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20 pages, 5461 KiB

Open AccessArticle

Design and Implementation of a 3D Korean Sign Language Learning System Using Pseudo-Hologram

by Naeun Kim, HaeYeong Choe, Sukwon Lee and Changgu Kang

Appl. Sci. 2025, 15(16), 8962; https://doi.org/10.3390/app15168962 - 14 Aug 2025

Sign language is a three-dimensional (3D) visual language that conveys meaning through hand positions, shapes, and movements. Traditional sign language education methods, such as textbooks and videos, often fail to capture the spatial characteristics of sign language, leading to limitations in learning accuracy and comprehension. To address this, we propose a 3D Korean Sign Language Learning System that leverages pseudo-hologram technology and hand gesture recognition using Leap Motion sensors. The proposed system provides learners with an immersive 3D learning experience by visualizing sign language gestures through pseudo-holographic displays. A Recurrent Neural Network (RNN) model, combined with Diffusion Convolutional Recurrent Neural Networks (DCRNNs) and ProbSparse Attention mechanisms, is used to recognize hand gestures from both hands in real-time. The system is implemented using a server–client architecture to ensure scalability and flexibility, allowing efficient updates to the gesture recognition model without modifying the client application. Experimental results show that the system enhances learners’ ability to accurately perform and comprehend sign language gestures. Additionally, a usability study demonstrated that 3D visualization significantly improves learning motivation and user engagement compared to traditional 2D learning methods. Full article

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16 pages, 4896 KiB

Open AccessArticle

Experimental Synthesis and Demonstration of the Twisted Laguerre–Gaussian Schell-Mode Beam

by Yuning Xia, Haiyun Wang, Lin Liu, Yahong Chen, Fei Wang and Yangjian Cai

Photonics 2023, 10(3), 314; https://doi.org/10.3390/photonics10030314 - 14 Mar 2023

Cited by 4 | Viewed by 2499

Abstract

The twisted Laguerre–Gaussian Schell-model (TLGSM) beam is a novel type of partially coherent beam embedded with both the second-order twist phase and the classical vortex phase. The intriguing properties induced by the interaction of the two types of phases have been demonstrated theoretically quite recently. In this work, we introduce a flexible way to experimentally synthesize a TLGSM beam with controllable twist strength. The protocol relies on the discrete pseudo-mode representation for the cross-spectral density of a TLGSM beam, in which the beam is viewed as an incoherent superposition of a finite number of spatially coherent modes. We show that all these pseudo modes endowed with random phases are mutually uncorrelated and can be encoded into a single frame of a dynamic computer-generated hologram. By sequentially displaying dynamic holograms on a single spatial-light modulator, the controllable TLGSM beam can be synthesized experimentally. The joint effect of the two phases on the propagation and self-reconstruction characteristics of the synthesized beam has also been studied in the experiment. Full article

(This article belongs to the Special Issue Advances and Application of Optical Manipulation)

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14 pages, 9926 KiB

Open AccessArticle

Enhancement of Imaging Quality of Interferenceless Coded Aperture Correlation Holography Based on Physics-Informed Deep Learning

by Rui Xiong, Xiangchao Zhang, Xinyang Ma, Lili Qi, Leheng Li and Xiangqian Jiang

Photonics 2022, 9(12), 967; https://doi.org/10.3390/photonics9120967 - 11 Dec 2022

Cited by 7 | Viewed by 2221

Abstract

Interferenceless coded aperture correlation holography (I-COACH) was recently introduced for recording incoherent holograms without two-wave interference. In I-COACH, the light radiated from an object is modulated by a pseudo-randomly-coded phase mask and recorded as a hologram by a digital camera without interfering with any other beams. The image reconstruction is conducted by correlating the object hologram with the point spread hologram. However, the image reconstructed by the conventional correlation algorithm suffers from serious background noise, which leads to poor imaging quality. In this work, via an effective combination of the speckle correlation and neural network, we propose a high-quality reconstruction strategy based on physics-informed deep learning. Specifically, this method takes the autocorrelation of the speckle image as the input of the network, and switches from establishing a direct mapping between the object and the image into a mapping between the autocorrelations of the two. This method improves the interpretability of neural networks through prior physics knowledge, thereby remedying the data dependence and computational cost. In addition, once a final model is obtained, the image reconstruction can be completed by one camera exposure. Experimental results demonstrate that the background noise can be effectively suppressed, and the resolution of the reconstructed images can be enhanced by three times. Full article

(This article belongs to the Special Issue Advances and Application of Imaging on Digital Holography)

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