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Access through your institution Buy or subscribe Ever since van Leeuwenhoek peered into his homemade microscope and revealed a world inhabited by “small animals,” scientists have been

pushing the limits of microscopy to see ever finer details in living cells and organisms. Though current methods would be science fiction to van Leeuwenhoek, we’ve entered an era of

diminishing returns: camera sensors are 95% efficient, modern lasers can evaporate samples, fluorescent molecules reliably emit thousands of photons, and objectives lenses hit their

fundamental physical performance limit over a century ago. Nevertheless, a “resolution revolution” seeded by Lukosz in the 1960s and heralded by Hell, Gustafsson and Betzig in the 2000s

introduced “super-resolution” (SR) to the scientific vernacular. In this issue of _Nature Methods_, Qiao et al.1 continue the revolution by riding the tidal wave of deep learning (DL) — a

framework rooted in the 1940s2 that has only recently enjoyed the computational muscle required by any but the simplest tasks3 — and present alchemic results: transforming low-resolution,

low-contrast, noisy images into super-resolved, high contrast, clean micrographs. Qiao et al.’s achievement is threefold. First, they collect an exceptional training dataset1, an invaluable

public resource for new method development, consisting of matched noisy, low-resolution images and high-quality, super-resolved, structured illumination microscopy (SIM, a variant of SR

microscopy) reconstructions. Second, they introduce two DL architectures, termed deep Fourier channel attention networks (DFCAN) and deep Fourier generative adversarial networks (DFGAN),

which, as their names imply, learn feature representations in the Fourier domain; and finally, they apply the networks to both the perennial problem of SIM reconstruction from nine

low-quality images and the more fantastical concept of single-image SR (SISR)4, in which an SR image is inferred entirely from a single diffraction-limited, or lower resolution, image. This

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ACCESS OPTIONS: * Log in * Learn about institutional subscriptions * Read our FAQs * Contact customer support REFERENCES * Qiao, C. _Nat. Methods_ https://doi.org/10.1038/s41592-020-01048-5

(2021). * McCulloch, W. S. & Pitts, W. _Bull. Math. Biophys._ 5, 115–133 (1943). Article  Google Scholar  * Silver, D. et al. _Nature_ 550, 354–359 (2017). Article  CAS  Google Scholar 

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https://cyberlaw.stanford.edu/publications/deep-fakes-looming-challenge-privacy-democracy-and-national-security (2018). * Wang, H. & Yeung, D.-Y. A survey on Bayesian deep learning. _ACM

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https://arxiv.org/abs/2011.03395 (2020). Download references AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * 10x Genomics, Pleasanton, CA, USA David P. Hoffman * DrivenData Inc, Denver, CO,

USA Isaac Slavitt & Casey A. Fitzpatrick Authors * David P. Hoffman View author publications You can also search for this author inPubMed Google Scholar * Isaac Slavitt View author

publications You can also search for this author inPubMed Google Scholar * Casey A. Fitzpatrick View author publications You can also search for this author inPubMed Google Scholar

CORRESPONDING AUTHOR Correspondence to David P. Hoffman. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. RIGHTS AND PERMISSIONS Reprints and permissions

ABOUT THIS ARTICLE CITE THIS ARTICLE Hoffman, D.P., Slavitt, I. & Fitzpatrick, C.A. The promise and peril of deep learning in microscopy. _Nat Methods_ 18, 131–132 (2021).

https://doi.org/10.1038/s41592-020-01035-w Download citation * Published: 21 January 2021 * Issue Date: February 2021 * DOI: https://doi.org/10.1038/s41592-020-01035-w SHARE THIS ARTICLE

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