Density Reconstruction from Noisy Radiographs using an Attention-based Transformer Network

Abstract

A trained attention-based transformer network can robustly recover density fields from a sequence of features derived from radiographic images corrupted with blur, scatter, and noise. This approach is demonstrated on imploding shell hydrodynamic simulations.