This repository contains the code used in the paper On learned operator correction in inverse problems. We investigate correcting for modelling errors in Photoacoustic Tomography (PAT) by training a neural network as operator correction.
The files training_*.py contain the code to train the correction for all corrections introduced in the paper. In order to run the code locally, it is required to change the paths for savepoints, operator models and data to the appropriate paths on your device. The Evaluation notebook contains routines to evaluate the trained model and to create the figures displayed in the paper.
The data and operators can be downloaded here and the weights of the trained network for the recursive forward-adjoint correction for both ball and vessel data, as introced in the paper, can be found here.