Focus Issue Introduction: 3D Image Acquisition and Display: Technology, Perception and Applications.

This Feature Issue of Optics Express is organized in conjunction with the 2022 Optica conference on 3D Image Acquisition and Display: Technology, Perception and Applications which was held in hybrid format from 11 to 15, July 2022 as part of the Imaging and Applied Optics Congress and Optical Sensors and Sensing Congress 2022 in Vancouver, Canada. This Feature Issue presents 31 articles which cover the topics and scope of the 2022 3D Image Acquisition and Display conference. This Introduction provides a summary of these published articles that appear in this Feature Issue.

Object localization and tracking in three dimensions by space-to-time encoding.

In this paper, we present a novel method for measuring the location and estimating the dynamics of fast-moving small objects in free space. The proposed 3D localization method is realized by a space-to-time optical transform and measurement of time-of-flight. We present the underlying physical and mathematical model of the method and provide an example based on a simple configuration. In the simplest mode, the method is implemented by two plane mirrors, a spherical light pulse illuminator, and a single fast response photodetector. The 3D spatial information is retrieved from the temporal measurements by solving an inverse problem that uses a sparse approximation of the scene. System simulation shows the ability to track fast small objects that are moving in space using only a single time-resolved detector.

Focus issue introduction: 3D image acquisition and display: technology, perception and applications.

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.

Roadmap on digital holography [Invited].

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.

Compressive imaging for defending deep neural networks from adversarial attacks.

Despite their outstanding performance, convolutional deep neural networks (DNNs) are vulnerable to small adversarial perturbations. In this Letter, we introduce a novel approach to thwart adversarial attacks. We propose to employ compressive sensing (CS) to defend DNNs from adversarial attacks, and at the same time to encode the image, thus preventing counterattacks. We present computer simulations and optical experimental results of object classification in adversarial images captured with a CS single pixel camera.

A Simplified Model for Optical Systems with Random Phase Screens.

A first-order optical system with arbitrary multiple masks placed at arbitrary positions is the basic scheme of various optical systems. Generally, masks in optical systems have a non-shift invariant (SI) effect; thus, the individual effect of each mask on the output cannot be entirely separated. The goal of this paper is to develop a technique where complete separation might be achieved in the common case of random phase screens (RPSs) as masks. RPSs are commonly used to model light propagation through the atmosphere or through biological tissues. We demonstrate the utility of the technique on an optical system with multiple RPSs that model random scattering media.

Roadmap on 3D integral imaging: sensing, processing, and display.

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.

DeepCubeNet: reconstruction of spectrally compressive sensed hyperspectral images with deep neural networks.

Several hyperspectral (HS) systems based on compressive sensing (CS) theory have been presented to capture HS images with high accuracy and with a lower number of measurements than needed by conventional systems. However, the reconstruction of HS compressed measurements is time-consuming and commonly involves hyperparameter tuning per each scenario. In this paper, we introduce a Convolutional Neural Network (CNN) designed for the reconstruction of HS cubes captured with CS imagers based on spectral modulation. Our Deep Neural Network (DNN), dubbed DeepCubeNet, provides significant reduction in the reconstruction time compared to classical iterative methods. The performance of DeepCubeNet is investigated on simulated data, and we demonstrate for the first time, to the best of our knowledge, real reconstruction of CS HS measurements using DNN. We demonstrate significantly enhanced reconstruction accuracy compared to iterative CS reconstruction, as well as improvement in reconstruction time by many orders of magnitude.

Compressive ultraspectral imaging using multiscale structured illumination.

We present a novel compressive spectral imaging technique that attains spatially resolved ultraspectral resolution. The technique employs a multiscale sampling technique based on the Hadamard basis for the single pixel hyperspectral imager. The proposed multiscale sampling method offers high-quality images at a low compression ratio while also facilitating a preview image at a lower resolution by using the fast Hadamard transform.

Fast Method for Liquid Crystal Cell Spatial Variations Estimation Based on Modeling the Spectral Transmission.

Liquid crystal phase retarders are utilized by photonic devices and imaging systems for various applications, such as tunable filtering, light modulation, polarimetric imaging, remote sensing and quality inspection. Due to technical difficulties in the manufacturing process, these phase retarders may suffer from spatial non-uniformities, which degrade the performance of the systems. These non-uniformities can be characterized by measuring the spectral transmission at each voltage and each point on the liquid crystal cell, which is time consuming. In this work, we present a new fast and simple method for measuring and computationally estimating the spatial variations of a liquid crystal phase retarder with planar alignment. The method is based on measuring the spectral transmission of the phase retarder at several spatial locations and estimating it at others. The experimental results show that the method provides an accurate spatial description of the phase retarder and can be employed for calibrating relevant systems.

Modeling optical memory effects with phase screens.

During the last decade, optical memory effects have been explored extensively for various applications. In this letter we propose phase screen models to facilitate the analysis and the simulation of wave propagation through optical media that exhibits memory effects. We show that the classical optical memory effect, which implies tilt wave correlations of the input and the scattered fields, can be readily modeled by a single random phase screen. For the recently discovered generalized optical memory effect, which implies the existence of shift wave correlations in addition to the tilt correlation, we propose an appropriate generalized random phase screen model.

Multi-aperture snapshot compressive hyperspectral camera.

