Single-shot digital holography in Gabor mode offers cost-effective quantitative phase imaging but suffers from the fundamental twin image problem, where real and conjugate images are inherently superimposed, severely limiting phase reconstruction accuracy. Traditional iterative phase retrieval methods require computationally expensive multiple propagations, while off-axis holography demands complex optical setups with precise alignment. We present the first unsupervised diffusion model for automated phase image reconstruction from single-shot in-line holograms, eliminating both twin image artifacts and the need for expensive off-axis configurations. Our framework integrates cycle-consistency and denoising modules to enable training on unpaired hologram-phase image datasets, learning the mapping between low-cost in-line measurements and high-quality phase distributions without requiring labeled data pairs. Comprehensive evaluation on diverse biological specimens demonstrates that our approach significantly outperforms conventional unsupervised methods, achieving superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values for both red blood cells and cancer cells. Critically, the model maintains exceptional performance even with limited training data, consistently outperforming supervised learning approaches under data-constrained conditions. The framework exhibits remarkable generalization capabilities, successfully reconstructing phase images from holograms captured at different propagation distances and processing various cancer cell types not included in training data. This computational breakthrough enables accurate, scalable, and hardware-efficient quantitative phase imaging, democratizing access to high-quality phase microscopy for resource-constrained environments while maintaining reconstruction fidelity comparable to complex off-axis systems.

A secret sharing scheme is an important cryptographic procedure that enables the secure distribution of secret information, such as private images, in an untrusted network. However, in all secret sharing schemes, the sizes of the shares increase in proportion to the size of the secret information, because they involve computationally expensive polynomials of degree$n-1$(n is total number of shares) and increasingly large modulus of modulo operations for security. Moreover, with large share sizes, it is not easy in practice to encrypt them quickly using block cipher algorithms. Therefore, ensuring the security of secret sharing for large-scale visual data with a reasonable share length and efficiently encrypting n shares properly represents formidable challenges. To overcome these challenges in secret sharing schemes, we propose new multiparty random phase wrapping secret sharing systems for visual datasets. Computational optical imaging enables the acquisition of wrapped phase information, ranging from –$\pi $to$\pi $, to be expressed in the form of a complex sinusoidal waveform. The proposed scheme allows for large-scale secret data—such as confidential digital images and visual data, including optical images—to be securely and efficiently shared and distributed to multiple parties or agencies utilizing a digital representation of a complex sinusoidal waveform of the secret information. The proposed scheme can be useful in cryptographic key escrow systems and in use with large-scale secret data.

Zero-knowledge proof systems based on Feige-Fiat–Shamir (FFS) protocol are an interactive protocol between two anonymous authentication parties. However, they require heavy computations because of many iterations for reducing the probability that an attacker can trick a remote server. The algorithm’s time complexity rapidly increases with the total number of the challenge values, which should be unpredictable. Hence, the FFS protocol is not suitable for practical zero-knowledge proof systems. In this study, we propose new zero-knowledge proof systems based on phase mask generation that are complex sinusoidal waveform versions of the FFS algorithm for efficient anonymous authentication in the diverse interactive systems. The proposed anonymous authentication schemes need a single iteration only, allowing for efficient uses of a random challenge mask with large bit-depth. The proposed schemes allow the verifier to verify that the prover knows the secret mask, such as binary pattern, visual image, or hologram, which are the prover’s secrets, without revealing any information about it to anyone else, including the verifier. Various numerical simulations demonstrate the proposed schemes’ feasibility and robustness.

This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial network (GAN) with marker-controlled watershed segmentation scheme. The GAN model performed RBC segmentations and classifications to develop ageing markers, and the watershed segmentation was used to completely separate overlapping RBCs. Our approach achieved good segmentation and classification accuracy with a Dice's coefficient of 0.94 at a high throughput rate of about 152 cells per second. These results were compared with other deep neural network architectures. Moreover, our image-based deep learning models recognized the morphological changes that occur in RBCs during storage. Our deep learning-based classification results were in good agreement with previous findings on the changes in RBC markers (dominant shapes) affected by storage duration. We believe that our image-based deep learning models can be useful for automated assessment of RBC quality, storage lesions for safe transfusions, and diagnosis of RBC-related diseases.

