Skip to main content

Motion Blur

  • Living reference work entry
  • First Online:
Computer Vision
  • 209 Accesses

Synonyms

Camera-shake blur; Object motion blur

Related Concepts

Definition

Motion blur is due to motion of scene objects or the camera, while the camera shutter is open, thus causing scene points to be imaged over a large area of camera sensor or film. The motion blur is a projection of the motion path of the moving objects onto the image plane. The motion path of a point can be due to translation and rotation of the camera or scene objects in three dimensions. There can be different paths for different parts of the scene, and in light-limited situations, when using long exposures, these paths can be quite large, resulting in very large blurs.

Background

Image blur can be described by a point spread function (PSF). A PSF models how an imaging system captures a single point in the world – it literally describes how a point spreads across an image. An entire image is then made up of a sum of the...

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

Access this chapter

Institutional subscriptions

References

  1. Joshi N, Szeliski R, Kriegman DJ (2008) PSF estimation using sharp edge prediction. In: IEEE conference on computer vision and pattern recognition, CVPR 2008, pp 1–8

    Google Scholar 

  2. Levin A, Weiss Y, Durand F, Freeman W (2009) Understanding and evaluating blind deconvolution algorithms. In: IEEE conference on computer vision and pattern recognition, CVPR 2009. IEEE Computer Society, pp 1964–1971

    Google Scholar 

  3. Joshi N, Kang SB, Zitnick CL, Szeliski R (2010) Image deblurring using inertial measurement sensors. ACM Trans Graph 29:30:1–30:9

    Google Scholar 

  4. Bascle B, Blake A, Zisserman A (1996) Motion deblurring and super-resolution from an image sequence. In: ECCV ’96: Proceedings of the 4th European conference on computer vision, vol II, London, UK. Springer, pp 573–582

    Google Scholar 

  5. Fergus R, Singh B, Hertzmann A, Roweis ST, Freeman WT (2006) Removing camera shake from a single photograph. ACM Trans Graph 25:787–794

    Article  Google Scholar 

  6. Yuan L, Sun J, Quan L, Shum HY (2007) Image deblurring with blurred/noisy image pairs. In: SIGGRAPH ’07: ACM SIGGRAPH 2007 papers, New York. ACM 1

    Google Scholar 

  7. Ben-Ezra M, Nayar S (2004) Motion-based Motion Deblurring. IEEE Trans Pattern Anal Mach Intell 26(6):689–698

    Article  Google Scholar 

  8. Tai YW, Du H, Brown MS, Lin S (2008) Image/video deblurring using a hybrid camera. In: IEEE conference on computer vision and pattern recognition (CVPR 2008), pp 1–8

    Google Scholar 

  9. Park SY, Park ES, Kim HI (2008) Image deblurring using vibration information from 3-axis accelerometer. J Inst Electron Eng Korea Syst Control 45(3): 1–11

    Google Scholar 

  10. Levin A (2006) Blind motion deblurring using image statistics. In: Advances in neural information processing systems. MIT Press

    Google Scholar 

  11. Jia J (2007) Single image motion deblurring using transparency, pp 1–8

    Google Scholar 

  12. Qi Shan WX, Jia J (2007) Rotational motion deblurring of a rigid object from a single image. In: ICCV 2007

    Google Scholar 

  13. Levin A, Sand P, Cho TS, Durand F, Freeman WT (2008) Motion-invariant photography. ACM Trans Graph 27:71:1–71:9

    Google Scholar 

  14. Richardson WH (1972) Bayesian-based iterative method of image restoration (1917–1983). J Opt Soc Am 62:55–59

    Article  Google Scholar 

  15. Levin A, Fergus R, Durand F, Freeman WT (2007) Image and depth from a conventional camera with a coded aperture. In: SIGGRAPH ’07: ACM SIGGRAPH 2007 papers, New York. ACM Press 70

    Google Scholar 

  16. Raskar R, Agrawal A, Tumblin J (2006) Coded exposure photography: motion deblurring using fluttered shutter. ACM Trans Graph 25:795–804

    Article  Google Scholar 

  17. Gong D, Yang J, Liu L, Zhang Y, Reid I, Shen C, van den Hengel A, Shi Q (2017) From motion blur to motion flow: a deep learning solution for removing heterogeneous motion blur. In: IEEE conference on computer vision and pattern recognition (CVPR 2017)

    Google Scholar 

  18. Sun J, Ponce J (2015) Learning a convolutional neural network for non-uniform motion blur removal. In: IEEE conference on computer vision and pattern recognition (CVPR 2015), pp 769–777

    Google Scholar 

  19. Xu X, Pan J, Zhang Y, Yang M (2018) Motion blur kernel estimation via deep learning. IEEE Trans Image Process 27(1):194–205

    Article  MathSciNet  Google Scholar 

  20. Su S, Delbracio M, Wang J, Sapiro G, Heidrich W, Wang O (2017) Deep video deblurring for hand-held cameras. In: IEEE conference on computer vision and pattern recognition (CVPR 2017)

    Google Scholar 

  21. Wieschollek P, Hirsch M, Scholkopf B, Lensch HPA (2017) Learning blind motion deblurring. In: IEEE international conference on computer vision (ICCV 2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Neel Joshi .

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Joshi, N. (2020). Motion Blur. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_512-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03243-2_512-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics