Iterative desensitisation of image restoration filters under wrong PSF and noise estimates
EURASIP Journal on Applied Signal Processing
Optimal estimation of deterioration from diagnostic image sequence
IEEE Transactions on Signal Processing
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
A robust hidden Markov Gauss mixture vector quantizer for a noisy source
IEEE Transactions on Image Processing
L1 prior majorization in Bayesian image restoration
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Blurred image recognition by Legendre moment invariants
IEEE Transactions on Image Processing
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Bayesian blind deconvolution from differently exposed image pairs
IEEE Transactions on Image Processing
An MLP neural net with L1 and L2 regularizers for real conditions of deblurring
EURASIP Journal on Advances in Signal Processing
A new algorithm for improving the resolution of Cryo-EM density maps
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
Total variation blind deconvolution employing split Bregman iteration
Journal of Visual Communication and Image Representation
Extensions of the Justen---Ramlau blind deconvolution method
Advances in Computational Mathematics
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Following the hierarchical Bayesian framework for blind deconvolution problems, in this paper, we propose the use of simultaneous autoregressions as prior distributions for both the image and blur, and gamma distributions for the unknown parameters (hyperparameters) of the priors and the image formation noise. We show how the gamma distributions on the unknown hyperparameters can be used to prevent the proposed blind deconvolution method from converging to undesirable image and blur estimates and also how these distributions can be inferred in realistic situations. We apply variational methods to approximate the posterior probability of the unknown image, blur, and hyperparameters and propose two different approximations of the posterior distribution. One of these approximations coincides with a classical blind deconvolution method. The proposed algorithms are tested experimentally and compared with existing blind deconvolution methods