We propose novel random phase key exchange schemes for image cryptography, which are complex sinusoidal waveform versions of the Diffie-Hellman (DH) key exchange algorithm. We demonstrate that the proposed schemes can be particularly helpful to establish a shared secret key in Fourier optics-based image cryptosystems since the problem of symmetric secret key establishment is one of the fundamental difficulties in Fourier optics-based image cryptography. We also propose a ring-type key exchange system in which multiple users can securely share a secret key, which is an extension of the key exchange scheme for two users. The schemes are verified by numerical simulations.

Digital holographic microscopy is an ideal tool for 3-D cell imaging and characterization. It provides a host of cell parameters based on cell morphology and its temporal dynamics or time variation. These parameters can be used to study and quantify cell growth and cell physiology. When coupled with classification algorithms, this technique can also be used to identify and classify cells such as blood cells for automated disease identification. A compact, portable version of this 3-D optical imaging system has the potential to become a device for compact field portable biological data collection, analysis, and cell identification leading to disease diagnosis with mobile devices, low cost instruments for deployment in remote areas with limited access to healthcare to combat disease. In this paper, we present an overview of our reported work on the development of digital holographic microscopes and their applications in 3-D cell imaging, cell parameter extraction and cell classification for potential automated disease identification.

Multidimensional optical imaging systems for information processing and visualization technologies have numerous applications in fields such as manufacturing, medical sciences, entertainment, robotics, surveillance, and defense. Among different three-dimensional (3-D) imaging methods, integral imaging is a promising multiperspective sensing and display technique. Compared with other 3-D imaging techniques, integral imaging can capture a scene using an incoherent light source and generate real 3-D images for observation without any special viewing devices. This review paper describes passive multidimensional imaging systems combined with different integral imaging configurations. One example is the integral-imaging-based multidimensional optical sensing and imaging systems (MOSIS), which can be used for 3-D visualization, seeing through obscurations, material inspection, and object recognition from microscales to long range imaging. This system utilizes many degrees of freedom such as time and space multiplexing, depth information, polarimetric, temporal, photon flux and multispectral information based on integral imaging to record and reconstruct the multidimensionally integrated scene. Image fusion may be used to integrate the multidimensional images obtained by polarimetric sensors, multispectral cameras, and various multiplexing techniques. The multidimensional images contain substantially more information compared with two-dimensional (2-D) images or conventional 3-D images. In addition, we present recent progress and applications of 3-D integral imaging including human gesture recognition in the time domain, depth estimation, mid-wave-infrared photon counting, 3-D polarimetric imaging for object shape and material identification, dynamic integral imaging implemented with liquid-crystal devices, and 3-D endoscopy for healthcare applications.

We have become an information-centric society vastly dependent on the collection, communication, and presentation of information. At any given moment, it is likely that we are in the vicinity of some form of a display as displays play a prominent role in a variety of devices and applications. Three-dimensional imaging and display technologies are important components for presentation and visualization of information and for creating real-world-like environments in communication. There are broad applications of 3-D imaging and display technologies in computers, communication, mobile devices, TV, video, entertainment, robotics, metrology, security and defense, healthcare, and medicine.

The capture and display of images of 3-D scenes under incoherent and polychromatic illumination is currently a hot topic of research, due to its broad applications in bioimaging, industrial procedures, military and surveillance, and even in the entertainment industry. In this context, Integral Imaging (InI) is a very competitive technology due to its capacity for recording with a single exposure the spatial-angular information of light-rays emitted by the 3-D scene. From this information, it is possible to calculate and display a collection of horizontal and vertical perspectives with high depth of field. It is also possible to calculate the irradiance of the original scene at different depths, even when these planes are partially occluded or even immersed in a scattering medium. In this paper, we describe the fundaments of InI and the main contributions to its development. We also focus our attention on the recent advances of the InI technique. Specifically, the application of InI concept to microscopy is analyzed and the achievements in resolution and depth of field are explained. In a different context, we also present the recent advances in the capture of large scenes. The progresses in the algorithms for the calculation of displayable 3-D images and in the implementation of setups for the 3-D displays are reviewed.

