  Microscopy images often suffer from high levels of noise, which can hinder
further analysis and interpretation. Content-aware image restoration (CARE)
methods have been proposed to address this issue, but they often require large
amounts of training data and suffer from over-fitting. To overcome these
challenges, we propose a novel framework for few-shot microscopy image
denoising. Our approach combines a generative adversarial network (GAN) trained
via contrastive learning (CL) with two structure preserving loss terms
(Structural Similarity Index and Total Variation loss) to further improve the
quality of the denoised images using little data. We demonstrate the
effectiveness of our method on three well-known microscopy imaging datasets,
and show that we can drastically reduce the amount of training data while
retaining the quality of the denoising, thus alleviating the burden of
acquiring paired data and enabling few-shot learning. The proposed framework
can be easily extended to other image restoration tasks and has the potential
to significantly advance the field of microscopy image analysis.
