DeepCGH: 3D computer-generated holography using deep learning
The goal of computer-generated holography (CGH) is to synthesize custom illumination patterns by modulating a coherent light beam. CGH algorithms typically rely on iterative optimization with a built-in trade-off between computation speed and hologram accuracy that limits performance in advanced app...
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Veröffentlicht in: | Optics express 2020-08, Vol.28 (18), p.26636-26650 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The goal of computer-generated holography (CGH) is to synthesize custom illumination patterns by modulating a coherent light beam. CGH algorithms typically rely on iterative optimization with a built-in trade-off between computation speed and hologram accuracy that limits performance in advanced applications such as optogenetic photostimulation. We introduce a non-iterative algorithm, DeepCGH, that relies on a convolutional neural network with unsupervised learning to compute accurate holograms with fixed computational complexity. Simulations show that our method generates holograms orders of magnitude faster and with up to 41% greater accuracy than alternate CGH techniques. Experiments in a holographic multiphoton microscope show that DeepCGH substantially enhances two-photon absorption and improves performance in photostimulation tasks without requiring additional laser power. |
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ISSN: | 1094-4087 1094-4087 |
DOI: | 10.1364/OE.399624 |