D3PO – Denoising, Deconvolving, and Decomposing Photon Observations

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. 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.

Reference

[1]Selig et. al., “Denoising, Deconvolving, and Decomposing Photon Observations”, accepted by Astronomy & Astrophysics, A&A, vol. 574, id. A74, 2014; arXiv:1311.1888

Note

Parts of this publication can with or without modification be found within the source code and this online documentation for obvious reasons, and they are not explicitly marked as quotations.

Documentation

Welcome to D3PO’s documentation!

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For a quickstart, download D3PO and check out the Demo.

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