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<!DOCTYPE html>
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<head>
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<title>AutoColor: Learned Light Power Control for Multi-Color Holograms</title>
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<h1 class="title is-1 publication-title">AutoColor: Learned Light Power Control for Multi-Color Holograms</h1>
<img width ="12%" src="https://complightlab.com/people/yicheng_zhan.png">
<img width ="12%" src="https://complightlab.com/people/koray_kavakli.png">
<img width ="12%" src="https://github.com/complight/complight.github.io/blob/master/docs/people/hakan_urey.png?raw=true">
<img width ="12%" src="https://raw.githubusercontent.com/complight/autocolor_/main/qi_sun.png">
<img width ="12%" src="https://user-images.githubusercontent.com/46696280/214999478-ea45353d-3704-4290-8e90-10c747887253.png">
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://github.com/AlberTgarY">Yicheng Zhan</a><sup>1,*</sup> |</span>
<span class="author-block">
<a href="https://github.com/KorayKavakli">Koray Kavakli</a><sup>1,2</sup> |</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/hakan-%C3%BCrey-1386a9a/?originalSubdomain=tr">Hakan Ürey</a><sup>2</sup> |
</span>
<span class="author-block">
<a href="https://engineering.nyu.edu/faculty/qi-sun">Qi Sun</a><sup>3</sup> |
</span>
<span class="author-block">
<a href="https://kaanaksit.com/">Kaan Akşit</a><sup>1,*</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University College London,</span>
<span class="author-block"><sup>2</sup>Koc University,</span>
<span class="author-block"><sup>3</sup>New York University</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">*Corresponding Authors</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><b><a href="https://spie.org/conferences-and-exhibitions/ar-vr-mr/attend">SPIE AR|VR|MR 2024</a></b></span>
</div>
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<a href="https://arxiv.org/abs/2305.01611"
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<span>Paper</span>
</a>
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<span>Code</span>
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<span>Data</span>
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<img width ="5%" src="https://user-images.githubusercontent.com/46696280/215226725-5fad80c7-c795-4054-8279-c9789ec59795.png">
<a href="https://complightlab.com/">UCL Computational Light Laboratory</a>
</span>
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</section>
<section class="section">
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<h2 class="title is-3">Abstract</h2>
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<p>
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.
</p>
</div>
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<h2 class="title is-3">Methodology</h2>
<h3 class="title is-4">Generative model and large language model</h3>
<div class="content has-text-justified">
<p>
By using the Large Language Model (LLM) called GPT-4 via its online interface ChatGPT,
we generate a series of detailed prompts with different keywords. We used a set of prompts
to guide the generation process and developed a large dataset of images locally using
text-to-image generation models and a super-resolution network. We estimated the depth
information for the generated images using a monocular depth information network and optimized
multi-color holograms and their light source powers using a Multi-color optimization pipeline.
The entire <a href="https://github.com/complight/hologram_dataset/tree/main/diffusion">dataset</a>
generation process was computationally intensive and took about ten days using multiple GPUs.
</p>
</div>
<h3 class="title is-4">Multi-color holograms</h3>
<p>We used <a href="https://complightlab.com/publications/multi_color">Multi-color</a> to generate multi-color holography
and uses a CNN to estimate light source power from input images. The estimated light source powers
are then used to optimize multi-color holograms using an existing pipeline. We validate the approach
experimentally and show that AutoColor can significantly improve computational efficiency by reducing
the optimization steps. We also developed a holographic display hardware prototype for quantitative evaluation
and summarize our quantitative evaluation in below.
</p>
<br \>
<div class="has-text-centered">
<img width="700" src="https://raw.githubusercontent.com/complight/autocolor_/main/schematic.png"
class="interpolation-image"
alt="schematic.png"/>
<p class="is-bold">AutoColor Overview</p>
</div>
<br/>
<!--/ Interpolating. -->
<!-- Re-rendering. -->
<h2 class="title is-3">Result</h2>
<div class="content has-text-justified">
<p>
We demonstrate that this approach requires significantly fewer steps and can lead to holograms with wide dynamic
ranges at interactive rates. The approach is validated through experimental analysis, and we suggest that AutoColor
represents a promising research frontier for future hologram development.
</p>
</div>
<div class="has-text-centered">
<img src="https://github.com/complight/autocolor_/blob/main/result.png?raw=true"
class="interpolation-image"
alt="result.png"/>
<p class="is-bold">Quantitative Evaluation</p>
</div>
<!--/ Re-rendering. -->
</div>
</div>
<!--/ Animation. -->
<!-- Concurrent Work. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-4">Related Links</h2>
<div class="content has-text-justified">
<p>
<a href="https://complightlab.com/publications/holobeam/">- HoloBeam: Paper-Thin Near-Eye Displays</a>
</p>
<p>
<a href="https://complightlab.com/publications/multi_color/">- Multi-color Holograms Improve Brightness in Holographic Displays</a>
</p>
<p>
<a href="https://complightlab.com/publications/realistic_defocus_cgh/">- Realistic Defocus for Multiplane Computer-Generated Holography</a>
</p>
<p>
<a href="https://complightlab.com/teaching/siggraph2022_optimizing_vision_and_visuals/">- Optimizing Vision and Visuals: Lectures on Cameras, Displays, and Perception</a>
</p>
<p>
<a href="https://github.com/kunguz/odak">- Odak</a>
</p>
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-4">Contact Us</h2>
<div class="content has-text-justified">
Please reach us through <a href = "mailto:k.aksit@ucl.ac.uk">email</a> to provide your feedback and comments.
</div>
</div>
</div>
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-4">Acknowledgements</h2>
<div class="content has-text-justified">
<p>
<i>Kaan Akşit</i>, <i>Koray Kavaklı</i> and <i>Yicheng Zhan</i> are supported by the Royal Society’s RGS/R2/212229
Research Grants 2021 Round 2 and Meta Reality Labs inclusive rendering initiative 2022.
</p>
<p>
<i>Hakan Urey</i>
is supported by the European Innovation Council’s HORIZON-EIC-2021-TRANSITION-CHALLENGES
program Grant Number 101057672 and Tübitak’s 2247-A National Lead Researchers Program, Project Number 120C145.
</p>
<p>
<i>Qi Sun</i> is partially supported by the National Science Foundation (NSF) research grants #2225861 and #2232817.
</p>
</div>
</div>
</div>
<div class="container is-max-desktop content">
<h2 class="title is-4">BibTeX</h2>
<pre><code>@article{zhan2024autocolor,
title={AutoColor: Learned Light Power Control for Multi-Color Holograms},
author={Zhan, Yicheng and Kavakl{\i}, Koray and Urey, Hakan and Sun, Qi and Akşit, Kaan},
journal={SPIE},
year={2024},
month=Jan,
arxiv={2305.01611},
html={https://complightlab.com/autocolor_},
code={https://github.com/complight/autocolor},
doi={https://doi.org/10.48550/arXiv.2305.01611}
} </code>
</pre>
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