A method for computer-generated holography comprises providing a target holographic image illumination distribution AT to a trained convolutional neural network (CNN). Using the CNN, a predicted complex optical field P0 is generated in an image plane downstream from a spatial light modulator (SLM). The complex optical field is back-propagated (using an inverse Fourier Transform iFT) to a SLM plane to produce a predicted SLM phase mask φSLM. The SLM is configured according to the predicted SLM phase mask and incident coherent light is passed therethrough in order to produce a holographic image at the image plane. The method avoids the need for iterative methods for computer-generated hologram generation which enables faster hologram generation. An method for training the associated convolutional neural network (CNN) is also disclosed in which the predicted SLM phase mask is evaluated using a cost function based upon a comparison between the reconstructed target image distribution AR and the original target image distribution AT. Cost function values are fed back to the CNN which adjusts its parameters to achieve desired cost function values.
High-speed computer generated holography using convolutional neural networks