  The emerging generation of radio interferometric (RI) telescopes, such as the
Square Kilometre Array (SKA), will acquire massive volumes of data and
transition radio astronomy to a big-data era. The ill-posed inverse problem of
imaging the raw visibilities acquired by RI telescopes will become
significantly more computationally challenging, particularly in terms of data
storage and computational cost. Current RI imaging methods, such as CLEAN, its
variants, and compressive sensing approaches (sparse regularisation), have
yielded excellent reconstruction fidelity. However, scaling these methods to
big-data remains difficult if not impossible in some cases. All
state-of-the-art methods in RI imaging lack the ability to process data streams
as they are acquired during the data observation stage. Such approaches are
referred to as online processing methods. We present an online sparse
regularisation methodology for RI imaging. Image reconstruction is performed
simultaneously with data acquisition, where observed visibilities are
assimilated into the reconstructed image as they arrive and then discarded.
Since visibilities are processed online, good reconstructions are recovered
much faster than standard (offline) methods which cannot start until the data
acquisition stage completes. Moreover, the online method provides additional
computational savings and, most importantly, dramatically reduces data storage
requirements. Theoretically, the reconstructed images are of the same fidelity
as those recovered by the equivalent offline approach and, in practice, very
similar reconstruction fidelity is achieved. We anticipate online imaging
techniques, as proposed here, will be critical in scaling RI imaging to the
emerging big-data era of radio astronomy.
