Computer-generated Holograms Research Articles

All-optical interrogation of neural circuits in behaving mice.

Recent advances combining two-photon calcium imaging and two-photon optogenetics with computer-generated holography now allow us to read and write the activity of large populations of neurons in vivo at cellular resolution and with high temporal resolution. Such 'all-optical' techniques enable experimenters to probe the effects of functionally defined neurons on neural circuit function and behavioral output with new levels of precision. This greatly increases flexibility, resolution, targeting specificity and throughput compared with alternative approaches based on electrophysiology and/or one-photon optogenetics and can interrogate larger and more densely labeled populations of neurons than current voltage imaging-based implementations. This protocol describes the experimental workflow for all-optical interrogation experiments in awake, behaving head-fixed mice. We describe modular procedures for the setup and calibration of an all-optical system (~3 h), the preparation of an indicator and opsin-expressing and task-performing animal (~3-6 weeks), the characterization of functional and photostimulation responses (~2 h per field of view) and the design and implementation of an all-optical experiment (achievable within the timescale of a normal behavioral experiment; ~3-5 h per field of view). We discuss optimizations for efficiently selecting and targeting neuronal ensembles for photostimulation sequences, as well as generating photostimulation response maps from the imaging data that can be used to examine the impact of photostimulation on the local circuit. We demonstrate the utility of this strategy in three brain areas by using different experimental setups. This approach can in principle be adapted to any brain area to probe functional connectivity in neural circuits and investigate the relationship between neural circuit activity and behavior.

High-performance cross-sandwich Huygens’ metasurface excited by congener quadrupole

Huygens’ metasurface (HMS) can realize high-efficient real-time control of electromagnetic waves through impedance matching,which is specialty of multipoles. Previous designs of HMS mostly focus on realizing 2π phase coverage by combining different poles, while ignoring the limitation on transmittance caused by different attributes between poles. In this paper, we propose a new design of HMS, which can fulfill​ the excitation of the pure magnetic quadrupole (MQ) on target wavelength. Combined with the theory of multipole scattering, we strip off the interference of electric quadrupole by adding glass layers to the cross-shaped units. By optimizing the parameters of the unit after adding glass, we get an HMS element with extremely high transmittance and phase distribution covering full 2π. We found that the peak of MQ will be simultaneously affected by the thickness and position of glass, while the peak position will only be affected by the former. We also characterize the wavefront manipulation performance of the element by designing and simulating typical applications such as beam deflection, focusing and hologram reconstruction. This efficient wavefront modification indicates that multipole analysis is an effective way of dielectric Huygens’ metasurface parameter optimization and can be applied to computer-generated holograms, structured color display devices.

Quasi-absolute interferometric testing of cylinders.

A three-step quasi-absolute testing method for optical cylinders is proposed in this Letter. Three measurements are taken at the so-called cat's eye position and confocal null testing positions with a computer-generated hologram (CGH) rotated around the axis parallel to that of the cylinder. The quasi-absolute surface error of the cylinder is obtained by simple operations including addition/subtraction and flip of the datasets. The uncertainty is traceable to an optical flat. Two different CGHs are used for a convex cylinder and give consistent quasi-absolute testing results of the surface error, which experimentally validates the method.

Emotion Recognition Using a Reduced Set of EEG Channels Based on Holographic Feature Maps.

An important function of the construction of the Brain-Computer Interface (BCI) device is the development of a model that is able to recognize emotions from electroencephalogram (EEG) signals. Research in this area is very challenging because the EEG signal is non-stationary, non-linear, and contains a lot of noise due to artifacts caused by muscle activity and poor electrode contact. EEG signals are recorded with non-invasive wearable devices using a large number of electrodes, which increase the dimensionality and, thereby, also the computational complexity of EEG data. It also reduces the level of comfort of the subjects. This paper implements our holographic features, investigates electrode selection, and uses the most relevant channels to maximize model accuracy. The ReliefF and Neighborhood Component Analysis (NCA) methods were used to select the optimal electrodes. Verification was performed on four publicly available datasets. Our holographic feature maps were constructed using computer-generated holography (CGH) based on the values of signal characteristics displayed in space. The resulting 2D maps are the input to the Convolutional Neural Network (CNN), which serves as a feature extraction method. This methodology uses a reduced set of electrodes, which are different between men and women, and obtains state-of-the-art results in a three-dimensional emotional space. The experimental results show that the channel selection methods improve emotion recognition rates significantly with an accuracy of 90.76% for valence, 92.92% for arousal, and 92.97% for dominance.

