Fourier Optics and Deep Learning Methods for Fast 3D Reconstruction in Digital Holography

TL;DR

This paper introduces a fast pipeline combining Fourier optics and deep learning for 3D hologram reconstruction, improving quality and speed in digital holography applications.

Contribution

It presents a novel pipeline integrating Fourier optics optimization algorithms with deep learning, enhancing 3D hologram synthesis from volumetric data.

Findings

Improved reconstruction quality using median filtering.

Deep learning method HoloNet outperforms traditional algorithms.

Optimization algorithms achieve lower error metrics.

Abstract

Computer-generated holography (CGH) is a promising method that modulates user-defined waveforms with digital holograms. An efficient and fast pipeline framework is proposed to synthesize CGH using initial point cloud and MRI data. This input data is reconstructed into volumetric objects that are then input into non-convex Fourier optics optimization algorithms for phase-only hologram (POH) and complex-hologram (CH) generation using alternating projection, SGD, and quasi-Netwton methods. Comparison of reconstruction performance of these algorithms as measured by MSE, RMSE, and PSNR is analyzed as well as to HoloNet deep learning CGH. Performance metrics are shown to be improved by using 2D median filtering to remove artifacts and speckled noise during optimization.