Sampling theorems for inverse problems on Riemannian manifolds

Abstract

We consider inverse problems consisting of the reconstruction of an unknown signal $f$ from noisy measurements $y=Ff+\text{noise}$, where $Ff$ is a function on a Riemannian manifold without boundary $\mathcal{M}$. We consider the case when only pointwise samples are available, namely $y_j=(Ff)(x_j)+\hat{I}{\cdot }_j$, where $\{x_j{\}}_{j=1}^n\subseteq \mathcal{M}$ is a Marcinkiewicz-Zygmund family. We derive sampling theorems providing explicit bounds on the reconstruction error depending on $n$, the smoothness of $f$ and the properties of $F$. We study in detail the case when $F$ is a convolution on a compact two-point homogeneous space. As a corollary, we state a sampling theorem for convolutions on the two-dimensional sphere, and discuss four relevant examples related to terrestrial and celestial measurements.

31 pages, 2 figures