Expanding energy envelope in holographic display via mutually coherent multi-directional illumination

Holographic display is considered as the most promising three-dimensional (3D) display due to its unique feature of reconstructing arbitrary wavefronts. However, the limited étendue, which hinders the immersive experience of observers, remains a major unresolved issue in holographic display technique. In this paper, we propose a novel approach to tweak the constraints of étendue by expanding the energy envelope in holographic display via mutually coherent multi-illumination. The proposed concept contains both a light source design for generating a mutually coherent multi-directional wave and a computer-generated hologram optimization framework for providing high-resolution 3D holograms. To verify the proposed approach, a benchtop prototype of a holographic near-eye display providing an intrinsic large exit-pupil is implemented. The experimental results clearly show that the exit-pupil is effectively expanded by four times and an appropriate viewpoint image is reconstructed according to the view position.

Efficient Computer-Generated Holography Based on Mixed Linear Convolutional Neural Networks

Imaging based on computer-generated holography using traditional methods has the problems of poor quality and long calculation cycles. However, recently, the development of deep learning has provided new ideas for this problem. Here, an efficient computer-generated holography (ECGH) method is proposed for computational holographic imaging. This method can be used for computational holographic imaging based on mixed linear convolutional neural networks (MLCNN). By introducing fully connected layers in the network, the suggested design is more powerful and efficient at information mining and information exchange. Using the ECGH, the pure phase image required can be obtained after calculating the custom light field. Compared with traditional computed holography based on deep learning, the method used here can reduce the number of network parameters needed for network training by about two-thirds while obtaining a high-quality image in the reconstruction, and the network structure has the potential to solve various image-reconstruction problems.

Three-dimensional computer holography enabled from a single 2D image.

To compute a high-quality computer-generated hologram (CGH) for true 3D real scenes, a huge amount of 3D data must be physically acquired and provided depending on specific devices or 3D rendering techniques. Here, we propose a computational framework for generating a CGH from a single image based on the idea of 2D-to-3D wavefront conversion. We devise a deep view synthesis neural network to synthesize light-field contents from a single image and convert the light-field data to the diffractive wavefront of the hologram using a ray-wave algorithm. The method is able to achieve extremely straightforward 3D CGH generation from hand-accessible 2D image content and outperforms existing real-world-based CGH computation, which inevitably relies on a high-cost depth camera and cumbersome 3D data rendering. We experimentally demonstrate 3D reconstructions of indoor and outdoor scenes from a single image enabled phase-only CGH.

Pincushion point-spread function for computer-generated holography.

Point-spread functions (PSFs) are non-stationary signals whose spatial frequency increases with the radius. These signals are only meaningful over a small spatial region when being propagated over short distances and sampled with regular sampling pitch. Otherwise, aliasing at steep incidence angles leads to the computation of spurious frequencies. This is generally addressed by evaluating the PSF in a bounded disk-shaped region, which has the added benefit that it reduces the required number of coefficient updates. This significantly accelerates numerical diffraction calculations in, e.g., wavefront recording planes for high-resolution holograms. However, the use of a disk-shaped PSF is too conservative since it only utilizes about 78.5% of the total bandwidth of the hologram. We therefore derive a novel, to the best of our knowledge, optimally shaped PSF fully utilizing the bandwidth formed by two bounding hyperbola. A number of numerical experiments with the newly proposed pincushion PSF were performed, reporting over three-fold reductions of the signal error and significant improvements to the visual quality of computer-generated holograms at high viewing angles.

Averaging approaches for highly accurate image-based edge localization

We introduce an optical and a digital averaging technique that considerably improves edge localization performance. Especially for high quality images, the optical method achieves measurement uncertainties down to levels of millipixels. The approach uses an optical replication scheme based on a computer-generated hologram to reduce noise and discretization errors. The second method is based on a neural network denoising architecture and is especially suited for high levels of photon noise. Edge localization can be improved by up to 60% while preserving high lateral and temporal resolution. The methods are first tested using high quality images obtained by a scientific CMOS sensor imaging a razor blade mounted on a mechanical stage. Then, the laboratory results are tested for larger distances to validate the methods for building deformation measurements.

Ultrafast farfield simulation of non-paraxial computer generated holograms.

