WikiSysop: Created page with "== Citation == Agarwal, Ishaant / Kaczmar-Michalska, Joanna / Noerrelykke, Simon F. / Rzepiela, Andrzej. Refinement of Cryo-EM 3D Maps with Self-Supervised Denoising Model: crefDenoiser. 2024. IUCR J, Vol. 11, p. 821-830 == Abstract == Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the..."

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Created page with "== Citation == Agarwal, Ishaant / Kaczmar-Michalska, Joanna / Noerrelykke, Simon F. / Rzepiela, Andrzej. Refinement of Cryo-EM 3D Maps with Self-Supervised Denoising Model: crefDenoiser. 2024. IUCR J, Vol. 11, p. 821-830 == Abstract == Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging macromolecular structures. However, despite extensive processing of large image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio, the..."

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== Citation ==

Agarwal, Ishaant / Kaczmar-Michalska, Joanna / Noerrelykke, Simon F. / Rzepiela, Andrzej. Refinement of Cryo-EM 3D Maps with Self-Supervised Denoising Model: crefDenoiser. 2024.
IUCR J, Vol. 11, p. 821-830

== Abstract ==

Cryogenic electron microscopy (cryo-EM) is a pivotal technique for imaging
macromolecular structures. However, despite extensive processing of large
image sets collected in cryo-EM experiments to amplify the signal-to-noise ratio,
the reconstructed 3D protein-density maps are often limited in quality due to
residual noise, which in turn affects the accuracy of the macromolecular
representation. Here, crefDenoiser is introduced, a denoising neural network
model designed to enhance the signal in 3D cryo-EM maps produced with
standard processing pipelines. The crefDenoiser model is trained without the
need for ‘clean’ ground-truth target maps. Instead, a custom dataset is
employed, composed of real noisy protein half-maps sourced from the Electron
Microscopy Data Bank repository. Competing with the current state-of-the-art,
crefDenoiser is designed to optimize for the theoretical noise-free map during
self-supervised training. We demonstrate that our model successfully amplifies
the signal across a wide variety of protein maps, outperforming a classic map
denoiser and following a network-based sharpening model. Without biasing the
map, the proposed denoising method leads to improved visibility of protein
structural features, including protein domains, secondary structure elements and
modest high-resolution feature restoration.

== Keywords ==

== Links ==

https://journals.iucr.org/m/issues/2024/05/00/fq5024/index.html

== Related software ==

== Related methods ==

== Comments ==

2024Agarwal crefDenoiser - Revision history