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Variational Bayesian Blind Deconvolution Using a Total Variation Prior
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DOI：10.1109/TIP.2008.2007354.png)
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Abstract
En 中文
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.
Keywords:
Bayesian methods
blind deconvolution
parameter estimation
total variation (TV)
variational methods
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