Abstract
In this paper, we propose novel algorithms for total variation (TV) based image restoration and parameter estimation utilizing variational distribution approximations. Within the hierarchical Bayesian formulation, the reconstructed image and the unknown hyperparameters for the image prior and the noise are simultaneously estimated. The proposed algorithms provide approximations to the posterior distributions of the latent variables using variational methods. We show that some of the current approaches to TV-based image restoration are special cases of our framework. Experimental results show that the proposed approaches provide competitive performance without any assumptions about unknown hyperparameters and clearly outperform existing methods when additional information is included.
Original language | English (US) |
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Pages (from-to) | 326-339 |
Number of pages | 14 |
Journal | IEEE Transactions on Image Processing |
Volume | 17 |
Issue number | 3 |
DOIs | |
State | Published - Mar 2008 |
Funding
Manuscript received May 2, 2007; revised December 13, 2007. 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 Con-solider Ingenio 2010: MIPRCV (CSD2007-00018). Preliminary results of this work can be found in [1]. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Michael Elad.
Keywords
- Bayesian methods
- Image restoration
- Parameter estimation
- Total variation (TV)
- Variational methods
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design