Tuesday publication post! 📖 In this recent publication from the #UniversiteeParisCite, authors Adrien Moncomble, Damien Alloyeau, Maxime Moreaud, Abdelali Khelfa, Guillaume Wang, Nathaly Ortiz-Peña, Hakim Amara, Riccardo Gatti, Romain Moreau, Christian Ricolleau and Jaysen Nelayah looked developed aquaDenoising—a deep learning framework trained on kinematic model-based simulations to dramatically enhance STEM images and videos!
What did they find by using the aquaDenoising technique?
📈 15x improvement in signal-to-noise ratio for gold NPs growing in water using the #PoseidonAX system.
💠 Authors were able to do automated segmentation of NP assemblies and individual particles with expert-level precision and high-throughput analysis that surpasses manual methods in speed and accuracy.
💻 This software is an open-source and is adaptable for various nanomaterials in liquid media.
By using AI-driven image processing with state-of-the-art liquid-phase EM, these processes can become automated, and can change liquid phase imaging to high-fidelity nanoscale imaging!
Want to read the entire work? Find it here!
https://linkinghub.elsevier.com/retrieve/pii/S2950257824000179
