Publications in Scientific Journals:
G. Kail, J.-Y. Tourneret, F. Hlawatsch, N. Dobigeon:
"Blind deconvolution of sparse pulse sequences under a minimum distance constraint: A partially collapsed Gibbs sampler method";
IEEE Transactions on Signal Processing,
For blind deconvolution of an unknown sparse sequence convolved with an unknown pulse, a powerful Bayesian method employs the Gibbs sampler in combination with a Bernoulli-Gaussian prior modeling sparsity. In this paper, we extend this method by introducing a minimum distance constraint for the pulses in the sequence. This is physically relevant in applications including layer detection, medical imaging, seismology, and multipath parameter estimation. We propose a Bayesian method for blind deconvolution that is based on a modified Bernoulli-Gaussian prior including a minimum distance constraint factor. The core of our method is a partially collapsed Gibbs sampler (PCGS) that tolerates and even exploits the strong local dependencies introduced by the minimum distance constraint. Simulation results demonstrate significant performance gains compared to a recently proposed PCGS. The main advantages of the minimum distance constraint are a substantial reduction of computational complexity and of the number of spurious components in the deconvolution result.
blind deconvolution, sparse deconvolution, Markov chain Monte Carlo method, partially collapsed Gibbs sampler, Bernoulli-Gaussian prior
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.