International Journal of Computer Vision

, Volume 98, Issue 2, pp 168–186 | Cite as

Non-uniform Deblurring for Shaken Images

  • Oliver WhyteEmail author
  • Josef Sivic
  • Andrew Zisserman
  • Jean Ponce


Photographs taken in low-light conditions are often blurry as a result of camera shake, i.e. a motion of the camera while its shutter is open. Most existing deblurring methods model the observed blurry image as the convolution of a sharp image with a uniform blur kernel. However, we show that blur from camera shake is in general mostly due to the 3D rotation of the camera, resulting in a blur that can be significantly non-uniform across the image. We propose a new parametrized geometric model of the blurring process in terms of the rotational motion of the camera during exposure. This model is able to capture non-uniform blur in an image due to camera shake using a single global descriptor, and can be substituted into existing deblurring algorithms with only small modifications. To demonstrate its effectiveness, we apply this model to two deblurring problems; first, the case where a single blurry image is available, for which we examine both an approximate marginalization approach and a maximum a posteriori approach, and second, the case where a sharp but noisy image of the scene is available in addition to the blurry image. We show that our approach makes it possible to model and remove a wider class of blurs than previous approaches, including uniform blur as a special case, and demonstrate its effectiveness with experiments on synthetic and real images.


Motion blur Blind deconvolution Camera shake Non-uniform/spatially-varying blur 


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Supplementary material

11263_2011_502_MOESM1_ESM.pdf (301 kb)
Non-uniform Deblurring for Shaken Images: Derivation of parameter update equations for blind de-blurring (PDF 301 kB)


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Oliver Whyte
    • 1
    • 4
    Email author
  • Josef Sivic
    • 1
    • 4
  • Andrew Zisserman
    • 2
    • 4
  • Jean Ponce
    • 3
    • 4
  1. 1.INRIAParisFrance
  2. 2.Department of Engineering ScienceUniversity of OxfordOxfordUK
  3. 3.Département d’InformatiqueEcole Normale SupérieureParisFrance
  4. 4.Willow Project, Laboratoire d’Informatique de l’Ecole Normale SupérieureCNRS/ENS/INRIA UMR 8548ParisFrance

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