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Holographic displays can generate light fields by dynamically modulating the wavefront of a coherent beam of light using a spatial light modulator, promising rich virtual and augmented reality applications. However, the limited spatial resolution of existing dynamic spatial light modulators imposes a tight bound on the diffraction angle. As a result, todays holographic displays possess low {e}tendue, which is the product of the display area and the maximum solid angle of diffracted light. The low {e}tendue forces a sacrifice of either the field of view (FOV) or the display size. In this work, we lift this limitation by presenting neural {e}tendue expanders. This new breed of optical elements, which is learned from a natural image dataset, enables higher diffraction angles for ultra-wide FOV while maintaining both a compact form factor and the fidelity of displayed contents to human viewers. With neural {e}tendue expanders, we achieve 64$times$ {e}tendue expansion of natural images with reconstruction quality (measured in PSNR) over 29dB on simulated retinal-resolution images. As a result, the proposed approach with expansion factor 64$times$ enables high-fidelity ultra-wide-angle holographic projection of natural images using an 8K-pixel SLM, resulting in a 18.5 mm eyebox size and 2.18 steradians FOV, covering 85% of the human stereo FOV.
High resolution Digital Elevation Models(DEMs) are an important requirement for many applications like modelling water flow, landslides, avalanches etc. Yet publicly available DEMs have low resolution for most parts of the world. Despite tremendous s
We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator architecture
This paper proposes a particle volume reconstruction directly from an in-line hologram using a deep neural network. Digital holographic volume reconstruction conventionally uses multiple diffraction calculations to obtain sectional reconstructed imag
Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important step in holographic image reconstruction. Here we demonstrate a convolut
Purpose: To improve reconstruction fidelity of fine structures and textures in deep learning (DL) based reconstructions. Methods: A novel patch-based Unsupervised Feature Loss (UFLoss) is proposed and incorporated into the training of DL-based reco