Dexterous holographic trapping of dark-seeking particles with Zernike holograms

Deep learning denoising diffusion probabilistic model applied to holographic data synthesis

In this Letter, we demonstrate for the first time, to our knowledge, a holographic data synthesis based on a deep learning probabilistic diffusion model (DDPM). Several different datasets of color images corresponding to different types of objects are converted to complex-valued holographic data through backpropagation. Then, we train a DDPM using the resulting holographic datasets. The diffusion model is composed of a noise scheduler, which gradually adds Gaussian noise to each hologram in the dataset, and a U-Net convolutional neural network that is trained to reverse this process. Once the U-Net is trained, any number of holograms with similar features as those of the datasets can be generated just by inputting a Gaussian random noise to the model. We demonstrate the synthesis of holograms containing color images of 2D characters, vehicles, and 3D scenes with different characters at different propagation distances.

Color multilayer holographic near-eye augmented reality display

This study demonstrates a full-color near-eye holographic display capable of superimposing color virtual scenes with 2D, 3D, and multiple objects with extended depth upon a real scene, which also has the ability to present different 3D information depending on the focus of the user's eyes using a single computer-generated hologram per color channel. Our setup makes use of a hologram generation method based on two-step propagation and the singular value decomposition of the Fresnel transform impulse response function to efficiently generate the holograms of the target scene. Then, we test our proposal by implementing a holographic display that makes use of a phase-only spatial light modulator and time-division multiplexing for color reproduction. We demonstrate the superior quality and computation speed of this approach compared with other hologram generation techniques with both numerical and experimental results.