Over the last years, three-dimensional (3-D) imaging has been applied to human action and gesture recognition, usually in the form of depth maps from RGB-D sensors. An alternative which has not been explored is 3-D integral imaging, aside from a recent preliminary study which shows that it can be an effective sensory modality with some advantages over the conventional monocular imaging. Since integral imaging has also been shown to be a powerful tool in other visual tasks (e.g., object reconstruction and recognition) under challenging conditions (e.g., low illumination, occlusions), and its passive long-range operation brings benefits over active close-range devices, a natural question is whether these advantages also hold for gesture recognition. Furthermore, occlusions are present in many real-world scenarios in gesture recognition, but it is an elusive problem which has scarcely been addressed. As far as we know, this letter analyzes for the first time the potential of integral imaging for gesture recognition under occlusions, by comparing it to monocular imaging and to RGB-D sensory data. Empirical results corroborates the benefits of 3-D integral imaging for gesture recognition, mainly under occlusions.

Digital holographic microscopy which has established itself as one of the leading techniques for quantitative phase contrast imaging. It has many advantages over conventional microscopy such as, improvement in image contrast and providing thickness profile of the object along the propagation direction of the probe beam. Other advantages are that numerical focusing and lensless imaging are possible with digital holography. Also from a series of holograms the time varying thickness profile of the object can be determined and used to evaluate the object. Here, we present an overview of our published work on three dimensional microscopy with digital holography for visualization and identification of manmade and biological specimen for their evaluation and identification.

This paper presents an overview of research work and some novel strategies and results on using data fusion in 3-D imaging when using multiple information sources. We examine a variety of approaches and applications such as 3-D imaging integrated with polarimetric and multispectral imaging, low levels of photon flux for photon-counting 3-D imaging, and image fusion in both multiwavelength 3-D digital holography and 3-D integral imaging. Results demonstrate the benefits data fusion provides for different purposes, including visualization enhancement under different conditions, and 3-D reconstruction quality improvement.

Recently, there has been a substantial increase in efforts to develop 3-D visualization technologies that can provide the viewers with a realistic 3-D visual experience. Various terms such as “reality communication” have been used to categorize these efforts. In order to provide the viewers with a complete and realistic visual sensation, the display or visualization system and the displayed content need to match the physiological 3-D information sensing capabilities of the human visual system which can be quite complex. These may include spatial and temporal resolutions, depth perception, dynamic range, spectral contents, nonlinear effects, and vergence accommodation effects. In this paper, first we present an overview of some of the 3-D display research efforts which have been extensively pursued in Asia, Europe, and North America among other areas. Based on the limitations and comfort-based requirements of the human visual system when viewing a nonnatural visual input from 3-D displays, we present an analytical framework that combines main perception and human visual requirements with analytical tools and principles used in related disciplines such as optics, computer graphics, computational imaging, and signal processing. Building on the widely used notion of light fields, we define a notion of perceivable light fields to account for the human visual system physiological requirements, and propagate it back to the display device to determine the display device specifications. This helps us clarify the fundamental and practical requirements of the 3-D display devices for reality viewing communication. In view of the proposed analytical framework, we overview various methods that can be applied to overcome the extensive information needed to be displayed in order to meet the requirements imposed by the human visual system.

Three-dimensional imaging is still not in the full commercial stage, but its application is widening due to its capability of creating real-world-like environments. This capability is especially important in realizing reality communication and telepresence operations in medical and unreachable places. For these applications, technologies for interacting with objects in the 3-D image should also be developed. The widespread use of 3-D images is expected in near future, but before that, problems of minimizing physical stresses, especially eye fatigue, should be solved for 3-D imaging. Currently, expected solutions to the problems are electroholography and super multiview methods. These two methods work at different principles, but they both can provide the continuous parallax as in the real-world scenes/objects. Electroholography can also provide focusable depth information to the viewers, though its viewing zone angle is limited. For the super multiview, it is not apparent that it can provide the focusable depth information, but it can provide more comfortable viewing condition than the holography because of its wider viewing zone angle.

Three-dimensional (3-D) optical image sensing and visualization technologies have been researched extensively for different applications in fields as diverse as entertainment, medical sciences, robotics, manufacturing, and defense. In many instances, the capabilities of 3-D imaging and display systems have revolutionized the progress of these disciplines, enabling new detection/display abilities that would not have been otherwise possible. As one of the promising methods in the area of 3-D sensing and display, integral imaging offers passive and relatively inexpensive way to capture 3-D information and to visualize it optically or computationally. The integral imaging technique belongs to the broader class of multiview imaging techniques and is based on a century old principle which has only been resurrected in the past decade owing to advancement of optoelectronic image sensors as well as the exponential increase in computing power. In this paper, historic and physical foundations of integral imaging are overviewed; different optical pickup and display schemes are discussed and system parameters and performance metrics are described. In addition, computational methods for reconstruction and range estimation are presented and several applications including 3-D underwater imaging, near infra red passive sensing, imaging in photon-starved environments, and 3-D optical microscopy are discussed among others.

