MATLAB source code
Radio interferometric (RI) data are noisy under-sampled spatial Fourier components of the unknown radio sky affected by direction-dependent antenna gains. Failure to model these antenna gains accurately results in a radio sky estimate with limited fidelity and resolution. The RI inverse problem has been recently addressed via a joint calibration and imaging approach which consists in solving a non-convex minimisation task, involving suitable priors for the DDEs, namely temporal and spatial smoothness, and l1-sparsity for the unknown radio map, in the context of realistic RI simulations. Building on these developments, we propose to promote strong sparsity of the radio map. i.e. in l0 sense, via a non-convex regularisation function that is the log-sum penalty. The resulting minimisation task is addressed via a sequence of non-convex minimisation tasks composed of re-weighted l1 image priors, which are solved approximately. The method has been applied to real observations from the Very Large Array and has shown to be very efficient when it comes to the recovery of high fidelity high resolution radio maps.
The code is a MATLAB implementation of the proposed algorithm.