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

Justin London
8/8/2025
cs.CV

No confident code implementation found for this paper. 3 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 3 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A Large-Depth-Range Layer-Based Hologram Dataset for Machine Learning-Based 3D Computer-Generated Holography

87% match2025

Towards Robust and Generalizable Gerchberg Saxton based Physics Inspired Neural Networks for Computer Generated Holography: A Sensitivity Analysis Framework

84% match2025

JPEG-Inspired Cloud-Edge Holography

83% match2025

Enhancing Digital Hologram Reconstruction Using Reverse-Attention Loss for Untrained Physics-Driven Deep Learning Models with Uncertain Distance

82% match2024

OAH-Net: A Deep Neural Network for Hologram Reconstruction of Off-axis Digital Holographic Microscope

81% match2024

P-Hologen: An End-to-End Generative Framework for Phase-Only Holograms

80% match1 repo12024

Cite this paper

@article{london2025fourier,
  title  = {Fourier Optics and Deep Learning Methods for Fast 3D Reconstruction in Digital Holography},
  author = {Justin London},
  year   = {2025},
  eprint = {2508.06703},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2508.06703v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2508.06703.svg)](https://codeofpaper.com/paper/2508.06703)

Discussion