SparseRI: A Compressed Sensing Framework for Aperture Synthesis Imaging in Radio Astronomy
In radio interferometry, information about a small region of the sky is obtained in the form of samples in the Fourier transform domain of the desired image. Since this sampling is usually incomplete, the missing information has to be reconstructed using additional assumptions about the image. The emerging field of Compressed Sensing (CS) provides a promising new approach to this type of problem which is based on the supposed sparsity of natural images in some transform domain. We present a versatile CS-based image reconstruction framework called SparseRI, an interesting alternative to the clean algorithm, that permits a wide choice of different regularisers for interferometric image reconstruction. The performance of our method is evaluated on simulated data as well as on actual radio interferometry measurements from the VLA, showing that our algorithm is able to reproduce the main features of the test sources. The proposed method is a first step towards an alternative reconstruction approach that may be able to avoid typical artefacts like negative flux regions, work with large fields of view and non-coplanar baselines, avoid the gridding process, and reduce the amount of manual work that is required in order to obtain best-quality results.