Artefact suppression for explainability

The objective of the project would be to develop a general machine learning method for removing benign
artefacts (e.g. geometric reflections) from NDE data and demonstrate its use for specific inspection tasks, whilst
also considering the explainability of the applied machine learning algorithm to real-world measurements in the
Non-Destructive Evaluation (NDE) context. Therefore, I shall be seeking a more general approach to artefact
suppression with applied machine learning methods that 1) Generalises easily to different modalities; and 2)
Predicts artefacts with sufficient fidelity allowing them to be subtracted from a measurement (rather than simply
masking that part of a measurement), thus allowing defect signals to pass even if they wholly or partly coincide
with an artefact.