Three-dimensional (3-D) imaging techniques have the potential to establish a future mass-market in the fields of entertainment and communications. Integral imaging (InI), which can capture and display true 3-D color images, has been seen as the right technology for 3-D viewing for audiences of more than one person. Due to the advanced degree of its development, InI technology could be ready for massive commercialization in the coming years. This development is the result of a strong research effort performed over the past few years. In this sense, this paper is devoted to reviewing some recent advances in InI, which have allowed improvement in the response of InI systems to the problems of the limited depth of field, poor axial and lateral resolution, pseudoscopic-to-orthoscopic conversion, production of 3-D images with continuous relief, or the limited range of viewing angles of InI monitors.

This paper presents an overview of the potential of free space optical technology in information security, encryption, and authentication. Optical waveform posses many degrees of freedom such as amplitude, phase, polarization, spectral content, and multiplexing which can be combined in different ways to make the information encoding more secure. This paper reviews optical techniques for encryption and security of two-dimensional and three-dimensional data. Interferometric methods are used to record and retrieve data by either optical or digital holography for security applications. Digital holograms are widely used in recording and processing three dimensional data, and are attractive for securing three dimensional data. Also, we review optical authentication techniques applied to ID tags with visible and near infrared imaging. A variety of images and signatures, including biometrics, random codes, and primary images can be combined in an optical ID tag for security and authentication.

The ability to sense, track, identify, and monitor biological micro/nanoorganisms in a real-time, automated, and integrated system is of great importance from both scientific and technological standpoints. Such a system and its possible variants would have numerous applications in a wide spectrum of fields, including defense against biological warfare, disease control, environmental health and safety, and medical treatments. In this paper, we review a comprehensive mixture of optical and computational tools developed in our group aiming at real-time sensing and recognition of biological microorganisms. Digital in-line holographic microscopy is used with both coherent and partially coherent illumination to probe the specimen interferometerically. The interference pattern is then recorded on an optoelectronic image sensor and transferred to a computer where special statistical algorithms are performed to segment, recognize, and track the microorganisms within the field of view of the microscope. The advantages of proposed holographic sensing are described compared to conventional two-dimensional imaging systems. In addition, the theoretical aspects and fundamental limitations of digital in-line holographic microscopy are discussed, which determine the relationship between system parameters and achievable performance. The proposed optical-digital integrated system for automated, real-time sensing and recognition of biological microorganisms has been deemed promising with the potential of widespread application. We demonstrate how the proposed techniques function together in a series of experiments.

We present a new method for three-dimensional (3-D) visualization and identification of biological microorganisms using partially temporal incoherent light in-line (PTILI) computational holographic imaging and multivariate statistical methods. For 3-D data acquisition of biological microorganisms, the band-pass filtered white light is used to illuminate a biological sample. The transversely and longitudinally diffracted pattern of the biological sample is magnified by microscope objective (MO) and is optically recorded with an image sensor array interfaced with a computer. Three-dimensional reconstruction of the biological sample from the diffraction pattern is accomplished by using computational Fresnel propagation method. Principal components analysis and nonparametric inference algorithms are applied to the 3-D complex amplitude biological sample for identification purposes. Experiments indicate that the proposed system can be useful for identifying biological microorganisms. To the best of our knowledge, this is the first report on using PTILI computational holographic microscopy for identification of biological microorganisms.

We present a novel nonuniform quantization compression technique-histogram quantization-for digital holograms of 3-D real-world objects. We exploit a priori knowledge of the distribution of the values in our data. We compare this technique to another histogram based approach: a modified version of Max's algorithm that has been adapted in a straight-forward manner to complex-valued 2-D signals. We conclude the compression procedure by applying lossless techniques to our quantized data. We demonstrate improvements over previous results obtained by applying uniform and nonuniform quantization techniques to the hologram data.

Digital holography is a technique that permits digital capture of holograms and subsequent processing on a digital computer. This paper reviews various applications of this technique. The presented applications cover three-dimensional (3-D) imaging as well as several associated problems. For the case of 3-D imaging, optical and digital methods to reconstruct and visualize the recorded objects are described. In addition, techniques to compress and encrypt 3-D information in the form of digital holograms are presented. Lastly, 3-D pattern recognition applications of digital holography are discussed. The described techniques constitute a comprehensive approach to 3-D imaging and processing.

