6 papers
Bayesian variational regularization on the ball
Matthew A. Price, Jason D. McEwen
We develop variational regularization methods which leverage sparsity-promoting priors to solve severely ill posed inverse problems defined on the 3D ball (i.e. the solid sphere).…
Sparse image reconstruction on the sphere: a general approach with uncertainty quantification
Matthew A. Price, Luke Pratley, Jason D. McEwen
Inverse problems defined naturally on the sphere are becoming increasingly of interest. In this article we provide a general framework for evaluation of inverse problems on the sph…
Efficient Generalized Spherical CNNs
Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker +4
Many problems across computer vision and the natural sciences require the analysis of spherical data, for which representations may be learned efficiently by encoding equivariance…
Spherical Bayesian mass-mapping with uncertainties: full sky observations on the celestial sphere
Matthew A. Price, Jason D. McEwen, L. Pratley +1
To date weak gravitational lensing surveys have typically been restricted to small fields of view, such that the has been sufficiently satisfied.…
Sparse Bayesian mass-mapping with uncertainties: peak statistics and feature locations
Matthew A. Price, Xiaohao Cai, Jason D. McEwen +1
Weak lensing convergence maps - upon which higher order statistics can be calculated - can be recovered from observations of the shear field by solving the lensing inverse problem.…
Sparse Bayesian mass-mapping with uncertainties: local credible intervals
Matthew A. Price, Xiaohao Cai, Jason D. McEwen +2
Until recently mass-mapping techniques for weak gravitational lensing convergence reconstruction have lacked a principled statistical framework upon which to quantify reconstructio…