Search Results (142)

Digital holography is a powerful imaging technique for quantitative analysis in various fields of science and technology, including biomedical applications. However, the development and validation of neural network-based methods in this field are often limited by the unavailability of large, well-annotated experimental data. For example, in biological studies, the typical size of an experimental dataset is around 1000 images. In this work, we present a procedural framework for generating synthetic datasets of biological cell phase images and digital holograms. The generated datasets were tested in several key tasks of neural network applications, including cell classification, detection, and phase reconstruction from in-line holograms. High classification accuracy is achieved for both phase images and holograms, while phase reconstruction reaches high structural similarity indices. The applicability of the generated datasets is further demonstrated in complex scenarios involving multiple moving cells and multiple object planes. The proposed approach provides a flexible and scalable platform for method development, performance evaluation, and future integration with experimental holographic imaging systems. Full article

Accurate axial localization of microparticles is a key requirement in in-line digital holography (ILDH), particularly under noisy conditions and for weakly scattered objects. This work presents experimental and simulated benchmarking of three widely used focus metrics: maximum intensity, complex amplitude, and Kurtosis. Experimental holograms of microparticles with different diameters were recorded using a compact ILDH system, while simulated holograms of a 10 µm particle were generated. Numerical reconstruction was performed using a Fresnel convolution approach with FFT-based propagation over a range of axial distances. The performance of each focus metric was evaluated based on peak definition, robustness to coherent noise, and consistency across particle sizes and configurations. The results show that both maximum intensity and Kurtosis provide consistent and reliable axial localization, with very similar behavior across all cases. In contrast, the complex amplitude metric is more sensitive to noise and exhibits larger fluctuations in the axial response. These results indicate that simple intensity-based metrics can achieve accurate localization under moderate signal-to-noise conditions, while higher-order statistical metrics improve robustness in more challenging scenarios. This work provides practical guidelines for selecting autofocus criteria in ILDH systems for particle imaging and holographic metrology. Full article

Continuous in situ marine holographic observation generates data volumes that challenge onboard storage and transmission. Parallel phase-shifting digital holography (PPSDH) is especially sensitive to compression because phase retrieval depends on consistent four-channel demodulation. We present a training-free spatiotemporal compression framework for sparse-particle marine PPSDH sequences based on background–residual decomposition and a shared four-channel processing path. The background is coded once per temporal window by a discrete wavelet transform (DWT) followed by principal component analysis (PCA), and the dynamic residual is decorrelated by temporal principal component analysis before quantization and entropy coding. The framework is evaluated on three primary 64-frame marine PPSDH sequences using a common reconstruction-and-evaluation pipeline with wrapped-phase root-mean-square error (PhaseRMSE) as the primary metric and amplitude peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as secondary references; expanded supplementary checks are also reported for nine additional selected 64-frame groups spanning sparse to transitional occupancy. On the primary sequence and within the high-fidelity achieved-rate overlap with the JPEG Pleno anchor codec INTERFERE, the proposed framework reduces PhaseRMSE by about 3.3-fold to 3.4-fold while increasing amplitude PSNR by about 11 dB and preserving amplitude SSIM above 0.99997. Lower-bitrate sweeps further quantify the rate–fidelity trade-off rather than claiming universal low-rate superiority. These results support BG–Res spatiotemporal coding as a practical phase-fidelity-oriented option for the tested sparse-to-transitional marine PPSDH conditions; extension to dense scenes, broader marine conditions, and downstream biological tasks requires separate validation. Full article

To develop innovative solutions to urban challenges via a participatory design involving collaboration among all stakeholders—government authorities, designers, businesses, and local communities—technological applications that facilitate interactions must be integrated from the earliest stages of urban development projects. However, such collaboration is often hampered by the lack of effective platforms for communication and exchange, particularly given the difficulty of achieving consensus between experts and non-experts. From another perspective, hologram technology remains relatively underexplored and underutilized in the fields of architecture and urban design despite being a highly promising immersive technology. This study applied hologram technology and computer vision to create an interactive digital platform that enhances participatory design among stakeholders, which is called hologram walk. The proposed system was implemented on a university campus in Vinh Long, Vietnam, for its renovation project. This technology-driven platform allows users to experience and interact with the design proposal, communicate their opinions and needs, and enables the design team to make responsive adjustments. This proposed platform optimizes collaboration, fosters innovative designs outcomes, and can be easily replicated in future architectural and urban projects. Full article

