Artefact suppression for explainability

该项目的目标是开发一种通用的机器学习方法，用于从NDE数据中去除良性伪影（例如几何反射），并展示其在特定检测任务中的用途，同时还考虑应用机器学习算法在无损评估（NDE）背景下对真实世界测量的可解释性。因此，我将寻求一种更通用的方法来抑制伪影，应用机器学习方法，1）容易推广到不同的模态; 2）预测伪影具有足够的保真度，允许它们从测量中减去（而不是简单地掩盖测量的那一部分），从而允许缺陷信号通过，即使它们完全或部分与伪影一致。

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.