We for the first time combine generated adversarial network (GAN) with wide-field 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  network can recover a high-resolution, accurate image of new specimen from its single low-resolution measurement. This capacity has been adequately demonstrated by imaging various types of samples, such as USAF resolution target, human pathological slides and fluorescence-labelled fibroblast cells . Their gigapixel, multi-color reconstructions verify a successful GAN-based single image super-resolution procedure. Furthermore, this deep learning-based imaging approach doesn;t necessarily introduce any change to the setup of a conventional wide-filed microscope, reconstructing large FOV ( about 95 mm^2 ), high-resolution ( about 1.7 {\mu}m) image at a high speed ( in 1 second) . As a result, GAN-microscopy opens a new way to computationally overcome the general challenge of high-throughput , high-resolution microscopy that is originally coupled to the physical limitation of system's optics .
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 .
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