[go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A novel deghosting method for exposure fusion

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  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 92:1–31

    Article  Google Scholar 

  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 Francisco

  3. Gallo O, Troccoli AJ, Hu J, Pulli K, Kautz J (2015) Locally non-rigid registration for mobile hdr photography. In: CVPR Workshops

  4. Granados M, Seidel HP, Lensch HPA (2008) Background estimation from non-time sequence images. In: Proceedings of graphics interface 2008, pp. 33–40

  5. Granados M, Kim KI, Tompkin J, Theobalt C (2013) Automatic noise modeling for ghost-free hdr reconstruction. ACM Trans. Graph. pp. 201–201

  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. 277–284

  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. 499–512

    Chapter  Google Scholar 

  8. Hu J, Gallo O, Pulli K, Sun X (2013) Hdr deghosting: How to deal with saturation ? In: CVPR, pp. 1163–1170

  9. Jacobs K, Loscos C, Ward G (2008) Automatic high-dynamic range image generation for dynamic scenes. IEEE Comput Graph Appl 28(2):84–93

    Article  Google Scholar 

  10. Kalantari NK, Ramamoorthi R (2017) Deep high dynamic range imaging of dynamic scenes. ACM Transactions on Graphics (Proceedings of SIGGRAPH) 36(4):2017

    Google Scholar 

  11. Kang SB, Uyttendaele M, Winder S, Szeliski R (2003) High dynamic range video. ACM Trans Graph 22(3):319–325

    Article  Google Scholar 

  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)

  13. Karaduzovic K, Hasic J, Mantiuk R (2017) Assessment of multi-exposure HDR image deghosting methods. Comput Graph 63:1–17

    Article  Google Scholar 

  14. Khan EA, Akyz AO, Reinhard E (2006) Ghost removal in high dynamic range images. In: ICIP, pp. 2005–2008. IEEE

  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):161–171

    Article  Google Scholar 

  16. Min TH, Park RH, Chang S (2009) Histogram based ghost removal in high dynamic range images. ICME, pp. 530–533

  17. Mitsunaga T, Nayar SK (1999) Radiometric self calibration. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1374–1380

  18. Pedone M, Heikkil J (2008) Constrain propagation for ghost removal in high dynamic range images. In: VISAPP, pp. 36–41

  19. Reinhard E, Ward G, Pattanaik S, Debevec P (2005) High Dynamic Range Imaging:Acquisition, Display, and Image-Based Lighting. Morgan Kaufmann Publishers Inc

  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. ACM

  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):203:1–203:11

    Google Scholar 

  22. Sidibe D, Puech W, Strauss O (2009) Ghost detection and removal in high dynamic range images. EUSIPCO

  23. Srikantha A, Sidibe D (2012) Ghost detection and removal for high dynamic range images: recent advances. Image Commun 27(6):650–662

    Google Scholar 

  24. Tocci MD, Kiser C, Tocci N, Sen P (2011) A versatile hdr video production system. SIGGRAPH '11, pp. 1–10. ACM

  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):683–707

    Article  Google Scholar 

  26. Tursun OT, Akyüz AO, Erdem A, Erdem E (2016) An objective deghosting quality metric for HDR images. Computer Graphics Forum 35(2):139–152

    Article  Google Scholar 

  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):724–728

    Google Scholar 

  28. Zhang W, Cham WK (2012) Reference-guided exposure fusion in dynamic scenes. J Vis Comun Image Represent 23(3):467–475

    Article  Google Scholar 

  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. 530–536

  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):405–414

    Article  Google Scholar 

Download references

Acknowledgements

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).

Author information

Authors and Affiliations

  1. School of Computer Engineering, Jinling Institute of Technology, Nanjing, 211169, Jiangsu, China

    Chunmeng Wang

  2. College of Computer Engineering, Weifang University, Weifang, 261061, Shandong, China

    Chen He

Authors
  1. Chunmeng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chunmeng Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, C., He, C. A novel deghosting method for exposure fusion. Multimed Tools Appl 77, 31911–31928 (2018). https://doi.org/10.1007/s11042-018-6261-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6261-5

Keywords

Search

Navigation