Generalizable Denoising of Microscopy Images using Generative Adversarial Networks and Contrastive Learning
This addresses the challenge of data acquisition and over-fitting in microscopy image analysis, enabling few-shot learning for improved interpretation.
The paper tackled the problem of high noise in microscopy images by proposing a novel framework for few-shot denoising, combining GANs with contrastive learning and structure-preserving losses, which drastically reduced training data requirements while retaining denoising quality on three datasets.
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.