The simulation of large-area diffractive optical elements (DOEs) is challenging when non-paraxial propagation and coupling effects between neighboring structures shall be considered. We developed a novel method for the farfield simulation of DOEs, especially computer-generated holograms (CGHs) with lateral feature sizes in the wavelength range. It uses a machine learning approach to predict the optical function based on geometry parameters. Therefore, training data are calculated by physical simulation methods to create a linear regression model. With the trained model a very fast computation of the farfield is possible with high conformity to results of rigorous methods.

Compact and Scalable Large Vortex Array Generation Using Azocarbazole Polymer and Digital Hologram Printing Technique

An integrated device capable of generating large number of multiplexed optical vortex beams with arbitrary topological charge is considered as one of the crucial requirement for driving information photonics forward. Here we report a simple method for simultaneous generation of 100 multiplexed optical vortex beams from a polymer film of size 1 mm2 and thickness of 30 μm. This is achieved through a combination of computer-generated holography, digital hologram printing and photoisomeric polymers. When the fabricated sample is illuminated with a collimated laser beam, a pre-determined vortex array with arbitrary topological charge is emitted. The polymer film easy to synthesize and exhibits a diffraction efficiency of 30% with a retention period longer than 50 days.

Wide field of view re-configurable foveal projection systems using a phase only spatial light modulator: an application to a new generation of automotive HUDs.

This paper shows how a phase only spatial light modulator (SLM) can be used to implement compact, wide field of view (FOV) projection systems, using a foveal imaging approach. The SLM is tested in two different configurations. First, as a wavefront corrector to locally compensate aberrations in wide angle projection, when illuminated by a white (RGB) light source. Second, as re-configurable diffractive optical element (DOE) to display phase computer generated holograms (CGHs) of wide angle images, that include aberration corrections for peripheral regions of interest (ROI) of the output image, when illuminated by a coherent source. Finally, the foveal projection system is coupled with a commercial combiner to project 11° ×4° virtual images at 2~m and assess the foveal correction method on different ROIs in the FOV.

Verification of Polarization Matching on the Hologram Recording Plane for the Implementation of an Optimized Digital Hologram Printing System

Conventional studies on digital hologram printer have mainly been conducted on image generation and reconstruction such as multi-view image generation method, computer-generated hologram (CGH) and a method of displaying a wave-front for a 3D object. However, There should be a criterion to evaluate the quality of the reconstructed image because hologram printer use photosensitive recording interference patterns in holographic material. For this reason, The evaluation method of the completed hologram using a digital hologram printer is focused on how well it came out without aberration or how high intensity of light has been reconstructed. However polarization has an effect on hologram regeneration efficiency because holography uses a laser that generates electromagnetic waves. Hologram recording method is usually to match linear polarization in the same direction. but digital hologram printer composed of complex optical components that causes a phase shifting due to a setup error. it will be a problem for resulting in unwanted polarization at the final recording plane. In this paper, we analyzed the possible polarization changes and analyze the optimal polarization matching status using values from hologram results and use them as a study to improve the efficiency of hologram result from holographic printer.

Imaging evaluation of computer-generated hologram by using three-dimensional modified structural similarity index

Image quality evaluation is a key factor in the advancement and improvement of display technology, which could lead to effective improvement through the evaluation result from various aspects to achieve the better result further. However, display principles and image characteristics should be considered for Image quality evaluation. For three-dimensional (3D) holograms, charge-coupled devices are mainly applied to capture and record reconstructed images for analysis. 3D holograms have multiple depths and thus can lead to varying light intensities at the information points of the reconstructed images during image acquisition. Finally, it will lead to poor analysis results. Aiming at the previously mentioned problem, an algorithm called three-dimensional structural similarity (3D-SSIM) is proposed in this study. It is expected to optimize hologram evaluation and analysis. A fixed-focus shooting system matching silhouette sampling and SSIM is proposed to sample objects with 360° in order to implement 3D SSIM evaluation. This study successfully optimized the hologram evaluation method, leading to more accurate image evaluation results of hologram algorithms or holographic display systems.

