Bayesian estimation of regularization and point spread function parameters for Wiener–Hunt deconvolution

Bayesian estimation of regularization and point spread function parameters for Wiener-Hunt deconvolution.

This paper tackles the problem of image deconvolution with joint estimation of point spread function (PSF) parameters and hyperparameters. Within a Bayesian framework, the solution is inferred via a global a posteriori law for unknown parameters and object. The estimate is chosen as the posterior mean, numerically calculated by means of a Monte Carlo Markov chain algorithm. The estimates are ef...

Enhancement of Point Spread Function of Planar Fluorescent Imaging System Using Iterative Deconvolution Method

This article has no abstract.

Bayesian Regularization and Nonnegative Deconvolution for Time Delay Estimation

Bayesian Regularization and Nonnegative Deconvolution (BRAND) is proposed for estimating time delays of acoustic signals in reverberant environments. Sparsity of the nonnegative filter coefficients is enforced using an L1-norm regularization. A probabilistic generative model is used to simultaneously estimate the regularization parameters and filter coefficients from the signal data. Iterative ...

Tomographic Estimation of the Point-spread Function of an Ultrasound Imaging System for Deconvolution

Bayesian estimation of regularization parameters for deformable surface models

In this article we build on our past attempts to reconstruct a 3D, time-varying bolus of radiotracer from first-pass data obtained by the dynamic SPECT imager, FASTSPECT, built by the University of Arizona. The object imaged is a CardioWest total artificial heart. The bolus is entirely contained in one ventricle and its associated inlet and outlet tubes. The model for the radiotracer distributi...