2013/11/30 by Marco Selig, Torsten Enßlin, Torsten A. Enßlin · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #A priori and a posteriori #Adaptive optics and wavefront sensing #Advanced Image Processing Techniques #Bayesian inference #Bayesian probability #Energy (signal processing) #Image and Signal Denoising Methods #Inference #Maximum a posteriori estimation #Photon #Position (finance) #Probabilistic logic #astro-ph.IM #cs.IT #math.IT #physics.data-an #stat.CO
paper · pdf · doi:10.1051/0004-6361/201323006
published as A&A 574, A74 (2015) · 22 pages, 8 figures, 2 tables, accepted by Astronomy & Astrophysics; refereed version, 1 figure added, results unchanged, software available at http://www.mpa-garching.mpg.de/ift/d3po/
openalex publication_date 2015/01/28 · arxiv created 2015/01/29 · arxiv updated 2015/01/30 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
The analysis of astronomical images is a non-trivial task. The D3PO algorithm addresses the inference problem of denoising, deconvolving, and decomposing photon observations. Its primary goal is the simultaneous but individual reconstruction of the diffuse and point-like photon flux given a single photon count image, where the fluxes are superimposed. In order to discriminate between these morphologically different signal components, a probabilistic algorithm is derived in the language of information field theory based on a hierarchical Bayesian parameter model. The signal inference exploits prior information on the spatial correlation structure of the diffuse component and the brightness distribution of the spatially uncorrelated point-like sources. A maximum a posteriori solution and a solution minimizing the Gibbs free energy of the inference problem using variational Bayesian methods are discussed. Since the derivation of the solution is not dependent on the underlying position space, the implementation of the D3PO algorithm uses the nifty package to ensure applicability to various spatial grids and at any resolution. The fidelity of the algorithm is validated by the analysis of simulated data, including a realistic high energy photon count image showing a 32 × 32 arcmin2 observation with a spatial resolution of 0.1 arcmin. In all tests the D3PO algorithm successfully denoised, deconvolved, and decomposed the data into a diffuse and a point-like signal estimate for the respective photon flux components.