Artefact suppression for explainability

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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.