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Learned holographic light transport: invited

Koray Kavaklı, Hakan Urey, and Kaan Akşit

Open Access Open Access

Abstract

Computer-generated holography 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.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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Supplementary Material (1)

NameDescription
Dataset 1       Phase only holograms and their captures.

Data Availability

The generated dataset of this work is available in Dataset 1, Ref. [35]. The code base discussed in Sections 2 and 4 is available in Refs. [30,36].

35. K. Kavaklı, H. Urey, and K. Akşit, “Phase-only holograms and captured photographs,” University College London, v. 1, 2021https://doi.org/10.5522/04/15087867.v1.

30. K. Akşit, A. S. Karadeniz, P. Chakravarthula, W. Yujie, K. Kavaklı, Y. Itoh, and D. R. Walton, “Odak 0.1.9,” Zenodo, 2021, https://doi.org/10.5281/zenodo.5526684.

36. K. Kavaklı, H. Urey, and K. Akşit, “Realistic holography,” v. 0.1, GitHub, 2021, https://github.com/complight/realistic_holography.

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Figures (5)
Fig. 1.
Fig. 1. Schematic diagram of our proof-of-concept holographic display prototype used in our experimental setup.

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Fig. 2.
Fig. 2. Mismatch between simulated and experimental results when using ideal holographic light transport. For a given (a) phase-only hologram, a simulated result can provide (b) a perfect image reconstruction. (c) For the same hologram, a real holographic display fails to achieve the image reconstructions we show in (Dataset 1, Ref. [35]).

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Fig. 3.
Fig. 3. Phase and amplitude comparison between complex kernels used in (a) an ideal holographic light transport and (b) a learned holographic light transport.

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Fig. 4.
Fig. 4. Visual comparison between (a) an ideal holographic light transport and (b) a learned holographic light transport in reconstructing images. Both of the photographs are captured with optimized holograms using corresponding holographic light transport models and our proof-of-concept prototype. Note that target image in both cases are not used in our training set (DIV2K [34]).

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Fig. 5.
Fig. 5. (a) Learned simulation versus (b) real photograph. The ideal light transport based hologram optimization estimates unrealistic results in simulation. (c) On the other hand, for a given target image, simulations based on learned holographic light transport closely resemble the experimental results.

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Equations (7)

Equations on this page are rendered with MathJax. Learn more.

u(x,y)=1jλ∬u0(x,y)ejkrrcos(θ)dxdy,
u0(x,y)=A(x,y)ejϕ(x,y),
u(x,y)=u0(x,y)∗h(x,y)=F−1(F(u0(x,y))F(h(x,y)))=U0(fx,fy)H(fx,fy),
h(x,y)=ejkzjλzejk2z(x2+y2),
L=(u(x,y)−t(x,y))2.
u0′(x,y)={e−j(ϕ(x,y)+π)forx=odde−jϕ(x,y)forx=even,
mλ=Δasin⁡(θ),
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