A non-local regularization strategy for image deconvolution
Pattern Recognition Letters
Variational Bayesian sparse kernel-based blind image deconvolution with student's-t priors
IEEE Transactions on Image Processing
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
IEEE Transactions on Image Processing
Local Bayesian image restoration using variational methods and Gamma-normal distributions
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Blind and semi-blind deblurring of natural images
IEEE Transactions on Image Processing
Maximum a posteriori video super-resolution using a new multichannel image prior
IEEE Transactions on Image Processing
Effective image restorations using a novel spatial adaptive prior
EURASIP Journal on Advances in Signal Processing
Non-stationary t-distribution prior for image source separation from blurred observations
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Transform based additive data hiding based on a hierarchical prior
ECC'11 Proceedings of the 5th European conference on European computing conference
A nonlinear level set model for image deblurring and denoising
The Visual Computer: International Journal of Computer Graphics
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In this paper, we propose a class of image restoration algorithms based on the Bayesian approach and a new hierarchical spatially adaptive image prior. The proposed prior has the following two desirable features. First, it models the local image discontinuities in different directions with a model which is continuous valued. Thus, it preserves edges and generalizes the on/off (binary) line process idea used in previous image priors within the context of Markov random fields (MRFs). Second, it is Gaussian in nature and provides estimates that are easy to compute. Using this new hierarchical prior, two restoration algorithms are derived. The first is based on the maximum a posteriori principle and the second on the Bayesian methodology. Numerical experiments are presented that compare the proposed algorithms among themselves and with previous stationary and non stationary MRF-based with line process algorithms. These experiments demonstrate the advantages of the proposed prior