Towards retrieving dispersion profiles using quantum-mimic Optical Coherence Tomography and Machine Learnin
This addresses a domain-specific problem for optical imaging researchers by enabling dispersion profiling from artefacts, though it appears incremental as it builds on existing quantum-mimic methods.
The paper tackled the problem of artefacts in quantum-mimic Optical Coherence Tomography by using machine learning to retrieve Group Velocity Dispersion profiles from these artefacts, achieving good qualitative representation for simulated and experimental data.
Artefacts in quantum-mimic Optical Coherence Tomography are considered detrimental because they scramble the images even for the simplest objects. They are a side effect of autocorrelation which is used in the quantum entanglement mimicking algorithm behind this method. Interestingly, the autocorrelation imprints certain characteristics onto an artefact - it makes its shape and characteristics depend on the amount of dispersion exhibited by the layer that artefact corresponds to. This unique relationship between the artefact and the layer's dispersion can be used to determine Group Velocity Dispersion (GVD) values of object layers and, based on them, build a dispersion-contrasted depth profile. The retrieval of GVD profiles is achieved via Machine Learning. During training, a neural network learns the relationship between GVD and the artefacts' shape and characteristics, and consequently, it is able to provide a good qualitative representation of object's dispersion profile for never-seen-before data: computer-generated single dispersive layers and experimental pieces of glass.