PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Non-uniform Deblurring for Shaken Images
Oliver Whyte, Josef Sivic, Andrew Zisserman and Jean Ponce
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June 2010, San Francisco, CA, USA.

Abstract

Blur from camera shake is mostly due to the 3D rota- tion of the camera, resulting in a blur kernel that can be significantly non-uniform across the image. However, most current deblurring methods model the observed image as a convolution of a sharp image with a uniform blur kernel. We propose a new parametrized geometric model of the blurring process in terms of the rotational velocity of the camera during exposure. We apply this model to two different algorithms for camera shake removal: the first one uses a single blurry image (blind deblurring), while the second one uses both a blurry image and a sharp but noisy im- age of the same scene. 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 real images.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:6965
Deposited By:Karteek Alahari
Deposited On:25 June 2010