  Accurate segmentation of live cell images has broad applications in clinical
and research contexts. Deep learning methods have been able to perform cell
segmentations with high accuracy; however developing machine learning models to
do this requires access to high fidelity images of live cells. This is often
not available due to resource constraints like limited accessibility to high
performance microscopes or due to the nature of the studied organisms.
Segmentation on low resolution images of live cells is a difficult task. This
paper proposes a method to perform live cell segmentation with low resolution
images by performing super-resolution as a pre-processing step in the
segmentation pipeline.
