PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake
Stefan Harmeling, Michael Hirsch and Bernhard Schölkopf
In: NIPS 2010, 6-9 Dec 2010, Vancouver, Canada.

Abstract

Modelling camera shake as a space-invariant convolution simplifies the problem of removing camera shake, but often insufficiently models actual motion blur such as those due to camera rotation and movements outside the sensor plane or when objects in the scene have different distances to the camera. In an effort to address these limitations, (i) we introduce a taxonomy of camera shakes, (ii) we build on a recently introduced framework for space-variant filtering by Hirsch et al. and a fast algorithm for single image blind deconvolution for space-invariant filters by Cho and Lee to construct a method for blind deconvolution in the case of space-variant blur, and (iii), we present an experimental setup for evaluation that allows us to take images with real camera shake while at the same time recording the space- variant point spread function corresponding to that blur. Finally, we demonstrate that our method is able to deblur images degraded by spatially-varying blur orig- inating from real camera shake, even without using additionally motion sensor information.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:7909
Deposited By:Stefan Harmeling
Deposited On:17 March 2011