Lensless digital holography provides a simple, low-cost imaging platform with a large field of view (FOV) and quantitative phase capability, making it attractive for biomedical imaging, microstructure inspection, and large area imaging. However, the achievable FOV is still limited by sensor size, and in-line reconstruction suffers from twin-image artifacts that degrade image quality. To overcome these limitations, this study proposes an extended FOV lensless digital holography method that combines hologram stitching with multi-depth phase retrieval. Multiple holograms acquired from laterally shifted FOVs are stitched to form an extended hologram, while holograms recorded at multiple axial depths are used to suppress twin-image artifacts and improve reconstruction fidelity. Experimental results show that the proposed method effectively expands the imaging area, enhances effective resolution by integrating complementary diffraction information from different FOVs, and improves image contrast and feature visibility. This approach enables extended FOV, resolution enhancement, and high-quality holographic imaging while preserving the simple lensless digital holography architecture. Full article

Mixed-scene holographic 3D display for film and television visual content presentation remains challenging because recorded digital holograms and computer-generated holograms (CGHs) are produced under different numerical and hardware constraints. Direct hologram superposition typically causes strong zero-order interference, diffraction efficiency degradation, and sampling pitch mismatch between the recording sensor and the replay panel, while conventional resizing reduces the effective replay aperture and narrows the available parallax. To address these issues, this paper proposes a zero-order-suppressed single-hologram fusion framework with parallax-preserving digital resizing. A recorded digital hologram is first processed by Gaussian high-pass filtering to suppress the dominant zero-order component, then resampled to match the LCOS replay pitch, and finally normalized and fused with a CGH generated through bipolar intensity encoding. On this basis, two resizing routes are developed: a spatial-domain method for aperture-preserving whole-scene scaling and a frequency-domain method for object-selective scaling and translation. Optical validation on a three-channel LCOS prototype shows that the quantitative diffraction efficiency analysis predicts an increase from approximately 10.1% to 20.05% per reconstructed object for the two-hologram fusion case, and the revised experimental results are consistent with this improvement trend. The experiments further verify replay scaling at multiple factors, the selective manipulation of physical and virtual objects, mixed-scene color replay, and occlusion-consistent depth ordering. Together with the distortion analysis, these results demonstrate improved replay visibility after fusion while maintaining geometric controllability and effective replay aperture. By relying on hologram-domain preprocessing and resizing rather than full mixed-scene recomputation, the proposed method also reduces computational burden. The study therefore provides an efficient and controllable mixed-scene holographic replay framework for visually enriched film and television content presentation, although its depth applicability remains bounded and dedicated real-time timing benchmarks are left for future work. Full article

High-speed realization of computer-generated holograms (CGHs) is a crucial problem in the field of modern 3D visualization and optical image processing system development. Binary CGHs can be realized using high-resolution, high-speed spatial light modulators such as ferroelectric liquid crystals on silicon devices or digital micro-mirror devices providing the high throughput of optoelectronic systems. However, the quality of holographic images restored by binary CGHs often suffers from distortions, background noise, and speckle noise caused by the limitations and imperfections of optical system components. The present manuscript introduces a method based on the optimization of CGH models directly in the optical system with a camera-in-the-loop configuration using effective direct search with a random trajectory algorithm. The method was experimentally verified. The results demonstrate a significant enhancement in the quality of the holographic images optically restored by binary-phase CGH models optimized through this method compared to purely digitally generated models. Full article

