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We propose a holographic projection system that achieves high image quality, brightness, and light efficiency. Using a novel, to the best of our knowledge, light-efficiency loss function, we are able to concentrate more light on the projection region and improve display brightness compared with conventional projectors. Leveraging emerging artificial intelligence-driven computer-generated holography and camera-in-the-loop calibration techniques, we learn a holographic wave propagation model using experimentally captured holographic images and demonstrate state-of-the-art light reallocation performance with high image quality.

This Feature Issue includes 2 reviews and 34 research articles that highlight recent works in the field of Computational Optical Sensing and Imaging. Many of the works were presented at the 2021 OSA Topical Meeting on Computational Optical Sensing and Imaging, held virtually from July 19 to July 23, 2021. Articles in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue.

This feature issue includes two reviews and 34 research papers that highlight recent works in the field of computational optical sensing and imaging. Many of the works were presented at the 2021 Optica (formerly OSA) Topical Meeting on Computational Optical Sensing and Imaging, held virtually from 19 July to 23 July 2021. Papers in the feature issue cover a broad scope of computational imaging topics, such as microscopy, 3D imaging, phase retrieval, non-line-of-sight imaging, imaging through scattering media, ghost imaging, compressed sensing, and applications with new types of sensors. Deep learning approaches for computational imaging and sensing are also a focus of this feature issue.

This Feature Issue of Optics Express is organized in conjunction with the 2021 Optica (OSA) conference on 3D Image Acquisition and Display: Technology, Perception and Applications which was held virtually from 19 to 23, July 2021 as part of the Imaging and Sensing Congress 2021. This Feature Issue presents 29 articles which cover the topics and scope of the 2021 3D conference. This Introduction provides a summary of these articles.

Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical-electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges.

Computer-generated holography suffers from high diffraction orders (HDOs) created from pixelated spatial light modulators, which must be optically filtered using bulky optics. Here, we develop an algorithmic framework for optimizing HDOs without optical filtering to enable compact holographic displays. We devise a wave propagation model of HDOs and use it to optimize phase patterns, which allows HDOs to contribute to forming the image instead of creating artifacts. The proposed method significantly outperforms previous algorithms in an unfiltered holographic display prototype.

Holographic pancake optics have been designed and fabricated in eyewear display optics literature dating back to 1985, however, a see-through pancake optic solution has not been demonstrated to date. The key contribution here is the first full-color volume holographic pancake optic in an optical see-through configuration for applications in mobile augmented reality. Specifically, the full-color volume holographic pancake is combined with a flat lightguide in order to achieve the optical see-through property. The fabricated hardware optics has a measured field of view of 29 degrees (horizontal) by 12 degrees (vertical) and a measured large eyebox that allows a ±10 mm horizontal motion and ∼±3 mm vertical motion for a 4 mm diameter pupil. The measured modulation transfer function (average orientation) is 10% contrast at 10 lp/deg. Three holograms were characterized with respect to their diffraction efficiency, angular bandwidth, focal length, haze, and thickness parameters. The phase function in the reflection mode hologram implements a spherical mirror that has a relatively simple recording geometry.

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography.

Wearable near-eye displays for virtual and augmented reality (VR/AR) have seen enormous growth in recent years. While researchers are exploiting a plethora of techniques to create life-like three-dimensional (3D) objects, there is a lack of awareness of the role of human perception in guiding the hardware development. An ultimate VR/AR headset must integrate the display, sensors, and processors in a compact enclosure that people can comfortably wear for a long time while allowing a superior immersion experience and user-friendly human-computer interaction. Compared with other 3D displays, the holographic display has unique advantages in providing natural depth cues and correcting eye aberrations. Therefore, it holds great promise to be the enabling technology for next-generation VR/AR devices. In this review, we survey the recent progress in holographic near-eye displays from the human-centric perspective., Competing Interests: Disclosures. The authors declare no conflicts of interest.

This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field.

