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AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms

2026-04-30T15:00:00Z

Title: AI-driven digital holographic microscopy for label-free quantitative cellular analysis: toward low-cost and field-deployable platforms Author(s): Moon, Inkyu; Javidi, Bahram Abstract: Recent progress in artificial intelligence (AI) and digital holographic microscopy (DHM) has enabled quantitative, label-free, and noninvasive cellular imaging with unprecedented precision. This review provides an overview of AI-driven DHM technologies that transform classical holographic phase reconstruction and cellular analysis into real-time, portable biomedical solutions. After outlining the optical and computational fundamentals of DHM and quantitative phase imaging, we describe how deep generative and diffusion models substantially enhance phase retrieval accuracy under noisy or single-shot conditions. We then summarize recent biomedical applications, integrating blood, cancer, and cardiac cell analyses into a unified framework of AI-assisted quantitative phenotyping. Deep and self-supervised learning approaches are shown to enable high-accuracy classification of red blood cells and cancer cells and label-free evaluation of cardiomyocyte contractility and drug response. The combination of AI-based reconstruction, self-supervised learning, and physics-informed modeling demonstrates robust performance even with limited labeled data. Finally, we discuss the system-level transition toward low-cost, edge-AI-enabled DHM platforms capable of real-time phase imaging in point-of-care or field environments. We highlight key challenges in data standardization, interpretability, and multimodal integration. Collectively, this review envisions AI-integrated DHM as a scalable, accessible technology bridging advanced quantitative imaging with practical biomedical diagnostics. (c) 2026 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Scalable Neural Cryptanalysis of Block Ciphers in Federated Attack Environments

2025-12-31T15:00:00Z

Title: Scalable Neural Cryptanalysis of Block Ciphers in Federated Attack Environments Author(s): Jeong, Ongee; Park, Seonghwan; Moon, Inkyu Abstract: This paper presents an extended investigation into deep learning-based cryptanalysis of block ciphers by introducing and evaluating a multi-server attack environment. Building upon our prior work in centralized settings, we explore the practicality and scalability of deploying such attacks across multiple distributed edge servers. We assess the vulnerability of five representative block ciphers-DES, SDES, AES-128, SAES, and SPECK32/64-under two neural attack models: Encryption Emulation (EE) and Plaintext Recovery (PR), using both fully connected neural networks and Recurrent Neural Networks (RNNs) based on bidirectional Long Short-Term Memory (BiLSTM). Our experimental results show that the proposed federated learning-based cryptanalysis framework achieves performance nearly identical to that of centralized attacks, particularly for ciphers with low round complexity. Even as the number of edge servers increases to 32, the attack models maintain high accuracy in reduced-round settings. We validate our security assessments through formal statistical significance testing using two-tailed binomial tests with 99% confidence intervals. Additionally, our scalability analysis demonstrates that aggregation times remain negligible (<0.01% of total training time), confirming the computational efficiency of the federated framework. Overall, this work provides both a scalable cryptanalysis framework and valuable insights into the design of cryptographic algorithms that are resilient to distributed, deep learning-based threats.

Simple and practical single-shot digital holography based on unsupervised diffusion model

2025-12-31T15:00:00Z

Title: Simple and practical single-shot digital holography based on unsupervised diffusion model Author(s): Park, Seonghwan; Park, Jaewoo; Kim, Youhyun; Moon, Inkyu; Javidi, Bahram Abstract: 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. © 2025 Elsevier B.V., All rights reserved.

Roadmap on Optics and Photonics for Security and Encryption

2025-07-31T15:00:00Z

Title: Roadmap on Optics and Photonics for Security and Encryption Author(s): Javidi, Bahram; Carnicer, Artur; Ahmadi, Kavan; Awatsuji, Yasuhiro; Chen, Wen; Fournel, Thierry; Genevet, Patrice; Guo, Jingying; He, Wenqi; Hebert, Mathieu; Jana, Aloke; Lam, Edmund Y.; Long, Gui-Lu; Matoba, Osamu; Mi, Zhaoke; Moon, Inkyu; Nishchal, Naveen K.; Pan, Dong; Peng, Xiang; Pinkse, Pepijn W. H.; Shi, Yishi; Situ, Guohai; Stern, Adrian; Wang, Xiaogang; Xia, Tian; Xiao, Yin; Zhenwei, Xie; Zhu, Shuo Abstract: In 1994, Javidi and Horner published a paper in Optical Engineering that highlighted the ability of free space optical systems to manipulate sensitive data for authentication purposes. The underlying idea was effective yet surprisingly simple: an optical nonlinear joint transform using a random phase mask in both the input and the reference could produce a correlation peak to indicate whether the input object is authentic or not. This seminal paper fueled the development of this new discipline. After three decades, optical encryption and security have matured into a field that plays a central role in the development of photonics techniques. While the pioneering work was mainly focused on the field of optical information processing, nowadays, a broad spectrum of disciplines are contributing to developing security solutions, including nanotechnology, materials science, quantum information, and deep learning, just to cite a few. The present roadmap paper gathers 28 leading authors in the field from 21 academic institutions across nine different countries. It is organized into 17 sections which discuss the present and future challenges, state-of-the-art technology, and real-world solutions to address the security challenges facing our society.