The essential components of generating three-dimensional (3-D) images are defined, and various methods of creating each of the components are explained. The components are image acquisition, image multiplexing and processing, and display mechanisms. For image acquisition, transforming, synthesizing based on disparity, voxel and wavefront deformation, and sampling and photographing methods are used. For multiplexing, time, spatial, and spatiotemporal, and for processing, chirping, layering, and pixel cell based multiview image arrangement methods are used. The display mechanisms are classified into projection, contact,and scanning types depending on their means of displaying images. From these methods, 3-D images with real volume, with parallax only and with psychologically induced depth sense can be generated. The display mechanisms have another important mission of creating viewing zones. For this purpose, all 3-D imaging methods employ a special form of optics or mechanisms complying with their image multiplexing schemes. These optics and mechanisms are essential in realizing 3-D imaging systems but at the same time they provide many unfriendly and uncomfortable effects to viewers, and also impose some functional limitations.

Three dimensional (3-D) imaging and display have been subjects of much research due to their diverse benefits and applications. However, due to the necessity to capture, record, process, and display an enormous amount of optical data for producing high-quality 3-D images, the developed 3-D imaging techniques were forced to compromise their performances (e.g., gave up the continuous parallax, restricting to a fixed viewing point) or to use special devices and technology (such as coherent illuminations, special spectacles) which is inconvenient for most practical implementation. Today's rapid progress of digital capture and display technology opened the possibility to proceed toward noncompromising, easy-to-use 3-D imaging techniques. This technology progress prompted the revival of the integral imaging (II)technique based on a technique proposed almost one century ago. II is a type of multiview 3-D imaging system that uses an array of diffractive or refractive elements to capture the 3-D optical data. It has attracted great attention recently, since it produces autostereoscopic images without special illumination requirements. However, with a conventional II system it is not possible to produce 3-D images that have both high resolution, large depth-of-field, and large viewing angle. This paper provides an overview of the approaches and techniques developed during the last decade to overcome these limitations. By combining these techniques with upcoming technology it is to be expected that II-based 3-D imaging systems will reach practical applicability in various fields.

We introduce optical imaging techniques for three-dimensional (3-D)visualization and identification of microorganisms. Three-dimensional sensing and reconstruction is performed by single-exposure on-line (SEOL)digital holography. A coherent microscope-based Mach-Zehnder interferometer records Fresnel digital holograms of microorganisms. Complex amplitude holographic images are computationally reconstructed at different depths by an inverse Fresnel transformation. For pattern recognition/identification, two approaches are addressed. One is 3-D morphology-based recognition and the other is shape-tolerant 3-D recognition. In the first approach, a series of image recognition techniques is used to analyze 3-D geometrical shapes of microorganisms, which is composed of magnitude and phase distributions. Segmentation, feature extraction, graph matching, feature selection, training, and decision rules are presented. For the second approach, a number of sampling segments are arbitrarily extracted from the reconstructed 3-D biological microorganism. These samples are processed using a number of cost functions and statistical inference theory for the equality of means and equality of variances between the sampling segments. Experimental results with sphacelaria alga, tribonema aequale alga, and polysiphonia alga are presented.

An optical associative processor for two-dimensional image retrieval is described. The system employs variable channel-dependent nonlinearity in the weight plane of the conventional filter-based optical associative memory. Thresholding the filter that is used to store an associative image corresponds to focusing attention on that specific image by altering the strengths of the stored weights in the associative processor. For a severe compression type of nonlinearity, the pixel values of the high spatial frequencies of the filter are increased, which emphasizes the similarity between the input and the selected stored image.>

The authors present experiments on nonlinear joint transform optical correlation. This processor is joint power spectrum based and does not use any filters. In this technique both the input signal and the processing function are displayed side by side at the input plane of the processor. The Fourier transform interference intensity is thresholded to provide higher correlation peak intensity and better-defined correlation spot. Various types of correlation signals can be produced simply by varying the severity of the nonlinearity and without the need to synthesize the specific matched filter. Nonlinear correlation experimental results using both optically addressed SLM (spatial light modulator) and electrically addressed SLM are provided.>