Single-photon light detection and ranging (LiDAR) techniques use emerging single-photon detectors (SPADs) to push 3D imaging capabilities to unprecedented ranges. However, it remains challenging to robustly estimate scene depth from the noisy and otherwise corrupted measurements recorded by a SPAD. Here, we propose a deep sensor fusion strategy that combines corrupted SPAD data and a conventional 2D image to estimate the depth of a scene. Our primary contribution is a neural network architecture-SPADnet-that uses a monocular depth estimation algorithm together with a SPAD denoising and sensor fusion strategy. This architecture, together with several techniques in network training, achieves state-of-the-art results for RGB-SPAD fusion with simulated and captured data. Moreover, SPADnet is more computationally efficient than previous RGB-SPAD fusion networks.

We describe a panoramic camera using one monocentric lens and an array of light field (LF) sensors to capture overlapping contiguous regions of the spherical image surface. Refractive sub-field consolidators divide the light before the image surface and concentrate the sub-images onto the optically active areas of adjacent CMOS sensors. We show the design of a 160° × 24° field-of-view (FOV) LF camera, and experimental test of a three sensor F/2.5 96° × 24° and five sensor (25 MPixel) F/4 140° × 24° camera. We demonstrate computational field curvature correction, refocusing, resolution enhancement, and depth mapping of a laboratory scene. We also present a 155° full circular field camera design compatible with LF or direct 164 MPixel sensing of 13 spherical sub-images, fitting within a one inch diameter sphere.

An optimization algorithm for preparing display-ready holographic elements (hogels) to synthesize a light field is outlined, and proof of concept is experimentally demonstrated. This method allows for higher-rank factorization, which can be used for time-multiplexing multiple frames for improved image quality, using phase-only and fully complex modulation with a single spatial light modulator.

Phase spatial light modulators (SLMs) are widely used for generating multifocal three-dimensional (3D) illumination patterns, but these are limited to a field of view constrained by the pixel count or size of the SLM. Further, with two-photon SLM-based excitation, increasing the number of focal spots penalizes the total signal linearly--requiring more laser power than is available or can be tolerated by the sample. Here we analyze and demonstrate a method of using galvanometer mirrors to time-sequentially reposition multiple 3D holograms, both extending the field of view and increasing the total time-averaged two-photon signal. We apply our approach to 3D two-photon in vivo neuronal calcium imaging.

In fluorescence measurements, light is often absorbed and scattered by a sample both for excitation and emission, resulting in the measured spectra to be distorted. Conventional linear unmixing methods computationally separate overlapping spectra but do not account for these effects. We propose a new algorithm for fluorescence unmixing that accounts for the attenuation-related distortion effect on fluorescence spectra. Using a matrix representation, we derive forward measurement formation and a corresponding inverse method; the unmixing algorithm is based on nonnegative matrix factorization. We also demonstrate how this method can be extended to a higher-dimensional tensor form, which is useful for unmixing overlapping spectra observed under the attenuation effect in spectral imaging microscopy. We evaluate the proposed methods in simulation and experiments and show that it outperforms a conventional, linear unmixing method when absorption and scattering contributes to the measured signals, as in deep tissue imaging.

Compressive displays are an emerging technology exploring the co-design of new optical device configurations and compressive computation. Previously, research has shown how to improve the dynamic range of displays and facilitate high-quality light field or glasses-free 3D image synthesis. In this paper, we introduce a new multi-mode compressive display architecture that supports switching between 3D and high dynamic range (HDR) modes as well as a new super-resolution mode. The proposed hardware consists of readily-available components and is driven by a novel splitting algorithm that computes the pixel states from a target high-resolution image. In effect, the display pixels present a compressed representation of the target image that is perceived as a single, high resolution image.

This Letter presents a new snapshot approach to hyperspectral imaging via dual-optical coding and compressive computational reconstruction. We demonstrate that two high-speed spatial light modulators, located conjugate to the image and spectral plane, respectively, can code the hyperspectral datacube into a single sensor image such that the high-resolution signal can be recovered in postprocessing. We show various applications by designing different optical modulation functions, including programmable spatially varying color filtering, multiplexed hyperspectral imaging, and high-resolution compressive hyperspectral imaging.