Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the petabyte scale for SKA. Modern proximal optimization algorithms have shown a potential to significantly outperform clean thanks to their ability to inject complex image models to regularize the inverse problem for image formation from visibility data. They were also shown to be parallelizable over large data volumes thanks to a splitting functionality enabling the decomposition of the data into blocks, for parallel processing of block-specific data-fidelity terms involved in the objective function. Focusing on intensity imaging, the splitting functionality is further exploited in this work to decompose the image cube into spatiospectral facets, and enables parallel processing of facet-specific regularization terms in the objective function, leading to the ‘Faceted HyperSARA’ algorithm. Reliable heuristics enabling an automatic setting of the regularization parameters involved in the objective are also introduced, based on estimates of the noise level, transferred from the visibility domain to the domains where the regularization is applied. Simulation results based on a matlab implementation and involving synthetic image cubes and data close to gigabyte size confirm that faceting can provide a major increase in parallelization capability when compared to the non-faceted approach (HyperSARA).
- image processing - techniques
ASJC Scopus subject areas
- Astronomy and Astrophysics
- Space and Planetary Science