In this Letter, we present a new snapshot hyperspectral (HS) camera based on a multi-aperture design. The technique uses an array of modified Fabry-Perot resonators together with a lens array in order to acquire an array of spectrally multiplexed modulated sub-images. Then the original HS image is reconstructed using a compressive sensing reconstruction algorithm. The HS camera has high optical throughput and enables acquisition of almost gigapixel HS datacubes with hundreds of spectral bands. Using our camera, we demonstrate optical compression of approximately 37:1.

Compressive sensing resonator spectroscopy.

We present a new fast compressive spectroscopic technique based on the resonance spectrometric mechanism. This technique uses an appropriately designed Fabry-Perot resonator and a photo-sensor in order to acquire different multiplexed spectral modulations, from which the original signal is reconstructed using a compressive sensing reconstruction algorithm. We present experimental results that demonstrate the acquisition of hundreds of spectral bands with a compression ratio of about 1:13.

Evaluation of the influence of arbitrary masks on the output field of optical systems using ABCD matrices.

We propose a method to express the complex field amplitude (CFA) at the output of any optical system with arbitrary masks. The method provides a general equation for any first-order optical system with given parameters and arbitrary masks. The method is based on the ABCD matrices of the system components, ignoring the masks. The obtained output CFA is formulated in a way that helps recognize the influence of each mask on the output CFA by projecting its influence on the output with the aid of ABCD matrices. The method offers a more transparent approach for efficient organization and minimization of the calculation steps.

Along-track scanning using a liquid crystal compressive hyperspectral imager.

In various applications, such as remote sensing and quality inspection, hyperspectral (HS) imaging is performed by spatially scanning an object. In this work, we present a new compressive hyperspectral imaging method that performs along-track scanning. The method relies on the compressive sensing miniature ultra-spectral imaging (CS-MUSI) system, which uses a single liquid crystal (LC) cell for spectral encoding and provides a more efficient way of HS data acquisition, compared to classical spatial scanning based systems. The experimental results show that a compression ratio of about 1:10 can be reached. Owing to the inherent compression, the captured data is preprepared for efficient storage and transmission.

Compressive 4D spectro-volumetric imaging.

In this Letter, we present a method for hyperspectral imaging of three-dimensional objects. A compressive sensing approach is utilized to remedy the acquisition effort required to capture the large amount of data. The spectral dimension is compressively sensed by means of a liquid crystal-based encoder, and the volumetric data are captured using a synthetic aperture integral imaging setup. We demonstrate reconstruction of spectro-volumetric tesseracts with hundreds of spectral bands at different depths without compromise of spatial resolution.

Sparse synthetic aperture with Fresnel elements (S-SAFE) using digital incoherent holograms.

Creating a large-scale synthetic aperture makes it possible to break the resolution boundaries dictated by the wave nature of light of common optical systems. However, their implementation is challenging, since the generation of a large size continuous mosaic synthetic aperture composed of many patterns is complicated in terms of both phase matching and time-multiplexing duration. In this study we present an advanced configuration for an incoherent holographic imaging system with super resolution qualities that creates a partial synthetic aperture. The new system, termed sparse synthetic aperture with Fresnel elements (S-SAFE), enables significantly decreasing the number of the recorded elements, and it is free from positional constrains on their location. Additionally, in order to obtain the best image quality we propose an optimal mosaicking structure derived on the basis of physical and numerical considerations, and introduce three reconstruction approaches which are compared and discussed. The super-resolution capabilities of the proposed scheme and its limitations are analyzed, numerically simulated and experimentally demonstrated.

Super-resolution compressive imaging with anamorphic optics.

A new imaging technique that combines compressive sensing and super-resolution techniques is presented. Compressive sensing is accomplished by capturing optically a set of Radon projections. Super-resolution measurements are simply taken by introducing a slanted two-dimensional array in the optical system. The goal of the technique is to overcome resolution limitation that occurs in imaging scenarios where dense pixels sensors with large number of pixels are not available or cannot be used. With the presented imaging technique, owing to the compressive sensing approach, we were able to reconstruct images with significantly more number of pixels than measured, and owing to the super-resolution design we have been able to achieve resolution significantly beyond that limited by the sensor's pixels size.

Compressive sensing spectrometry based on liquid crystal devices.

We present a new type of compressive spectroscopy technique employing a liquid crystal (LC) phase retarder. A tunable LC cell is used in a manner compliant with the compressive sensing (CS) framework to significantly reduce the spectral scanning effort. The presented optical spectrometer consists of a single LC phase retarder combined with a single photo detector, where the LC phase retarder is used to modulate the input spectrum and the photodiode is used to measure the transmitted spectral signal. Sequences of measurements are taken, where each measurement is done with a different state of the retarder. Then, the set of photodiode measurements is used as input data to a CS solver algorithm. We demonstrate numerally compressive spectral sensing with approximately ten times fewer measurements than with an equivalent conventional spectrometer.

Reconstruction guarantees for compressive tomographic holography.

Three-dimensional (3D) object tomography from a two-dimensional recorded hologram is a process of high-dimensional data inference from undersampled data. As such, recently, techniques developed in the field of compressive sensing and sparse representation have been applied for this task. While many applications of compressive sensing for tomography from digital holograms have been demonstrated in the past few years, the fundamental limits involved have not yet been addressed. We formulate the guarantees for compressive sensing-based recovery of 3D objects and show their relation to the physical attributes of the recording setup.