This study considers the applicability and effectiveness of the Viola–Jones method to automatically distinguish zooplankton particles from the background in images reconstructed from digital holograms obtained in natural conditions. For the first time, this algorithm is applied to holographic images containing coherent noise and residual defocusing. The method was trained on 880 annotated (marked) holographic images of Daphnia magna along with 120 background frames. It was then tested on independent laboratory and field datasets, including morphologically related taxa. With optimized settings, the precision of the algorithm reached ~90% and F1~85% on noisy holographic images, and the algorithm also demonstrated the preliminary ability to recognize similar taxa without retraining. The algorithm is well suited for analyzing holographic data as a fast and resource-efficient pre-filter—it effectively separates particles from the background and thereby allows subsequent classification or its application in real-time aquatic environment monitoring systems. The article presents experimental results demonstrating the efficiency of this algorithm during plankton monitoring in situ. Full article

Digital holographic microscopes provide a microscopy solution with a resolution in the low-micrometre range that offers similar performance to optical microscopy, but as a relatively low-cost alternative. The most significant cost saving is due to the ability to reconstruct microscopic images from holograms using low-cost components without the need for an optical stack. The cost saving opens up the avenue towards a feasible solution for geographically distributed in situ microscopic sensing in rural areas for problems like air and water pollution monitoring. The most significant contributors to cost are the camera sensor module, the pinhole, and the processing platform. The latter two components are addressed, at least in part, in this work. We successfully manufactured sub-100 $\mu$m diameter pinholes using ultraviolet (UV) laser cutting with an LPKF printed circuit board (PCB) prototyping platform and present the low-cost micromachining method. The pinholes were utilised within a prototype field-programmable gate array (FPGA) demonstrator that successfully reconstructed the holographic images. The choice for the FPGA approach as the initial step, albeit more complex, lends itself towards the easier development of a dedicated reconstructed application-specific integrated circuit (ASIC) to ultimately drive the cost down even further. Full article

Digital holography enables the reconstruction of both 2D and 3D object information from interference patterns captured by digital cameras. A major challenge in this field is speckle noise, which significantly degrades the quality of the reconstructed images. We propose a novel speckle noise reduction method based on 3D adaptive filtering. Our technique processes a stack of holograms, each with an uncorrelated speckle pattern, using an adapted 3D Frost filter. Unlike conventional filtering techniques, our approach exploits statistical adaptivity to enhance noise suppression while preserving fine image details in the reconstructed holograms. Both numerical simulations and optical experiments confirm that our 3D filtering technique significantly enhances reconstruction quality. Specifically, it reduces the normalized standard deviation by up to 40% and improves the structural similarity index by up to 60% compared to classical 2D, 3D median, BM3D, and BM4D filters. Optical experiments validate the method’s effectiveness in practical digital holography scenarios by local and global image quality estimation metrics. These results highlight adaptive 3D filtering as a promising approach for mitigating speckle noise while maintaining structural integrity in digital holography reconstructions. Full article

We present a deep learning-based approach to optimize the central angle between adjacent camera viewpoints for the efficient generation of natural 360-degree holographic 3D content. High-quality 360-degree digital holograms require the acquisition of densely sampled RGB–depth map pairs, a process that traditionally requires significant computational costs. Our method introduces a novel pipeline that systematically evaluates the impact of varying central angles—defined as the angular separation between equidistant viewpoints in an object-centered coordinate system—on both depth map estimation and holographic 3D image reconstruction. By systematically applying this pipeline, we determine the optimal central angle that achieves an effective balance between image quality and computational efficiency. Experimental investigations demonstrate that our approach significantly reduces computational demands while maintaining superior fidelity of the reconstructed 3D holographic images. The relationship between central angle selection and the resulting quality of 360-degree digital holographic 3D content is thoroughly analyzed, providing practical guidelines for the creation of immersive holographic video experiences. This work establishes a quantitative standard for the geometric configuration of viewpoint sampling in object-centered environments and advances the practical realization of real-time, high-quality holographic 3D content. Full article

