We introduce a computer-generated holography system based on the theory of partial spatial coherence, simplifying the required optical setups and improving computational efficiency. The method utilizes the generalized Van Cittert-Zernike Schell (GVS) model to calculate the forward propagation of light emitted by a common mobile phone screen and reflected by a phase-only spatial light modulator at various propagation distances. An inverse problem is then solved using stochastic gradient descent optimization to obtain the intensity and phase holograms that produce the desired intensity images at different depths. We conduct a two-layer optimization validated through numerical and optical experiments. Using the GVS model, we report speed improvements of 15× and higher-quality numerical reconstructions over the reference method of compressive propagation with random wavefronts. Furthermore, we achieve numerical and optical 3D focus-defocus effects by optimizing a focal stack with 20 depth layers.

Recent advances in computer-generated holography (CGH) have significantly improved visual quality through high-resolution rendering; however, the accompanying increase in data size has become a critical obstacle to practical deployment. Conventional image compression techniques such as JPEG and High Efficiency Video Coding (HEVC) do not adequately account for the unique statistical and spectral characteristics of holograms, thereby limiting both compression efficiency and reconstruction quality. This study proposes an efficient compression framework specialized for digital holograms by integrating a ringing reduction technique for diffraction calculations with CompressAI, a deep learning–based image compression framework. We demonstrate that ringing artifacts are a major factor hindering efficient hologram compression, and show that by enabling neural networks to learn the intrinsic statistical and spectral features of holograms. Our method achieves a superior balance between compression efficiency and reconstruction quality compared to conventional approaches, particularly at low bit rates. Furthermore, by introducing a multi-channel input representation, our method achieves higher compression ratios.

The split-Lohmann lens technique has been proposed for computer-generated holograms, demonstrating exceptional computational efficiency. It requires a single 2D image, along with its corresponding depth map, to generate holograms with continuous depth at low computational cost. However, its reliance on a single-view depth map presents limitations, rendering it incapable of handling parallax and occlusion effects for multi-view observation. Polygon-based methods possess the capability to resolve this issue. This manuscript proposes an algorithm that applies the split-Lohmann lens technique to polygon-based holograms, demonstrating its feasibility for three-dimensional reconstruction and its superior computational efficiency compared to traditional polygon-based methods and the point-polygon hybrid method.

We showcase INTERFERE, a hologram compression framework selected as the basis of the JPEG Pleno Holography standard. It supports view-dependent coding with simultaneous spatial and angular random access. INTERFERE utilizes an adaptive quantization mechanism that can assign a variable bit width across small phase-space regions. This ensures that the transform coefficients are compactly represented before entropy coding. In this work, we design a new entropy coding mechanism with division-free binary arithmetic coding, allowing us to better capitalize on the compact quantized representation. We also create new probability models for driving the binary arithmetic coder, which can fully harness SIMD instructions while exhibiting significantly smaller memory requirements, enabling it to reside in the CPU cache. Speed-ups ranging from 6x - 600x in decoding and encoding times on the CPU were achieved over our previous solution without compromising rate-distortion performance. With this work, we propose a practical hologram codec that is class-leading in rate-distortion performance, ease of random access, and encoding/decoding throughput, thereby providing a practical solution to one of digital holography's most pressing challenges.

Numerical Fresnel diffraction is broadly used in optics and holography in particular. So far, it has been implemented using convolutional approaches, spatial convolutions, or the fast Fourier transform. We propose a new way of computing Fresnel diffraction using Gabor frames and chirplets. Contrary to previous techniques, the algorithm has linear time complexity, does not exhibit aliasing, does not need zero padding, has no constraints on changing shift/resolution/pixel pitch between source and destination planes, and works at any propagation distance. We provide theoretical and numerical analysis, detail the algorithm, and report simulation results with an accelerated GPU implementation. This algorithm may serve as a basis for more flexible, faster, and memory-efficient computer-generated holography methods.

We introduce a computer-generated holography system based on the theory of partial spatial coherence that simplifies the required optical setups and improves the computational efficiency. The method utilizes the generalized Van Cittert-Zernike Schell (GVS) model to calculate the forward propagation of light emitted by a common mobile phone screen and reflected by a phase-only spatial light modulator at various propagation distances. An inverse problem is then solved using stochastic gradient descent optimization to obtain the intensity and phase holograms that produce the desired intensity images at different depths. We conduct a two-layer optimization validated through numerical and optical experiments. By using the GVS model, we report speed improvements of 15x and higher-quality numerical reconstructions over the reference method of compressive-propagation with random wavefronts. Furthermore, we achieve numerical and optical 3D focus-defocus effects by optimizing a focal stack with 20 depth layers.

Hologram compression presents a significant challenge, as holograms differ fundamentally from ordinary images. To address this, this study proposed a lossy hologram compression method utilizing tensor decomposition.

In this article, we focus on the problem of sampling a point spread function (PSF) in a tilted plane for computational wave optics and holography. Conventionally, the PSF represents a symmetric wavefield distribution expressed in an orthogonal plane to the target hologram. In contrast, we reveal the wavefield for a point-spread function over a tilted plane, showing an asymmetric structure depending on the tilt angle. An asymmetric PSF formula was mathematically derived and proposed. The axial propagation of the asymmetric PSF was validated by applying it to the point-polygon hybrid method of computer-generated holograms. The off-axis propagation of the asymmetric PSF was confirmed in optical experiments by modulating the obliquely incident light with the asymmetric phase pattern uploaded to the spatial light modulator. The proposed method allows accurate off-axis light, propagation, which is not possible with a conventional symmetric PSF.

