We propose a novel algorithm for image reconstruction in radio interferometry. The ill-posed inverse problem associated with the incomplete Fourier sampling identified by the visibility measurements is regularized by the assumption of average signal sparsity over representations in multiple wavelet bases. The algorithm, defined in the versatile framework of convex optimization, is dubbed Sparsity Averaging Reweighted Analysis (SARA). We show through simulations that the proposed approach outperforms state-of-the-art imaging methods in the field, which are based on the assumption of signal sparsity in a single basis only.
@article{arxiv.1205.3123,
title = {Sparsity Averaging Reweighted Analysis (SARA): a novel algorithm for radio-interferometric imaging},
author = {R. E. Carrillo and J. D. McEwen and Y. Wiaux},
journal= {arXiv preprint arXiv:1205.3123},
year = {2012}
}
Comments
12 pages, 7 figures, replaced to fix typos in Figures 3a and 4a. Accepted in MRAS