Removing parasitic elements from Quantum Optical Coherence Tomography data with Convolutional Neural Networks

Q-OCT is a non-contact and non-invasive light-based imaging method which is gaining attention due to its increased image resolution and quality. The biggest, yet unresolved, disadvantage of Q-OCT is artefacts, additional elements cluttering the images and leading to a loss of the structural information in the obtained images. In our work, Convolutional Neural Network (CNN) is applied to remove artefacts from Quantum Optical Coherence Tomography (Q-OCT) images. In our approach, we train our model with computer-generated data instead of experimental images. The preliminary results show that such an approach is successful in retrieving artefact-free structural information, even for multilayer objects, for which this information is lost due to the number of induced artefacts. The limitations and challenges associated with our approach are identified and discussed.