Learned holographic light transport: invited

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.

Realistic Defocus Blur for Multiplane Computer-Generated Holography

“…There is six target image plane in this case. In the future, we also plan on exploring learned models [5] for improving the visual quality further and mitigating imperfections in hardware setup.…”

Deep-Learning Computational Holography: A Review

“…2020) are complex processing. The study Kavaklı et al (2022) obtained an optimized point spread function for diffraction calculation from the error between numerically reproduced images from holograms calculated from the ideal diffraction calculation and the actual reproduced images captured by a camera. It is worth noting that the optimized point spread function has an asymmetric distribution different from the point spread function in the ideal case.…”

Time-multiplexed Neural Holography: A flexible framework for holographic near-eye displays with fast heavily-quantized spatial light modulators

“…For example, surrogate gradient methods that use a camera in the loop (CITL) for hologram optimization can significantly improve image quality [Choi et al 2021b;. Alternatively, differentiable wave propagation models can be learned to calibrate for the gap between simulated models and physical optics [Chakravarthula et al 2020;Choi et al 2021a;Kavakli et al 2022;. Moreover, neural networks can be trained to enable real-time CGH algorithms [Horisaki et al 2021[Horisaki et al , 2018.…”

“…The target plane is a distance 𝑧 from the SLM. In addition, 𝑎 src and 𝜙 src are learned to account for content-independent spatial variations in amplitude and phase of the incident source field at the SLM plane while 𝑎 F and 𝜙 F are added to the ASM propagation to learn spatial variations in amplitude and phase in the Fourier plane similarly to the learned complex convolutional kernel presented by Kavakli et al [2022]. Similar to Choi et al, we capture a training and a test set comprised of a large number of SLM phase patterns and corresponding amplitude images recorded at a set of distances { 𝑗 } , 𝑗 = 1 .…”