  In this study, a method has been developed to improve the resolution of
histological human placenta images. For this purpose, a paired series of high-
and low-resolution images have been collected to train a deep neural network
model that can predict image residuals required to improve the resolution of
the input images. A modified version of the U-net neural network model has been
tailored to find the relationship between the low resolution and residual
images. After training for 900 epochs on an augmented dataset of 1000 images,
the relative mean squared error of 0.003 is achieved for the prediction of 320
test images. The proposed method has not only improved the contrast of the
low-resolution images at the edges of cells but added critical details and
textures that mimic high-resolution images of placenta villous space.
