Abstract

We present in this article the use of probabilistic background constraints in astronomical image deconvolution to approach a solution as an interval estimate. We elaborate our objective—the interval estimate of the unknown object from observed data and our approach—Monte Carlo experiment and analysis of marginal distributions of image values. One-dimensional observation and deconvolution using the proposed approach are simulated. Confidence intervals revealing the uncertainties due to the background constraint are calculated and significance levels for sources retrieved from restored images are provided.

© 2012 Optical Society of America

Strict a priori constraints for maximum-likelihood blind deconvolution

E. Thiébaut and J.-M. Conan
J. Opt. Soc. Am. A 12(3) 485-492 (1995)

Background estimation in nonlinear image restoration

Geert M. P. van Kempen and Lucas J. van Vliet
J. Opt. Soc. Am. A 17(3) 425-433 (2000)

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

François Orieux, Jean-François Giovannelli, and Thomas Rodet
J. Opt. Soc. Am. A 27(7) 1593-1607 (2010)

Deconvolution based on the Wiener–Lucy chain algorithm: an approach to recover local information losses in the deconvolution procedure

Fumiaki Tsumuraya
J. Opt. Soc. Am. A 13(7) 1532-1536 (1996)

Blind image deconvolution by means of asymmetric multiplicative iterative algorithm

Jianlin Zhang, Qiheng Zhang, and Guangming He
J. Opt. Soc. Am. A 25(3) 710-717 (2008)