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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 12-26 |
| Number of pages | 15 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| State | Published - 2009 |
Funding
Manuscript received February 12, 2008; revised September 10, 2008. First published November 25, 2008; current version published December 12, 2008. Preliminary results of this work were presented at EUSIPCO, September 2007 [1]. This work was supported in part by the “Comisión Nacional de Ciencia y Tecnología” under contract TIC2007-65533 and in part by the Spanish research programme Consolider Ingenio 2010: MIPRCV (CSD2007-00018). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Stanley J. Reeves.
Keywords
- Bayesian methods
- Blind deconvolution
- Parameter estimation
- Total variation (TV)
- Variational methods
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design