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)
How to cite:
Belthangady, C.; Royer, L.A. Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction. Preprints2018, 2018120137 (doi: 10.20944/preprints201812.0137.v1).
Belthangady, C.; Royer, L.A. Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction. Preprints 2018, 2018120137 (doi: 10.20944/preprints201812.0137.v1).
Cite as:
Belthangady, C.; Royer, L.A. Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction. Preprints2018, 2018120137 (doi: 10.20944/preprints201812.0137.v1).
Belthangady, C.; Royer, L.A. Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction. Preprints 2018, 2018120137 (doi: 10.20944/preprints201812.0137.v1).
Abstract
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?
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
