The angular differential imaging (ADI) is used to improve contrast in high resolution astronomical imaging. An example is the direct imaging of exoplanet in camera fed by Extreme Adaptive Optics. The subtraction of the main dazzling object to observe the faint companion was improved using Principal Component Analysis (PCA). It factorizes the positive astronomical frames into positive and negative components. On the contrary, the Nonnegative Matrix Factorization (NMF) uses only positive components, mimicking the actual composition of the long exposure images.

Arcidiacono, C., Simoncini, V. (2018). Approximate nonnegative matrix factorization algorithm for the analysis of angular differential imaging data. 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA : SPIE [10.1117/12.2311681].

Approximate nonnegative matrix factorization algorithm for the analysis of angular differential imaging data

Simoncini, V.
2018

Abstract

The angular differential imaging (ADI) is used to improve contrast in high resolution astronomical imaging. An example is the direct imaging of exoplanet in camera fed by Extreme Adaptive Optics. The subtraction of the main dazzling object to observe the faint companion was improved using Principal Component Analysis (PCA). It factorizes the positive astronomical frames into positive and negative components. On the contrary, the Nonnegative Matrix Factorization (NMF) uses only positive components, mimicking the actual composition of the long exposure images.
2018
Proceedings of SPIE - The International Society for Optical Engineering
111
114
Arcidiacono, C., Simoncini, V. (2018). Approximate nonnegative matrix factorization algorithm for the analysis of angular differential imaging data. 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA : SPIE [10.1117/12.2311681].
Arcidiacono, C.; Simoncini, V.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/663352
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