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arXiv · 2012.12740

Joint deconvolution and unsupervised source separation for data on the sphere

Rémi Carloni Gertosio·Jérôme Bobin
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Abstract

Tackling unsupervised source separation jointly with an additional inverse problem such as deconvolution is central for the analysis of multi-wavelength data. This becomes highly challenging when applied to large data sampled on the sphere such as those provided by wide-field observations in astrophysics, whose analysis requires the design of dedicated robust and yet effective algorithms. We therefore investigate a new joint deconvolution/sparse blind source separation method dedicated for data sampled on the sphere, coined SDecGMCA. It is based on a projected alternate least-squares minimization scheme, whose accuracy is proved to strongly rely on some regularization scheme in the present joint deconvolution/blind source separation setting. To this end, a regularization strategy is introduced that allows designing a new robust and effective algorithm, which is key to analyze large spherical data. Numerical experiments are carried out on toy examples and realistic astronomical data.

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Rémi Carloni Gertosio, Jérôme Bobin. 2020-12-23. Joint deconvolution and unsupervised source separation for data on the sphere. https://doi.org/10.1016/j.dsp.2020.102946

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