Dragonfly Daily 16 Denoising with Deep Learning in Dragonfly (2020) скачать в хорошем качестве

Dragonfly Daily 16 Denoising with Deep Learning in Dragonfly (2020)

Hosted by Mike Marsh, Dragonfly Product Manager at ORS Download and Get Started with Dragonfly https://www.theobjects.com/dragonfly/... This is lesson 16 in an ongoing daily tutorial series that teaches new users how to become Dragonfly experts in no time. This lesson introduces users to Denoising with Deep Learning in Dragonfly More about Dragonfly Daily (register for live stream) https://theobjects.com/dragonfly/lear... Watch the Playlist with all of the Dragonfly Daily tutorials on YouTube http://orss.ca/ytp2 Topics in this tutorial 1-How to make a neural network model 2-The training pattern: pair inputs with outputs (cheap images with expensive images) 3-Denoising images Intuitive: High-Noise - Low-Noise Unexpected: High-Noise -High-Noise Are you crazy? Source + Noise -Source + Noise 4-Empirical low-noise images; Synthetic low-noise images 5-Models are transferable Share on Infinite Toolbox Let's Connect 🡆 Twitter-   / orsdragonfly3d   🡆 LinkedIn:   / object-research-systems   🡆 ORS Website: ORS Website: https://www.theobjects.com 🡆 Sales-related inquiries: sales@theobjects.com 🡆 Support: support@theobjects.com About ORS Object Research Systems (ORS) develops deep learning powered 3D visualDenoising with Deep Learningization and image analysis software. Dragonfly and Dragonfly Cloud, ORS’ flagship products, provide innovators from leading universities or industries, an advanced machine learning and neural networks based segmentation engine. Dragonfly's quantification tools then provide powerful options for counting, measuring, and characterizing image features, such as pores, fibers, grains, and much more. Its user-friendly experience translates its powerful and accurate quantitative findings in high-impact visuals. The ability to build fully automated workflows also enables reproducible results. #Dragonfly #deeplearning #TensorFlow #Keras #Denoising #SuperResolution #microscopy #imagesegmentation #microCT #FIBSEM #scientificimaging #electronmicroscopy #imageprocessing