Deep Learning Enhances Light Microscopy Efficiency
This is a news story, published by ScienceDaily, that relates primarily to Li news.
Li news
For more Li news, you can click here:
more Li newsemerging technologies news
For more emerging technologies news, you can click here:
more emerging technologies newsScienceDaily news
For more news from ScienceDaily, you can click here:
more news from ScienceDailyAbout the Otherweb
Otherweb, Inc is a public benefit corporation, dedicated to improving the quality of news people consume. We are non-partisan, junk-free, and ad-free. We use artificial intelligence (AI) to remove junk from your news feed, and allow you to select the best tech news, business news, entertainment news, and much more. If you like emerging technologies news, you might also like this article about
light microscopy image improvement. We are dedicated to bringing you the highest-quality news, junk-free and ad-free, about your favorite topics. Please come every day to read the latest microscopy images news, real microscopy datasets news, emerging technologies news, and other high-quality news about any topic that interests you. We are working hard to create the best news aggregator on the web, and to put you in control of your news feed - whether you choose to read the latest news through our website, our news app, or our daily newsletter - all free!
microscopy images datasetsScienceDaily
•Next step in light microscopy image improvement
79% Informative
New deep learning architecture enables higher efficiency compared to widely used methods.
New computational model is faster than traditional models while matching or even surpassing their images' quality.
The model, called Multi-Stage Residual-BCR Net (m-rBCR), was specifically developed for microscopy images.
Li suggested to advance the neural network architecture of a model called BCR-Net that itself was inspired by a frequency representation-based signal compression scheme introduced in the 1990s by Gregory Beylkin , Ronald Coifman , and Vladimir Rokhlin .
The team validated the m-rBCR model on four different datasets, two simulated microscopy images datasets and two real microscopy datasets.
VR Score
88
Informative language
93
Neutral language
60
Article tone
formal
Language
English
Language complexity
73
Offensive language
not offensive
Hate speech
not hateful
Attention-grabbing headline
not detected
Known propaganda techniques
not detected
Time-value
long-living
External references
no external sources
Source diversity
no sources
Affiliate links
no affiliate links