University of Washington,  Microsoft Research
Deblurred image (our result)
Deblurred using spatially-invariant method (Shan et al.)
this paper, we present a novel single image deblurring method to handle
camera shake motion that leads to spatially nonuniform blur kernels.
Existing spatially-invariant deconvolution methods are used in a local
and robust way to initialize priors for portions of the latent image.
The camera motion is represented as a Motion Density Function (MDF)
which records the fraction of time spent in each discretized portion of
the space of all possible camera poses. Spatially varying blur kernels
can then be derived directly from the MDF. We specify sparsity and
compactness priors over the MDF and formulate an optimization problem
to iteratively solve for both the MDF and the deblurred image. We show
that general 6D camera motion is well approximated by 3 degrees of
motion (in-plane translation and rotation) and analyze the scope of
this approximation. We present results on both synthetic and captured
data. Our system out-performs the current state of the art approaches
which makes the assumption of spatial invariance of the blur kernels.
Gupta A., Joshi N., Zitnick
L.,Cohen M., Curless B. Single
Using Motion Density Functions. In
Proceedings of European Conference on Computer Vision (ECCV) 2010.
This work was supported by funding from by funding from the University
of Washington Animation Research Labs, Microsoft, Adobe, and Pixar. We
would like to thank Qi Shan for useful discussions about performance of
existing deblurring methods and for providing non-blind image
deblurring code from his research.
Send any comments or questions to Ankit Gupta (Email : ankit [at] cs
[dot] washington [dot] edu)