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

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. • For live imaging, low exposure microscopy acquisitions are preferred to avoid photobleaching and sample toxicity. • A high level of noise makes further image analysis and understanding of the microscopy images difficult and less robust. • Supervised DL methods provide robust performance but require the curation of paired low and high-quality images. • Self-supervised DL methods provide an easy way to denoise images as they do not require the user to provide a paired dataset. • Recent DL methods combine denoising with other computational tasks such as segmentation or deconvolution. [ABSTRACT FROM AUTHOR]

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