Deconvolution of Irregularly Subsampled Images

Ahmed Karam-Eldaly, Yoann Altmann, Antonios Perperidis, Stephen McLaughlin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Imaging technologies, such as coherent fibred bundle optical microscopy (FBOM), operate with irregularly-spaced sparse subsamples from their field of view. In this paper, we address the problem of data deconvolution for applications where the observed irregularly distributed samples are considered as a result of a convolution operator acting on original samples and corrupted by additive observation noise. We propose a hierarchical Bayesian model in which suitable prior distributions are assigned to the unknown model parameters, and compare two estimation strategies including Markov chain Monte Carlo (MCMC) and variational Bayes (VB), which are used to perform Bayesian inference using the posterior distribution. Simulations conducted on both synthetic and real datasets illustrate the benefits of the proposed methods in terms quality of deconvolution of the sparse samples.

Original languageEnglish
Title of host publication2018 IEEE Statistical Signal Processing Workshop (SSP)
PublisherIEEE
Pages603-607
Number of pages5
ISBN (Print)9781538615713
DOIs
Publication statusPublished - 30 Aug 2018
Event20th IEEE Statistical Signal Processing Workshop 2018 - Freiburg im Breisgau, Germany
Duration: 10 Jun 201813 Jun 2018

Conference

Conference20th IEEE Statistical Signal Processing Workshop 2018
Abbreviated titleSSP 2018
CountryGermany
CityFreiburg im Breisgau
Period10/06/1813/06/18

Keywords

  • Bayesian estimation
  • Deconvolution
  • Irregular spatial sampling
  • Markov chain Monte Carlo methods
  • Variational Bayes

ASJC Scopus subject areas

  • Signal Processing
  • Instrumentation
  • Computer Networks and Communications

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