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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.
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Rafael Eduardo Carrillo Rangel, Jason Douglas McEwen, Yves Wiaux
Rafael Eduardo Carrillo Rangel, Vijay Kartik, Jean-Philippe Thiran, Yves Wiaux
Jason Douglas McEwen, Yves Wiaux