  Many instruments performing optical and non-optical imaging and sensing, such
as Optical Coherence Tomography (OCT), Magnetic Resonance Imaging or
Fourier-transform spectrometry, produce digital signals containing modulations,
sine-like components, which only after Fourier transformation give information
about the structure or characteristics of the investigated object. Due to the
fundamental physics-related limitations of such methods, the distribution of
these signal components is often nonlinear and, when not properly compensated,
leads to the resolution, precision or quality drop in the final image. Here, we
propose an innovative approach that has the potential to allow cleaning of the
signal from the nonlinearities but most of all, it now allows to switch the
given order off, leaving all others intact. The latter provides a tool for more
in-depth analysis of the nonlinearity-inducing properties of the investigated
object, which can lead to applications in early disease detection or more
sensitive sensing of chemical compounds. We consider OCT signals and
nonlinearities up to the third order. In our approach, we propose two neural
networks: one to remove solely the second-order nonlinearity and the other for
removing solely the third-order nonlinearity. The input of the networks is a
novel two-dimensional data structure with all the information needed for the
network to infer a nonlinearity-free signal. We describe the developed networks
and present the results for second-order and third-order nonlinearity removal
in OCT data representing the images of various objects: a mirror, glass, and
fruits.
