Imaging in focus: an introduction to denoising bioimages in the era of deep learning

Romain F Laine, Guillaume Jacquemet, Alexander Krull

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Abstract

Fluorescence microscopy enables the direct observation of previously hidden dynamic processes of life, allowing profound insights into mechanisms of health and disease. However, imaging of live samples is fundamentally limited by the toxicity of the illuminating light and images are often acquired using low light conditions. As a consequence, images can become very noisy which severely complicates their interpretation. In recent years, deep learning (DL) has emerged as a very successful approach to remove this noise while retaining the useful signal. Unlike classical algorithms which use well-defined mathematical functions to remove noise, DL methods learn to denoise from example data, providing a powerful content-aware approach. In this review, we first describe the different types of noise that typically corrupt fluorescence microscopy images and introduce the denoising task. We then present the main DL-based denoising methods and their relative advantages and disadvantages. We aim to provide insights into how DL-based denoising methods operate and help users choose the most appropriate tools for their applications.

Original languageEnglish
Article number106077
Number of pages9
JournalThe International Journal of Biochemistry & Cell Biology
Volume140
Early online date20 Sept 2021
DOIs
Publication statusPublished - Nov 2021

Bibliographical note

Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.

Keywords

  • Deep Learning
  • Denoising
  • Live-cell imaging
  • Noise
  • Microscopy

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