)CrossRefGoogle Scholar
  13. 13.
    Pece, F., Kautz, J.: HDR for dynamic scenes. In: Proc. of Conference for Visual Media Production (2010)Google Scholar
  14. 14.
    Eden, A., Uyttendaele, M., Szeliski, R.: Seamless image stitching of scenes with large motions and exposure differences. In: Proc. of IEEE CVPR, vol. 2, pp. 2498–2505 (2006)Google Scholar
  15. 15.
    Khan, E., Akyüz, A., Reinhard, E.: Ghost removal in high dynamic range images. In: Proc. of IEEE ICIP, pp. 2005–2008 (2006)Google Scholar
  16. 16.
    Zhang, W., Cham, W.K.: Gradient-directed composition of multi-exposure images. In: Proc. of IEEE CVPR, pp. 530–536 (2010)Google Scholar
  17. 17.
    Kang, S.B., Uyttendaele, M., Winder, S., Szeliski, R.: High dynamic range video. In: ACM ToG (Proceedings of SIGGRAPH), pp. 319–325 (2003)Google Scholar
  18. 18.
    Zimmer, H., Bruhn, A., Weickert, J.: Freehand HDR imaging of moving scenes with simultaneous resolution enhancement. In: CGF (Proc. of Eurographics), vol. 2, pp. 405–414 (2011)Google Scholar
  19. 19.
    Mertens, T., Kautz, J., Reeth, F.V.: Exposure fusion: A simple and practical alternative to high dynamic range photography. In: CGF, pp. 161–171 (2008)Google Scholar
  20. 20.
    HaCohen, Y., Shechtman, E., Goldman, D.B., Lischinski, D.: Non-rigid dense correspondence with applications for image enhancement. In: ACM ToG (Proceedings of SIGGRAPH), vol. 30, pp. 70:1–70:9 (2011)Google Scholar
  21. 21.
    Zwicker, M., Pfister, H., van Baar, J., Gross, M.: Surface splatting. In: ACM ToG (Proceedings of SIGGRAPH), pp. 371–378 (2001)Google Scholar
  22. 22.
    Liu, C., Yuen, J., Torralba, A.: SIFT flow: Dense correspondence across scenes and its applications. IEEE Trans. on PAMI, 978–994 (2011)Google Scholar
  23. 23.
    Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Trans. on PAMI, 500–513 (2011)Google Scholar
  24. 24.
    Kim, S.J., Lin, H.T., Lu, Z., Susstrunk, S., Lin, S., BrownHai, M.S.: A new in-camera imaging model for color computer vision and its application. IEEE Trans. on PAMI (2012)Google Scholar
  25. 25.
    Tico, M., Pulli, K.: Image enhancement method via blur and noisy image fusion. In: Proc. of ICIP, pp. 1521–1524 (2009)Google Scholar
  26. 26.
    Mann, S.: Comparametric equations with practical applications in quantigraphic image processing. IEEE Trans. on Image Processing 9, 1389–1406 (2000)zbMATHCrossRefGoogle Scholar
  27. 27.
    Grossberg, M., Nayar, S.: Determining the Camera Response from Images: What is Knowable? IEEE Trans. on PAMI 25, 1455–1467 (2003)CrossRefGoogle Scholar
  28. 28.
    Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM ToG 22, 313–318 (2003)Google Scholar
  29. 29.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jun Hu
    • 1
  • Orazio Gallo
    • 2
  • Kari Pulli
    • 2
  1. 1.Department of Computer ScienceDuke UniversityUSA
  2. 2.NVIDIA ResearchSanta ClaraUSA

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