James R. Hopgood

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We propose a new image and blur prior model, based on non-stationary 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(More)
Enhancement of an unknown signal from distorted observations is an extremely important Engineering problem. In addition to noise, the observation space often contains a degrading filter component. A typical example is blind speech enhancement, where a reverberant channel between a stationary source and the receiver can be modeled as a static infinite(More)
The Variational Bayesian approach has recently been proposed to tackle the blind image restoration (BIR) problem. We consider extending the procedures to include realistic boundary modelling and non-stationary image restoration. Correctly modelling the boundaries is essential for achieving accurate blind restorations of photographic images, whilst(More)
Separability of signal mixtures given only one mixture observation is defined as the identification of the accuracy to which the signals can be separated. The paper shows that when signals are separated using the generalised Wiener filter, the degree of sepa-rability can be deduced from the filter structure. To identify this structure, the processes are(More)