Comprehensive deep learning model for optical holography

TL;DR

This paper introduces DOENet, a deep learning model that efficiently generates and reconstructs optical holograms across multiple colors and observation planes, outperforming traditional iterative methods in speed and versatility.

Contribution

The paper presents a novel deep learning framework for fast, multitasking hologram generation and object reconstruction without iterative algorithms or phase information.

Findings

DOENet accurately reconstructs objects from intensity holograms.

The model multiplexes color holographic images at multiple observation planes.

Reconstruction quality shows excellent agreement with ground-truth data.

Abstract

Holography is a vital tool used in various applications from microscopy, solar energy, imaging, display to information encryption. Generation of a holographic image and reconstruction of object/hologram information from a holographic image using the current algorithms are time-consuming processes. Versatile, fast in the meantime accurate methodologies are required to compute holograms performing color imaging at multiple observation planes and reconstruct object/sample information from a holographic image for widely accommodating optical holograms. Here, we focus on design of optical holograms for generation of holographic images at multiple observation planes and colors via a deep learning model the DOENet. The DOENet produces optical holograms which show multitasking performance as multiplexing color holographic image planes by tuning holographic structures. Furthermore, our deep learning model retrieves an object/hologram information from an intensity holographic image without requiring phase and amplitude information from the intensity image. We show that reconstructed objects/holograms show excellent agreement with the ground-truths. The DOENet does not need iteratively reconstruction of object/hologram information while conventional object/hologram recovery methods rely on multiple holographic images at various observation planes along with the iterative algorithms. We believe that the DOENet based object/hologram reconstruction and generation of holographic images may speed up wide-area implementation of optical holography.