🚨Our preprint on DL for single-image super-resolution in microscopy is out!🌟 sl1nk.com/p62yedg
Our benchmark recaps an adventure of learning and questioning with @ivanhcenalmor.bsky.social & @iarganda.eurosky.social 🌟 how to objectively assess SISR models & ease the choice for their use in👉 biology🔬
Estibaliz Gómez de Mariscal, PhD
Super excited to share a project I’ve worked on since my Master’s thesis, what a journey!🚵
Finally out as a Nature Methods Stage 1 Registered Report preprint doi.org/10.6084/m9.f...
We benchmark deep learning-based single-image super-resolution methods for microscopy imaging.
Interested? Check out 🧵
Virtual super-resolution deep learning methods provide a powerful solution to overcome the physical and temporal constraints of microscopy imaging. Yet, assessing and choosing an ideal methodological strategy complicates their use in life sciences and creates a lack of trust in these methods. Here we propose an objective comparison of nine popular single-image super-resolution (SISR) models in a collection of publicly available microscopy datasets, including cell components like microtubules, endoplasmic reticulum, and actin, using confocal microscopy, SEM, SIM, SMLM and STED microscopy modalities for fixed and live-cell microscopy data. The proposed models will be assessed quantitatively with a collection of metrics in microscopy and computer vision, and qualitatively by experts in the field. The proposed models will be made accessible through open, user-friendly, containerised notebooks. This systematic assessment of SISR approaches will provide a more comprehensive understanding of these methods' performance and contribute to standardising SISR methods in microscopy.
