Optimization of phase-only holograms calculated with scaled diffraction calculation through deep neural networks

TL;DR

This paper introduces a deep learning approach to optimize phase-only holograms generated with scaled diffraction, enabling faster and higher-quality 3D holographic image reconstruction compared to traditional iterative methods.

Contribution

It presents a novel deep neural network method that enhances phase-only hologram quality and speed by integrating scaled diffraction and the random phase-free approach.

Findings

Outperforms Gerchberg-Saxton algorithm in quality and speed

Handles zoomable reconstructed images larger than the hologram

Provides real-time hologram optimization

Abstract

Computer-generated holograms (CGHs) are used in holographic three-dimensional (3D) displays and holographic projections. The quality of the reconstructed images using phase-only CGHs is degraded because the amplitude of the reconstructed image is difficult to control. Iterative optimization methods such as the Gerchberg-Saxton (GS) algorithm are one option for improving image quality. They optimize CGHs in an iterative fashion to obtain a higher image quality. However, such iterative computation is time consuming, and the improvement in image quality is often stagnant. Recently, deep learning-based hologram computation has been proposed. Deep neural networks directly infer CGHs from input image data. However, it is limited to reconstructing images that are the same size as the hologram. In this study, we use deep learning to optimize phase-only CGHs generated using scaled diffraction computations and the random phase-free method. By combining the random phase-free method with the scaled diffraction computation, it is possible to handle a zoomable reconstructed image larger than the hologram. In comparison to the GS algorithm, the proposed method optimizes both high quality and speed.