Office of Commercialization and Economic Development
Office of Technology Commercialization

DeepCGH: 3D Computer-Generated Holography using Deep Learning

Technology #20-0056

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Researchers
Nicolas Christian Richard Pegard
Managed By
Chance Rainwater
Commercialization Manager 919.445.9371
Patent Protection
Provisional Patent Application Filed

Existing algorithms for computer-generated holography (CGH) rely on iterative approaches that require selecting between computation speed and hologram accuracy.  This built-in tradeoff is increasingly incompatible with emerging CGH applications in optogenetics and virtual reality that demand production of high-resolution holograms on short time scales.  The present UNC invention is a CGH algorithm based on a non-iterative, unsupervised machine learning model that produces large holograms (up to 11 megavoxels) several orders of magnitude faster than existing techniques and does so with greater accuracy.  The unsupervised nature of the model further allows for its tailoring to specific applications through judicious selection of training datasets and loss functions.  This DeepCGH approach has been validated in experimental applications. 

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