Development of a Fringe Printer With 0.35 μm Pixel Pitch

The development of computational devices can provide computer-generated holograms (CGH) of over 100 Giga-pixel easily. The performance improvement of the graphical processing unit makes calculations faster with a normal personal computer. In contrast, the output device of CGH is not reported much. Since CGH is a fringe pattern, high resolution and fine pixel pitch are required for the output device. We have been developing a direct fringe printer, which consists of a laser, an X-Y stage, an SLM, and optical parts available on the market. Our previous report presented a liquid crystal panel, which was used for SLM that had full HD resolution and 7 μm pixel pitch. Since the Pixel pitch of the liquid crystal panel is not small enough for CGH, the optical setup works to demagnify the pixel pitch of the printed fringe pattern. To record high resolution CGH, the calculated fringe pattern is split and exposed in tiles by the X–Y stage. The results showed a 0.44 μm pixel pitch and over 100 Gpixel CGHs. However, to output more high-quality CGH, the development of pixel pitch and resolution is very important. In this paper, we review the optical system of a fringe printer that achieved an output of 0.35 μm pixel pitch CGH. We also investigate the performance of the new fringe printer.

Deep-Learning Computational Holography: A Review

Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.

Computer-Generated Holograms Application in Security Printing

In the present article the application of computer-generated holograms in security printing was considered. The main subject was creation of informative memory marks based on CGH for automatic read out of the information embedded in this marks that can be used for identification, copyrighting, or other types of authentication control of security elements. The fundamental theoretical basics of computer-generated holograms and its applications were given. The proposed technical solutions, such as “Printed hologram”, “Smart verification”, and “Holomemory”, were considered in detail. The announced solutions were presented in the schemes of interaction between manufacturers of security printing and its potential consumers—citizens and regulatory government agencies.

Experimental investigation in nodal aberration theory (NAT): separation of astigmatic figure error from misalignments in a Cassegrain telescope.

We present simulations and experimental validations for separating astigmatic figure error from misalignments in Nodal Aberration Theory (NAT) with a high-precision Cassegrain telescope. Both the primary mirror figure error and the secondary mirror misalignments induce binodal astigmatism for the telescope systems. The separation of these two aberration factors plays a crucial role in the telescope alignment process. In this study, the figure error of the aspheric primary mirror of the Cassegrain telescope induced by the mirror mounts was measured interferometrically utilizing a computer-generated hologram (CGH). According to the primary mirror figure error, the astigmatic node locations in the image plane were simulated using real raytracing. The center of the nodes was located on the field center, and the nodes were placed symmetrically with respect to the field center in the image plane. The telescope's alignment was performed using the simulation results, and the node locations were measured on a grid of field points interferometrically. Thereafter, secondary mirror misalignments around the coma-free pivot point were introduced into the optical system, and the node's center was shifted from the field center in the image plane as predicted by NAT. The simulations and interferometric field measurements were performed and compared on a grid of field points for the misaligned state in the presence of primary mirror figure error. The experimental results confirm the predictions from NAT. Statistical analysis was also performed to confirm the accuracy and stability of the measurements.

Enhanced Single-Beam Multiple-Intensity Phase Retrieval Using Holographic Illumination

Single-beam multiple-intensity iterative phase retrieval is a high-precision and lens-free computational imaging method, which reconstructs the complex-valued distribution of the object from a volume of axially captured diffraction intensities using the post-processing algorithm. However, for the object with slowly-varying waves, the method may encounter the problem of convergence stagnation since the lack of diversity between the captured intensity patterns. In this paper, a novel technique to enhance phase retrieval using holographic illumination is proposed. One special computer-generated hologram is designed, which can generate multiple significantly different images at the required distances. The incident plane wave is firstly modulated by the hologram, and then the exit wave is used to illuminate the object. Benefitting from this holographic illumination, remarkable intensity changes in the given detector planes can be produced, which is conducive to fast and high-accuracy reconstruction. Simulation and optical experiments are performed to verify the feasibility of the proposed method.

Deep-learning-based computer-generated hologram from a stereo image pair.

We propose a deep-learning-based approach to producing computer-generated holograms (CGHs) of real-world scenes. We design an end-to-end convolutional neural network (the Stereo-to-Hologram Network, SHNet) framework that takes a stereo image pair as input and efficiently synthesizes a monochromatic 3D complex hologram as output. The network is able to rapidly and straightforwardly calculate CGHs from the directly recorded images of real-world scenes, eliminating the need for time-consuming intermediate depth recovery and diffraction-based computations. We demonstrate the 3D reconstructions with clear depth cues obtained from the SHNet-based CGHs by both numerical simulations and optical holographic virtual reality display experiments.