Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction

Chinmay Belthangady; Loic A. Royer

doi:10.20944/preprints201812.0137.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 December 2018 / Approved: 11 December 2018 / Online: 11 December 2018 (17:03:14 CET)
Version 2 : Received: 4 February 2019 / Approved: 5 February 2019 / Online: 5 February 2019 (10:30:40 CET)

Deep Learning is a recent and important addition to the computational toolbox available for image reconstruction in fluorescence microscopy. We review state-of-the-art applications such as image restoration, super-resolution, and light-field imaging, and discuss how the latest Deep Learning research can be applied to other image reconstruction tasks such as structured illumination, spectral deconvolution, and sample stabilisation. Despite its successes, Deep Learning also poses significant challenges, has often misunderstood capabilities, and overlooked limits. We will address key questions, such as: What are the challenges in obtaining training data? Can we discover structures not present in the training data? And, what is the danger of inferring unsubstantiated image details?

microscopy, fluorescence, machine learning, deep learning, inverse problems, image reconstruction, image restoration, super-resolution, deconvolution, spectral unmixing

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Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction

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