Variational Bayesian blind deconvolution using a total variation prior

Authors:
S. Derin Babacan;Rafael Molina;Aggelos K. Katsaggelos
Affiliations:
Department of Electrical Engineering and Computer Science, Northwestern University, IL;Departamento de Ciencias de la Computación e I.A., Universidad de Granada, Spain;Department of Electrical Engineering and Computer Science, Northwestern University, IL
Venue:
IEEE Transactions on Image Processing
Year:
2009

Citing 16
Cited 5

Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
An introduction to variational methods for graphical models

Learning in graphical models
Noise robust spatial gradient estimation for use in displacement estimation

ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 1)-Volume 1 - Volume 1
On the Effect of the Form of the Posterior Approximation in Variational Learning of ICA Models

Neural Processing Letters
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)
Total variation minimizing blind deconvolution with shock filter reference

Image and Vision Computing
A variational approach for Bayesian blind image deconvolution

IEEE Transactions on Signal Processing
A regularization approach to joint blur identification and image restoration

IEEE Transactions on Image Processing
Total variation blind deconvolution

IEEE Transactions on Image Processing
Fast, robust total variation-based reconstruction of noisy, blurred images

IEEE Transactions on Image Processing
Blind image restoration by anisotropic regularization

IEEE Transactions on Image Processing
Cosine transform based preconditioners for total variation deblurring

IEEE Transactions on Image Processing
Blind deconvolution of images using optimal sparse representations

IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation

IEEE Transactions on Image Processing
Parameter Estimation in TV Image Restoration Using Variational Distribution Approximation

IEEE Transactions on Image Processing

Variational Bayesian image restoration with a product of spatially weighted total variation image priors

IEEE Transactions on Image Processing
Blind and semi-blind deblurring of natural images

IEEE Transactions on Image Processing
Joint NDT image restoration and segmentation using gauss-Markov-Potts prior models and variational Bayesian computation

IEEE Transactions on Image Processing
Bayesian blind deconvolution from differently exposed image pairs

IEEE Transactions on Image Processing
MRF-Based blind image deconvolution

ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we present novel algorithms for total variation (TV) based blind deconvolution and parameter estimation utilizing a variational framework. Using a hierarchical Bayesian model, the unknown image, blur, and hyperparameters for the image, blur, and noise priors are estimated simultaneously. A variational inference approach is utilized so that approximations of the posterior distributions of the unknowns are obtained, thus providing a measure of the uncertainty of the estimates. Experimental results demonstrate that the proposed approaches provide higher restoration performance than non-TV-based methods without any assumptions about the unknown hyperparameters.