Multimedia Tools and Applications

, Volume 77, Issue 24, pp 31911–31928 | Cite as

A novel deghosting method for exposure fusion

  • Chunmeng WangEmail author
  • Chen He


A novel ghost-free exposure fusion method for generating an HDR image of a dynamic scene is presented in this paper. Given a sequence of input images with gradually increased exposures, due to the theory that the luminance is linearly depended on the exposure time (Mertens et al. Comput Graph Forum 28(1):161–171, 2009), each input image is normalized to make it have consistent luminance with a reference image. Then moving objects in the dynamic scene are detected using a modified difference method for further exposure fusion. Experiments and comparisons show that our method has advantage in deghosting when the reference image contains saturated regions and generate high-quality results with natural textures. Furthermore, our method has a largely improved timing performance compared with previous reference-guided methods.


Compression HDR Deghosting Exposure fusion Photometric relation 



This work is supported by the Project of High-level Talents Research Foundation of Jinling Institute of Technology (jit-b-201802) and the Project of Shandong Province Higher Educational Science and Technology Program under grant (No.J17 KB184).


  1. 1.
    Baker S, Scharstein D, Lewis J, Roth S, Black MJ, Szeliski R (2011) A database and evaluation methodology for optical flow. Int J Comput Vis 92:1–31CrossRefGoogle Scholar
  2. 2.
    Gallo O, Gelfand N, Chen W, Tico M, Pulli K (2009) Artifact-free high dynamic range imaging. In: Proceedings of the IEEE International Conference of Computational Photography (ICCP). San FranciscoGoogle Scholar
  3. 3.
    Gallo O, Troccoli AJ, Hu J, Pulli K, Kautz J (2015) Locally non-rigid registration for mobile hdr photography. In: CVPR WorkshopsGoogle Scholar
  4. 4.
    Granados M, Seidel HP, Lensch HPA (2008) Background estimation from non-time sequence images. In: Proceedings of graphics interface 2008, pp. 33–40Google Scholar
  5. 5.
    Granados M, Kim KI, Tompkin J, Theobalt C (2013) Automatic noise modeling for ghost-free hdr reconstruction. ACM Trans. Graph. pp. 201–201Google Scholar
  6. 6.
    Grosch T (2006) Fast and robust high dynamic range image generation with camera and object movement. In: Vision, Modeling and Visualization, RWTH Aachen, pp. 277–284Google Scholar
  7. 7.
    Hu J, Gallo O, Pulli K (2012) Exposure stacks of live scenes with hand-held cameras. In: Proceedings of the 12th European Conference on Computer Vision, ECCV'12, pp. 499–512CrossRefGoogle Scholar
  8. 8.
    Hu J, Gallo O, Pulli K, Sun X (2013) Hdr deghosting: How to deal with saturation ? In: CVPR, pp. 1163–1170Google Scholar
  9. 9.
    Jacobs K, Loscos C, Ward G (2008) Automatic high-dynamic range image generation for dynamic scenes. IEEE Comput Graph Appl 28(2):84–93CrossRefGoogle Scholar
  10. 10.
    Kalantari NK, Ramamoorthi R (2017) Deep high dynamic range imaging of dynamic scenes. ACM Transactions on Graphics (Proceedings of SIGGRAPH) 36(4):2017Google Scholar
  11. 11.
    Kang SB, Uyttendaele M, Winder S, Szeliski R (2003) High dynamic range video. ACM Trans Graph 22(3):319–325CrossRefGoogle Scholar
  12. 12.
    Kanita KH, Telalovic J, Mantiuk R (2014) Expert evaluation of deghosting algorithms for multi-exposure high dynamic range imaging. In: Second International Conference and SME Workshop on HDR imaging (HDRi2014)Google Scholar
  13. 13.
    Karaduzovic K, Hasic J, Mantiuk R (2017) Assessment of multi-exposure HDR image deghosting methods. Comput Graph 63:1–17CrossRefGoogle Scholar
  14. 14.
    Khan EA, Akyz AO, Reinhard E (2006) Ghost removal in high dynamic range images. In: ICIP, pp. 2005–2008. IEEEGoogle Scholar
  15. 15.
    Mertens T, Kautz J, Reeth FV (2009) Exposure fusion: a simple and practical alternative to high dynamic range photography. Comput Graph Forum 28(1):161–171CrossRefGoogle Scholar
  16. 16.
    Min TH, Park RH, Chang S (2009) Histogram based ghost removal in high dynamic range images. ICME, pp. 530–533Google Scholar
  17. 17.
    Mitsunaga T, Nayar SK (1999) Radiometric self calibration. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1374–1380Google Scholar
  18. 18.
    Pedone M, Heikkil J (2008) Constrain propagation for ghost removal in high dynamic range images. In: VISAPP, pp. 36–41Google Scholar
  19. 19.
    Reinhard E, Ward G, Pattanaik S, Debevec P (2005) High Dynamic Range Imaging:Acquisition, Display, and Image-Based Lighting. Morgan Kaufmann Publishers IncGoogle Scholar
  20. 20.
    Seetzen H, Heidrich W, Stuerzlinger W, Ward G, Whitehead L, Trentacoste M, Ghosh A, Vorozcovs A (2004) High dynamic range display systems. SIGGRAPH '04, pp.760–768. ACMGoogle Scholar
  21. 21.
    Sen P, Kalantari NK, Yaesoubi M, Darabi S, Goldman DB, Shechtman E (2012) Robust patch-based hdr reconstruction of dynamic scenes. ACM Transactions on Graphics (TOG) (Proceedings of SIGGRAPH Asia 2012) 31(6):203:1–203:11Google Scholar
  22. 22.
    Sidibe D, Puech W, Strauss O (2009) Ghost detection and removal in high dynamic range images. EUSIPCOGoogle Scholar
  23. 23.
    Srikantha A, Sidibe D (2012) Ghost detection and removal for high dynamic range images: recent advances. Image Commun 27(6):650–662Google Scholar
  24. 24.
    Tocci MD, Kiser C, Tocci N, Sen P (2011) A versatile hdr video production system. SIGGRAPH '11, pp. 1–10. ACMGoogle Scholar
  25. 25.
    Tursun OT, Akyz AO, Erdem A, Erdem E (2015) The state of the art in hdr deghosting: a survey and evaluation. Computer Graphics Forum 34(2):683–707CrossRefGoogle Scholar
  26. 26.
    Tursun OT, Akyüz AO, Erdem A, Erdem E (2016) An objective deghosting quality metric for HDR images. Computer Graphics Forum 35(2):139–152CrossRefGoogle Scholar
  27. 27.
    Yang W, Zhang T (1998) A new method for the detection of moving targets in complex scenes. Journal of Computer Research and Development 35(8):724–728Google Scholar
  28. 28.
    Zhang W, Cham WK (2012) Reference-guided exposure fusion in dynamic scenes. J Vis Comun Image Represent 23(3):467–475CrossRefGoogle Scholar
  29. 29.
    Zhang W, Kuen Cham W (2010) Gradient-directed composition of multi-exposure images.In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 530–536Google Scholar
  30. 30.
    Zimmer H, Bruhn A, Weickert J (2011) Freehand hdr imaging of moving scenes with simultaneous resolution enhancement. Computer Graphics Forum (Proceedings of Eurographics) 30(2):405–414CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer EngineeringJinling Institute of TechnologyNanjingChina
  2. 2.College of Computer EngineeringWeifang UniversityWeifangChina

Personalised recommendations