An introduction to variational methods for graphical models
Learning in graphical models
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
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 Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Image Processing And Analysis: Variational, Pde, Wavelet, And Stochastic Methods
Large scale multikernel RVM for object detection
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
A variational approach for Bayesian blind image deconvolution
IEEE Transactions on Signal Processing
Total variation blind deconvolution
IEEE Transactions on Image Processing
Blind image restoration by anisotropic regularization
IEEE Transactions on Image Processing
A soft double regularization approach to parametric blind image deconvolution
IEEE Transactions on Image Processing
Blind deconvolution of images using optimal sparse representations
IEEE Transactions on Image Processing
Bayesian Restoration Using a New Nonstationary Edge-Preserving Image Prior
IEEE Transactions on Image Processing
Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A fast algorithm for robust mixtures in the presence of measurement errors
IEEE Transactions on Neural Networks
IEEE Transactions on Image Processing
Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps
IEEE Transactions on Image Processing
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In this paper, we present a new Bayesian model for the blind image deconvolution (BID) problem. The main novelty of this model is the use of a sparse kernel-based model for the point spread function (PSF) that allows estimation of both PSF shape and support. In the herein proposed approach, a robust model of the BID errors and an image prior that preserves edges of the reconstructed image are also used. Sparseness, robustness, and preservation of edges are achieved by using priors that are based on the Student's-t probability density function (PDF). This pdf, in addition to having heavy tails, is closely related to the Gaussian and, thus, yields tractable inference algorithms. The approximate variational inference methodology is used to solve the corresponding Bayesian model. Numerical experiments are presented that compare this BID methodology to previous ones using both simulated and real data.