Deep Learning-Based Image Restoration and Super-Resolution for Fluorescence Microscopy: Overview and Resources

Abstract

Fluorescence microscopy is a key method for the visualization of cellular, subcellular, and molecular live-cell dynamics, enabling access to novel insights into mechanisms of health and disease. However, effects like phototoxicity, the fugitive nature of signals, photo bleaching, and method-inherent noise can degrade the achievable signal-to-noise ratio and image resolution. In recent years, deep learning (DL) approaches have been increasingly applied to remove these degradations. In this review, we give a brief overview over existing classical and DL approaches for denoising, deconvolution, and computational super-resolution of fluorescence microscopy data. We summarize existing open-source databases within these fields as well as code repositories related to corresponding publications and further contribute an example project for DL-based image denoising, which provides a low barrier entry into DL coding and respective applications. In summary, we supply interested researchers with tools to apply or develop DL applications in live-cell imaging and foster research participation in this field.

Keywords: Computational super-resolution; Deconvolution; Deep learning; Denoising; Fluorescence microscopy; Image restoration.