We present a holography technique with daily-use light in which multidimensional information such as three-dimensional (3D) space, phase, wavelength, and polarization are simultaneously recorded as a multiplexed hologram. Multidimensional information is actively acquired in the field of machine vision and utilized for object recognition. Holography is well known as a technique to acquire multidimensional information on a two-dimensional (2D) recording material by introducing laser light sources and spatial or temporal frequency-division multiplexing. We have enabled multidimension-multiplexed holographic imaging with a monochrome image sensor without spatial or temporal frequency-division multiplexing by developing the computational coherent superposition (CCS) scheme. Exploiting CCS and incoherent digital holography or self-reference digital holography, we achieved simultaneous holographic image sensing of multidimensional information with daily-use light and a monochrome image sensor. We present optical designs to realize the proposed holography and the experimental results obtained with the developed optical systems.

High computational requirements often limit computer-generated holography (CGH). Phase-added stereogram methods reduce this cost by approximating point-spread functions by plane wave segments. While this sparse representation allows for a speedup, this approximation introduces blocking artifacts and reduces accuracy. We present a novel approach that integrates the Gabor transform to make sparse diffraction calculations. Additionally, we partition the point cloud into Lozenge-shaped cells, optimizing memory management on GPU architectures by using cache-friendly operations. Experimental results on large point clouds demonstrate a significant improvement in speed and accuracy, achieving up to a 47× speedup over optimized brute-force methods with enhanced quality over classic phase-added stereograms.

We present a wave propagation algorithm that models the diffraction of partially spatially coherent light and can be efficiently computed using a single forward propagation. The model uses the Van Cittert-Zernike theorem to find the complex coherence factor from a light source reaching the object plane, and it then uses the generalized Schell's theorem to compute the diffraction pattern produced at a given propagation distance, not limited to the far field. The numerical propagation is computed by a series of fast Fourier transforms, and it can be adapted to use any existing coherent propagator, giving it high flexibility with minimal additional computational cost. We report speed improvements up to two orders of magnitude and accuracy improvements up to three orders of magnitude over sample-based methods to model partially spatially coherent light in computer-generated holography (CGH) and digital holography (DH).

We propose a lossy compression technique for bi-level holograms by utilizing INTERFERE, an STFT-based codec in concert with the DFT’s conjugate symmetry property for real-valued signals. At high compression ratios, this framework outperforms all other known solutions.

Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography.

With digital holographic display and recording setups steadily improving and the advent of realistic super-highresolution holograms (>100 megapixels), the efficient compression of digital holograms (DHs) becomes an urgent matter. Therefore, JPEG Pleno holography is undergoing a standardization effort to address this challenge. The accepted, current baseline coding solution for lossy compression of complex-valued DHs, entitled INTERFERE, is presented in this paper. Its features include a simple and modular overall architecture, high scalability, viewselective coding, low decoder complexity, and the highest rate-distortion performance among state-of-the-art solutions. We also introduce, to our knowledge, a novel meta-quantization strategy that can be used for signals exhibiting large variations in dynamic range in the domain being quantized. We were able to demonstrate on the versatile JPEG Pleno hologram database BD-rate reductions between 16% and 272% (average of 119%) over HEVC for achieving an SNR in the range 5-25 dB. With this first compression standard on DHs, we hope to provide an essential building block for their future commercialization in large-scale consumer markets. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Conventional diffraction calculations typically employ complex Fourier transforms in which the source and target fields are represented by complex values. However, this approach is inefficient for certain applications. To address this problem, this study introduces diffraction calculations for three fields: real-to-complex, complex-to-real, and real-to-real. These calculations utilize a real-valued fast Fourier transform and Hermite symmetry, enabling accelerated computation by eliminating half of the spectra. This study also demonstrates the practical applications of these diffraction calculations. These applications include image reproduction in digital holography, speckle reduction in holographic projection, and accelerated hologram computation in holographic displays.

Computational holography involves creating complex holographic patterns, which is both fundamental and computationally intensive. This process presents significant challenges, particularly in achieving real-time hologram generation. This study presents a thorough comparison and analysis of computational efficiency for computing polygon-based computer-generated holograms (CGH) in terms of programming language (Python and MATLAB), execution hardware (CPU and GPU) and algorithms (interpolation-based and analytical-based). We open-sourced all the codes used for polygonal CGH executed in both MATLAB and Python, offering valuable insights into the performance suitability of different algorithms and languages. Basically, MATLAB demonstrates superior performance over Python, especially for CPU calculations, whereas it performs similarly when utilizing a graphics processing unit (GPU) and an accelerated algorithm like the wavefront recording plane (WRP) method. Analytical-based method and interpolation-based method are not consistently superior; the former performs well when addressing small matrices (e.g., using WRP), while the latter performs well when addressing large matrices.

A novel scanning particle image velocimetry technique, to the best of our knowledge, is proposed to characterize flows in microfluidic applications. Three-dimensional information is acquired by oscillating the target sample over a fixed focal plane, allowing the reconstruction of particle trajectories with micrometer accuracy over an extended depth. This technology is suited for investigating acoustic flows with unprecedented precision in microfluidic applications. In this contribution, we describe the experimental setup and the data processing pipeline in detail; we study the technique’s performance by reconstructing pressure-driven flow; and we report the three-dimensional trajectory of a 2 µm particle in an acoustic flow in a 525µm×375µm microchannel with micrometric accuracy.