Modern signal processing (SP) methods rely very heavily on probability and statistics to
solve challenging SP problems. SP methods are now expected to deal with ever more …

This paper presents a new Bayesian model and algorithm used for depth and reflectivity
profiling using full waveforms from the time-correlated single-photon counting measurement …

This paper presents two new algorithms for the joint restoration of depth and reflectivity (DR)
images constructed from time-correlated single-photon counting measurements. Two …

We propose a Bayesian uncertainty quantification method for large-scale imaging inverse
problems. Our method applies to all Bayesian models that are log-concave, where maximum …

Many imaging problems require solving an inverse problem that is ill-conditioned or ill-
posed. Imaging methods typically address this difficulty by regularizing the estimation …

This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing,
accounting for endmember variability. The pixels are modeled by a linear combination of …

This paper presents two hierarchical Bayesian methods for performing maximum-a-
posteriori inference when the value of the regularisation parameter is unknown. The …

We developed a variational Bayesian learning framework for the infinite generalized
Dirichlet mixture model (ie a weighted mixture of Dirichlet process priors based on the …

This paper proposes a joint segmentation and deconvolution Bayesian method for medical
ultrasound (US) images. Contrary to piecewise homogeneous images, US images exhibit …

This paper presents a new Bayesian estimation technique for hidden Potts-Markov random
fields with unknown regularisation parameters, with application to fast unsupervised K-class …