Conditional neural holography: a distance-adaptive CGH generator

TL;DR

This paper introduces a CNN-based hologram generator capable of producing computer-generated holograms at specified propagation distances, enhancing flexibility while maintaining high speed and accuracy.

Contribution

The proposed distance-adaptive CGH generator uniquely allows specifying the propagation distance, unlike previous CNN methods with fixed distances.

Findings

Comparable performance to fixed-distance CNN methods

Achieves practical speed and accuracy for hologram generation

Successfully generates holograms at various distances

Abstract

A convolutional neural network (CNN) is useful for overcoming the trade-off between generation speed and accuracy in the process of synthesizing computer-generated holograms (CGHs). However, methods using a CNN have limited applicability as they cannot specify the propagation distance when synthesizing a hologram. We developed a distance-adaptive CGH generator that can generate CGHs by specifying the target image and propagation distance, which comprises a zone plate encoder stage and an augmented HoloNet stage. Our model is comparable to that of prior CNN methods, with a fixed distance, in terms of performance and achieves the generation accuracy and speed necessary for practical use.