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DeepCGH: 3D computer-generated holography using deep learning
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DOI：10.1364/OE.399624.png)
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Abstract
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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. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
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