Sparse image reconstruction on the sphere: analysis and synthesis

Christopher G. R. Wallis, Yves Wiaux, Jason D. McEwen

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
79 Downloads (Pure)


We develop techniques to solve ill-posed inverse problems on the sphere by sparse regularisation, exploiting sparsity in both axisymmetric and directional scale-discretised wavelet space. Denoising, inpainting, and deconvolution problems, and combinations thereof, are considered as examples. Inverse problems are solved in both the analysis and synthesis settings, with a number of different sampling schemes. The most effective approach is that with the most restricted solution-space, which depends on the interplay between the adopted sampling scheme, the selection of the analysis/synthesis problem, and any weighting of the l1 norm appearing in the regularisation problem. More efficient sampling schemes on the sphere improve reconstruction fidelity by restricting the solution-space and also by improving sparsity in wavelet space. We apply the technique to denoise Planck 353 GHz observations, improving the ability to extract the structure of Galactic dust emission, which is important for studying Galactic magnetism.
Original languageEnglish
Pages (from-to)5176-5187
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number11
Early online date16 Jun 2017
Publication statusPublished - Nov 2017


  • cs.IT
  • astro-ph.GA
  • astro-ph.IM
  • math.IT


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