Sparsity averaging for radio-interferometric imaging
This addresses the challenge of compressive imaging in radio astronomy by improving reconstruction quality for images with mixed smooth and extended features, though it appears incremental as it builds on existing compressed sensing and sparsity theories.
The paper tackles the problem of imaging with radio-interferometric data by proposing a regularization method that promotes average sparsity over multiple frames, such as gradients and wavelets, to better capture complex natural images with varied structures.
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.