https://scholar.dgist.ac.kr/handle/20.500.11750/6122 2026-04-04T13:58:16Z https://scholar.dgist.ac.kr/handle/20.500.11750/59951 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. 2025-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59941 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. 2025-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59921 Title: A Holographic Sensor-Integrated Deep Learning Framework for Noninvasive Assessment of Stored Red Blood Cell Quality Author(s): Park, Seonghwan; An, Hyunbin; Rehman, Abdur; Moon, Inkyu Abstract: Prolonged storage of red blood cells (RBCs) induces morphological degradation that can compromise transfusion efficacy. Traditional quality assessment methods are often labor-intensive and time-consuming, limiting their utility in real-time settings. Although deep learning has been applied to RBC imaging, most approaches require large datasets and complex architectures, making them impractical for efficient deployment. This study introduces a holographic sensor-integrated deep learning framework for noninvasive RBC quality assessment using small datasets. A diffusion model is employed to synthetically generate phase images and segmentation masks, augmenting limited data. Self-supervised learning with pre-trained models further enhances classification performance while maintaining a streamlined model architecture. Compared to conventional segmentation methods, the proposed framework achieves higher accuracy and significantly faster inference. It also enables reliable detection of storage-induced morphological changes, providing proportional indicators of transfusion viability. Experimental results validate its effectiveness as a practical tool for real-time, sensor-driven monitoring of RBC quality. 2025-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/59919 Title: Privacy-preserving image captioning using virtual photon-limited imaging and federated learning Author(s): Martin, Antoinette Deborah; Moon, Inkyu Abstract: The growing demand for visual privacy in optical imaging systems has motivated the development of frameworks that can both preserve privacy and maintain utility in downstream tasks. In this work, we propose a privacy-preserving image captioning framework that integrates Poisson Multinomial Distribution-based Photon Counting Imaging (PMD-PCI) with deep learning techniques. PMD-PCI simulates photon-limited imaging conditions by generating highly sparse multispectral images, thereby inherently concealing fine visual details. These sparse representations are used as inputs to two encoder-decoder architectures, ResNet101-Transformer and ViT-Transformer, for automated caption generation. To further enhance privacy and reduce the risk of centralized data exposure, we employ federated learning, allowing model training across distributed clients without direct access to raw images. Experimental evaluations on the Flickr8k and Flickr30k datasets show that accurate captions can be generated from photon-limited images with more than 50,000 incident photons at a resolution of 224 × 224 pixels. On Flickr30k, the proposed ResNet101-Transformer achieves a BLEU-4 score of 17.32 and a CIDEr score of 27.80 at 50,000 photons in a centralized setting, demonstrating that meaningful captions can be produced even under severe optical sparsification. Compared to traditional encryption-based techniques such as Double Random Phase Encoding (DRPE) and AES, our approach provides a better trade-off between privacy and captioning performance. Furthermore, convergence analysis reveals that federated learning achieves near-optimal performance within just 3 communication rounds, significantly reducing the communication overhead required for training. The proposed framework bridges physical-layer image privacy with optical imaging and learning-based caption generation, making it suitable for secure, low-light, or resource-constrained vision systems. © 2026 The Authors. 2026-01-31T15:00:00Z