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Journal Articles International Journal of Computer Vision Year : 2011

Non-uniform Deblurring for Shaken Images


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.
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hal-01053877 , version 1 (13-09-2014)


  • HAL Id : hal-01053877 , version 1


Oliver Whyte, Josef Sivic, Andrew Zisserman, Jean Ponce. Non-uniform Deblurring for Shaken Images. International Journal of Computer Vision, 2011, 98 (2), pp.168-186. ⟨hal-01053877⟩
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