  Biological experiments based on organ-on-chips (OOCs) exploit light
Time-Lapse Microscopy (TLM) for a direct observation of cell movement that is
an observable signature of underlying biological processes. A high spatial
resolution is essential to capture cell dynamics and interactions from recorded
experiments by TLM. Unfortunately, due to physical and cost limitations,
acquiring high resolution videos is not always possible. To overcome the
problem, we present here a new deep learning-based algorithm that extends the
well known Deep Image Prior (DIP) to TLM Video Super Resolution (SR) without
requiring any training. The proposed Recursive Deep Prior Video (RDPV) method
introduces some novelties. The weights of the DIP network architecture are
initialized for each of the frames according to a new recursive updating rule
combined with an efficient early stopping criterion. Moreover, the DIP loss
function is penalized by two different Total Variation (TV) based terms. The
method has been validated on synthetic, i.e., artificially generated, as well
as real videos from OOC experiments related to tumor-immune interaction.
Achieved results are compared with several state-of-the-art trained deep
learning SR algorithms showing outstanding performances.
