Three-dimensional deeply generated holography [Invited]
Ryoichi Horisaki, Yohei Nishizaki, Katsuhisa Kitaguchi, Mamoru Saito, Jun Tanida
Abstract
In this paper, we present a noniterative method for 3D computer-generated holography based on deep learning. A convolutional neural network is adapted for directly generating a hologram to reproduce a 3D intensity pattern in a given class. We experimentally demonstrated the proposed method with optical reproductions of multiple layers based on phase-only Fourier holography. Our method is noniterative, but it achieves a reproduction quality comparable with that of iterative methods for a given class.
Topics & Concepts
HolographyOpticsComputer scienceComputer-generated holographyDigital holographyFourier transformHolographic displayConvolutional neural networkSpatial frequencyClass (philosophy)Phase retrievalArtificial intelligencePhysicsQuantum mechanicsAdvanced Optical Imaging TechnologiesDigital Holography and MicroscopyAdvanced Vision and Imaging