Sparsity averaging for radio-interferometric imaging
Abstract
We propose a novel regularization method for compressive imaging in the context of the compressed sensing (CS) theory with coherent and redundant dictionaries. Natural images are often complicated and several types of structures can be present at once. It is well known that piecewise smooth images exhibit gradient sparsity, and that images with extended structures are better encapsulated in wavelet frames. Therefore, we here conjecture that promoting average sparsity or compressibility over multiple frames rather than single frames is an extremely powerful regularization prior.
Cite
@article{arxiv.1402.2335,
title = {Sparsity averaging for radio-interferometric imaging},
author = {Rafael E. Carrillo and Jason D. McEwen and Yves Wiaux},
journal= {arXiv preprint arXiv:1402.2335},
year = {2014}
}
Comments
1 page, 1 figure, Proceedings of the Biomedical and Astronomical Signal Processing Frontiers (BASP) workshop 2013, Related journal publications available at http://arxiv.org/abs/arXiv:1208.2330 and http://arxiv.org/abs/1307.4370