  Fluorescence microscopy has enabled a dramatic development in modern biology
by visualizing biological organisms with micrometer scale resolution. However,
due to the diffraction limit, sub-micron/nanometer features are difficult to
resolve. While various super-resolution techniques are developed to achieve
nanometer-scale resolution, they often either require expensive optical setup
or specialized fluorophores. In recent years, deep learning has shown the
potentials to reduce the technical barrier and obtain super-resolution from
diffraction-limited images. For accurate results, conventional deep learning
techniques require thousands of images as a training dataset. Obtaining large
datasets from biological samples is not often feasible due to the
photobleaching of fluorophores, phototoxicity, and dynamic processes occurring
within the organism. Therefore, achieving deep learning-based super-resolution
using small datasets is challenging. We address this limitation with a new
convolutional neural network-based approach that is successfully trained with
small datasets and achieves super-resolution images. We captured 750 images in
total from 15 different field-of-views as the training dataset to demonstrate
the technique. In each FOV, a single target image is generated using the
super-resolution radial fluctuation method. As expected, this small dataset
failed to produce a usable model using traditional super-resolution
architecture. However, using the new approach, a network can be trained to
achieve super-resolution images from this small dataset. This deep learning
model can be applied to other biomedical imaging modalities such as MRI and
X-ray imaging, where obtaining large training datasets is challenging.
