Deep Clean: An Investigation into Machine Learning-based Approaches to Artefact Removal in Digitised Analog Photographic Images

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The goal of this research is to provide a reliable way to identify and correct artefacts in images which are introduced by the medium through which the data is captured. It aims to do so by exploiting the inner structure of pre-trained neural networks in order to fine tune them to decouple, i.e. "disentangle" image content information from artefacts introduced by the recording medium. As preserving useful, non-corrupted content information is an important requirement to the task of restoration, successfully separating content from artefacts requires an attention mechanism to be added to improve baseline deep learning approaches to generating restored data from corrupted input.