# Learned holographic light transport

arXiv: https://arxiv.org/abs/2108.08253 · HTML: https://mlsanity.com/paper/2108.08253/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 1 Aug 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report](https://mlsanity.com/paper/1810.01641/) (2018)
- [Optical Gaze Tracking with Spatially-Sparse Single-Pixel Detectors](https://mlsanity.com/paper/2009.06875/) (2020)
- [Telelife: The Future of Remote Living](https://mlsanity.com/paper/2107.02965/) (2021)

## Built on by (most cited first, 6 of 6)

- [Time-multiplexed Neural Holography: A flexible framework for holographic near-eye displays with fast heavily-quantized spatial light modulators](https://mlsanity.com/paper/2205.02367/) (2022)
- [Unrolled Primal-Dual Networks for Lensless Cameras](https://mlsanity.com/paper/2203.04353/) (2022)
- [AutoColor: Learned Light Power Control for Multi-Color Holograms](https://mlsanity.com/paper/2305.01611/) (2023)
- [Configurable Holography: Towards Display and Scene Adaptation](https://mlsanity.com/paper/2405.01558/) (2024)
- [P-Hologen: An End-to-End Generative Framework for Phase-Only Holograms](https://mlsanity.com/paper/2404.01330/) (2024)
- [Large-Area Fabrication-Aware Computational Diffractive Optics](https://mlsanity.com/paper/2505.22313/) (2025)

## Abstract (arXiv)

Computer-Generated Holography (CGH) algorithms often fall short in matching simulations with results from a physical holographic display. Our work addresses this mismatch by learning the holographic light transport in holographic displays. Using a camera and a holographic display, we capture the image reconstructions of optimized holograms that rely on ideal simulations to generate a dataset. Inspired by the ideal simulations, we learn a complex-valued convolution kernel that can propagate given holograms to captured photographs in our dataset. Our method can dramatically improve simulation accuracy and image quality in holographic displays while paving the way for physically informed learning approaches.