Blind restoration of blurred photographs via AR modelling and MCMC

We propose a new image and blur prior model, based on nonstationary autoregressive (AR) models, and use these to blindly deconvolve blurred photographic images, using the Gibbs sampler. As far as we are aware, this is the first attempt to tackle a real-world blind image deconvolution (BID) problem using Markov chain Monte Carlo (MCMC) methods. We give examples with simulated and real out-of-focus images, which show the state-of-the-art results that the proposed approach provides.

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Tom E. Bishop, Rafael Molina, James R. Hopgood
• Keywords: Blurred Photographic Images, ICIP 2008, Image Processing, Markov Chain Monte Carlo, Real-world Blind Image