Learned holographic light transport
This addresses a specific problem in computer-generated holography for display technology, with incremental improvements in simulation fidelity.
The paper tackles the mismatch between simulated and physical holographic displays by learning the holographic light transport using a dataset from a camera and display, resulting in dramatically improved simulation accuracy and image quality.
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.