International Journal of Computer Vision
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Strong sub-and super-gaussianity
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Blind deconvolution using a normalized sparsity measure
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A variational approach for Bayesian blind image deconvolution
IEEE Transactions on Signal Processing
Variational Bayesian Super Resolution
IEEE Transactions on Image Processing
Fast removal of non-uniform camera shake
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
| Hi-index | 0.00 |
We present a general method for blind image deconvolution using Bayesian inference with super-Gaussian sparse image priors. We consider a large family of priors suitable for modeling natural images, and develop the general procedure for estimating the unknown image and the blur. Our formulation includes a number of existing modeling and inference methods as special cases while providing additional flexibility in image modeling and algorithm design. We also present an analysis of the proposed inference compared to other methods and discuss its advantages. Theoretical and experimental results demonstrate that the proposed formulation is very effective, efficient, and flexible.