Digital holographic microscopy (DHM) provides numerous advantages, such as noninvasive sample analysis, real-time dynamic detection, and three-dimensional (3D) reconstruction, making it a valuable tool in fields such as biomedical research, cell mechanics, and environmental monitoring. To achieve more accurate and comprehensive imaging, it is crucial to capture detailed information on the microstructure and 3D morphology of samples. Phase processing of holograms is essential for recovering phase information, thus making it a core component of DHM. Traditional phase processing techniques often face challenges, such as low accuracy, limited robustness, and poor generalization. Recently, with the ongoing advancements in deep learning, addressing phase processing challenges in DHM has become a key research focus. This paper provides an overview of the principles behind DHM and the characteristics of each phase processing step. It offers a thorough analysis of the progress and challenges of deep learning methods in areas such as phase retrieval, filtering, phase unwrapping, and distortion compensation. The paper concludes by exploring trends, such as ultrafast 3D holographic reconstruction, high-throughput holographic data analysis, multimodal data fusion, and precise quantitative phase analysis. Full article

In this paper, a security-enhanced three-dimensional (3D) image hiding and encryption method is proposed by combining a layer-based phase-only hologram (POH) under structured light illumination with chaotic encryption and digital image watermarking technology. In the proposed method, the original 3D plaintext image is firstly encoded into a layer-based POH and then further encrypted into an encrypted phase with the help of a chaotic random phase mask (CRPM). Subsequently, the encrypted phase is embedded into a visible ciphertext image by using a digital image watermarking technology based on discrete wavelet transform (DWT) and singular value decomposition (SVD), leading to a 3D image hiding with high security and concealment. The encoding of POH and the utilization of CRPM can substantially enhance the level of security, and the DWT-SVD-based digital image watermarking can effectively hide the information of the 3D plaintext image in a visible ciphertext image, thus improving the imperceptibility of valid information. It is worth noting that the adopted structured light during the POH encoding possesses many optical parameters, which are all served as the supplementary keys, bringing about a great expansion of key space; meanwhile, the sensitivities of the wavelength key and singular matrix keys are also substantially enhanced thanks to the introduction of structured light, contributing to a significant enhancement of security. Numerical simulations are performed to demonstrate the feasibility of the proposed 3D image hiding method, and the simulation results show that the proposed method exhibits high feasibility and apparent security-enhanced effect as well as strong robustness. Full article

Against the backdrop of the rapid advancement of intelligent speech interaction and holographic display technologies, this paper introduces an interactive holographic display system. This paper applies 2D-to-3D technology to acquisition work and uses a Complex-valued Convolutional Neural Network Point Cloud Gridding (CCNN-PCG) algorithm to generate a computer-generated hologram (CGH) with depth information for application in point cloud data. During digital human hologram building, 2D-to-3D conversion yields high-precision point cloud data. The system uses ChatGLM for natural language processing and emotion-adaptive responses, enabling multi-turn voice dialogs and text-driven model generation. The CCNN-PCG algorithm reduces computational complexity and improves display quality. Simulations and experiments show that CCNN-PCG enhances reconstruction quality and speeds up computation by over 2.2 times. This research provides a theoretical framework and practical technology for holographic interactive systems, applicable in virtual assistants, educational displays, and other fields. Full article

Digital holography can capture and reconstruct 3D object information, making it valuable for biomedical imaging and materials science. However, traditional holographic reconstruction methods require the use of phase shift operation in the time or space domain combined with complex computational processes, which, to some extent, limits the range of application areas. The integration of deep learning (DL) advancements with physics-informed methodologies has opened new avenues for tackling this challenge. However, most of the existing DL-based holographic reconstruction methods have high model complexity. In this study, we first design a lightweight model with fewer parameters through the synergy of deep separable convolution and Swish activation function and then employ it as a teacher to distill a smaller student model. By reducing the number of network layers and utilizing knowledge distillation to improve the performance of a simple model, high-quality holographic reconstruction is achieved with only one hologram, greatly reducing the number of parameters in the network model. This distilled lightweight method cuts computational expenses dramatically, with its parameter count representing just 5.4% of the conventional Unet-based method, thereby facilitating efficient holographic reconstruction in settings with limited resources. Full article