High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Variational Bayesian blind deconvolution using a total variation prior
IEEE Transactions on Image Processing
Variational Bayesian sparse kernel-based blind image deconvolution with student's-t priors
IEEE Transactions on Image Processing
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Image Processing
Motion deblurring as optimisation
Proceedings of the Seventh Indian Conference on Computer Vision, Graphics and Image Processing
Blur estimation for barcode recognition in out-of-focus images
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Bayesian blind deconvolution with general sparse image priors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
Hi-index | 35.68 |
In this paper, the blind image deconvolution (BID) problem is addressed using the Bayesian framework. In order to solve for the proposed Bayesian model, we present a new methodology based on a variational approximation, which has been recently introduced for several machine learning problems, and can be viewed as a generalization of the expectation maximization (EM) algorithm. This methodology reaps all the benefits of a "full Bayesian model" while bypassing some of its difficulties. We present three algorithms that solve the proposed Bayesian problem in closed form and can be implemented in the discrete Fourier domain. This makes them very cost effective even for very large images. We demonstrate with numerical experiments that these algorithms yield promising improvements as compared to previous BID algorithms. Furthermore, the proposed methodology is quite general with potential application to other Bayesian models for this and other imaging problems.