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.

We introduce a computer-generated holography algorithm based on deep learning with unsupervised training. Our method generates high fidelity holograms in a few milliseconds and outperforms alternate methods that require many iterations and longer computation.