# AutoColor: Learned Light Power Control for Multi-Color Holograms

arXiv: https://arxiv.org/abs/2305.01611 · HTML: https://mlsanity.com/paper/2305.01611/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 2 May 2023.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Color-Perception-Guided Display Power Reduction for Virtual Reality](https://mlsanity.com/paper/2209.07610/) (2022)
- [Gaze-Contingent Retinal Speckle Suppression for Perceptually-Matched Foveated Holographic Displays](https://mlsanity.com/paper/2108.06192/) (2021)
- [Learned holographic light transport](https://mlsanity.com/paper/2108.08253/) (2021)
- [Multi-color Holograms Improve Brightness in Holographic Displays](https://mlsanity.com/paper/2301.09950/) (2023)
- [Metameric Varifocal Holography](https://mlsanity.com/paper/2110.01981/) (2021)

## Built on by (most cited first, 1 of 1)

- [Configurable Holography: Towards Display and Scene Adaptation](https://mlsanity.com/paper/2405.01558/) (2024)

## Abstract (arXiv)

Multi-color holograms rely on simultaneous illumination from multiple light sources. These multi-color holograms could utilize light sources better than conventional single-color holograms and can improve the dynamic range of holographic displays. In this letter, we introduce AutoColor , the first learned method for estimating the optimal light source powers required for illuminating multi-color holograms. For this purpose, we establish the first multi-color hologram dataset using synthetic images and their depth information. We generate these synthetic images using a trending pipeline combining generative, large language, and monocular depth estimation models. Finally, we train our learned model using our dataset and experimentally demonstrate that AutoColor significantly decreases the number of steps required to optimize multi-color holograms from > 1000 to 70 iteration steps without compromising image quality.