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.

MOTIVATION: Skeletal muscle cells (skMCs) combine together to create long, multi-nucleated structures called myotubes. By studying the size, length, and number of nuclei in these myotubes, we can gain a deeper understanding of skeletal muscle development. However, human experimenters may often derive unreliable results owing to the unusual shape of the myotube, which causes significant measurement variability. RESULTS: We propose a new method for quantitative analysis of the dexamethasone side effect on human-derived young and aged skeletal muscle by simultaneous myotube and nuclei segmentation using deep learning combined with post-processing techniques. The deep learning model outputs myotube semantic segmentation, nuclei semantic segmentation, and nuclei center, and post-processing applies a watershed algorithm to accurately distinguish overlapped nuclei and identify myotube branches through skeletonization. To evaluate the performance of the model, the myotube diameter and the number of nuclei were calculated from the generated segmented images and compared with the results calculated by human experimenters. In particular, the proposed model produced outstanding outcomes when comparing human-derived primary young and aged skMCs treated with dexamethasone. The proposed standardized and consistent automated image segmentation system for myotubes is expected to help streamline the drug-development process for skeletal muscle diseases. AVAILABILITY AND IMPLEMENTATION: The code and the data are available at https://github.com/tdn02007/QA-skMCs-Seg.

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.

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.

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.

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 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.