Journal
INTERNATIONAL JOURNAL OF BIOCHEMISTRY & CELL BIOLOGY
Volume 140, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.biocel.2021.106077
Keywords
Deep learning; Denoising; Live-cell imaging; Noise; Microscopy
Categories
Funding
- MRC Skills development fellowship [MR/T027924/1]
- Academy of Finland
- Sigrid Juselius Foundation
- Cancer Society of Finland
- Drug Discovery and Diagnostics
- Angstrombo Akademi University Research Foundation (CoE CellMech)
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Fluorescence microscopy enables direct observation of hidden dynamic processes of life, but image noise can complicate interpretation. Deep learning methods have emerged as successful approaches for denoising, providing a powerful content-aware solution.
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.
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