  We combine generative adversarial network (GAN) with light microscopy to
achieve deep learning super-resolution under a large field of view (FOV). By
appropriately adopting prior microscopy data in an adversarial training, the
neural network can recover a high-resolution, accurate image of new specimen
from its single low-resolution measurement. Its capacity has been broadly
demonstrated via imaging various types of samples, such as USAF resolution
target, human pathological slides, fluorescence-labelled fibroblast cells, and
deep tissues in transgenic mouse brain, by both wide-field and light-sheet
microscopes. The gigapixel, multi-color reconstruction of these samples
verifies a successful GAN-based single image super-resolution procedure. We
also propose an image degrading model to generate low resolution images for
training, making our approach free from the complex image registration during
training dataset preparation. After a welltrained network being created, this
deep learning-based imaging approach is capable of recovering a large FOV (~95
mm2), high-resolution (~1.7 {\mu}m) image at high speed (within 1 second),
while not necessarily introducing any changes to the setup of existing
microscopes.
