  Quantitative phase microscopy (QPM) is a label-free technique that enables to
monitor morphological changes at subcellular level. The performance of the QPM
system in terms of spatial sensitivity and resolution depends on the coherence
properties of the light source and the numerical aperture (NA) of objective
lenses. Here, we propose high space-bandwidth QPM using partially spatially
coherent optical coherence microscopy (PSC-OCM) assisted with deep neural
network. The PSC source synthesized to improve the spatial sensitivity of the
reconstructed phase map from the interferometric images. Further, compatible
generative adversarial network (GAN) is used and trained with paired
low-resolution (LR) and high-resolution (HR) datasets acquired from PSC-OCM
system. The training of the network is performed on two different types of
samples i.e. mostly homogenous human red blood cells (RBC) and on highly
heterogenous macrophages. The performance is evaluated by predicting the HR
images from the datasets captured with low NA lens and compared with the actual
HR phase images. An improvement of 9 times in space-bandwidth product is
demonstrated for both RBC and macrophages datasets. We believe that the
PSC-OCM+GAN approach would be applicable in single-shot label free tissue
imaging, disease classification and other high-resolution tomography
applications by utilizing the longitudinal spatial coherence properties of the
light source.
