1 research outputs found
Geometric Invariants for Sparse Unknown View Tomography
In this paper, we study a 2D tomography problem for point source models with
random unknown view angles. Rather than recovering the projection angles, we
reconstruct the model through a set of rotation-invariant features that are
estimated from the projection data. For a point source model, we show that
these features reveal geometric information about the model such as the radial
and pairwise distances. This establishes a connection between unknown view
tomography and unassigned distance geometry problem (uDGP). We propose new
methods to extract the distances and approximate the pairwise distance
distribution of the underlying points. We then use the recovered distribution
to estimate the locations of the points through constrained non-convex
optimization. Our simulation results verify the robustness of our point source
reconstruction pipeline to noise and error in the estimation of the features