2020/12/22 by Rémi Carloni Gertosio, J. Bobin, Jérôme Bobin
Computer Science · Engineering · Mathematics · #Algorithm #Artificial intelligence #Blind Source Separation Techniques #Blind deconvolution #Blind signal separation #Computer science #Deconvolution #Image and Signal Denoising Methods #Inverse problem #Joint (building) #Mathematical optimization #Mathematics #Regularization (linguistics) #Source separation #Sparse and Compressive Sensing Techniques #Telecommunications #eess.SP
paper · pdf · doi:10.1016/j.dsp.2020.102946
Accepted manuscript in Digital Signal Processing (Elsevier)
openalex publication_date 2020/12/22 · arxiv created 2020/12/23 · arxiv updated 2020/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.