Search Results (566)

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

With the advent of the big data era, traditional storage technologies struggle to meet the demands for long-term, secure, and cost-effective preservation of massive amounts of information. Collinear holographic storage technology has emerged as a strong contender for next-generation optical storage due to its high storage density, rapid parallel transmission, and exceptional reliability. Among various storage materials, phenanthraquinone-doped poly(methyl methacrylate) (PQ/PMMA) photopolymer has garnered significant attention for its negligible photo-induced volume shrinkage, low cost, controllable thickness, and polarization-sensitive holographic response properties. However, the material’s limited photosensitivity, low polarization response, and poor optical uniformity severely constrain its application in high-speed recording and multidimensional multiplexing holographic systems. This paper reviews the primary methods and strategies employed over the past five years to enhance the holographic performance of PQ/PMMA photopolymer materials, based on the microscopic physicochemical mechanisms underlying traditional and polarization holography, including chemical modification, nanoscale doping, mechanical control, etc. Through a systematic review of these research advances, this paper aims to provide theoretical foundations and technical references for developing high-performance PQ/PMMA photopolymer materials suitable for collinear holographic storage. Full article

In the far-field approximation, an object’s diffraction field can be expressed as its Fourier transform multiplied by a phase factor. Here, we present a simple method with which to directly retrieve this phase factor from a single-shot off-axis interference pattern. By exploiting and adjusting its unique two-dimensional quadratic form, the quadratic contribution from the object’s Fourier transform can generally be neglected, particularly for amplitude-only objects and slowly varying phase objects. The phase factor is extracted by fitting a quadratic surface to the unwrapped phase obtained via Fourier-transform-based phase retrieval. Removing this factor enables precise reconstruction through a straightforward inverse Fourier transform, without requiring iterative computations. Compared with conventional far-field diffraction setups, our approach reduces system length and allows the use of smaller CCD sensors. Experimental validation using a modified Mach–Zehnder interferometer demonstrates high reconstruction accuracy and robustness. Overall, this method provides an efficient, practical, and real-time solution for object reconstruction, with the potential to simplify and miniaturize optical setups, offering an alternative approach to standard coherent diffraction imaging techniques. Full article

We propose a field-transformation-based framework for generating phase-only light-field holograms from a single RGB image. The method establishes an explicit pipeline from monocular scene inference to holographic wavefront synthesis, without requiring multi-view capture or task-specific hologram-network training. First, we construct a layered occlusion RGB-D model from the input image using monocular depth estimation, connectivity-based layer decomposition, and occlusion-aware inpainting, which provides a lightweight 3D prior for sparse-view rendering in the small-parallax regime. Second, we transform the rendered sparse RGB-D light field into a target complex wavefront on the recording plane through local frequency mapping, thereby bridging explicit scene geometry and wave-optical field construction. Third, we optimize the phase-only hologram under multi-plane amplitude constraints using a geometrically consistent initial phase and an error-driven adaptive depth-sampling strategy, which improves convergence stability and reconstruction quality under a limited computational budget. Numerical experiments show that the proposed method achieves better depth continuity, occlusion fidelity, and lower speckle noise than representative layer-based and point-based methods, and improves the average PSNR and SSIM by approximately 3 dB and 0.15, respectively, over Hogel-Free Holography. Optical experiments further confirm the physical feasibility and robustness of the proposed framework. Full article

Semiclassical gravity admits a vast set of candidate black hole interior states, raising the question of which of these correspond to independent quantum microstates that account for black hole entropy. In this review, we survey several explicit constructions of black hole interior microstates in AdS₂ holography and AdS/CFT and assess whether they furnish bases of the black hole Hilbert space. We further highlight the settings in which non-perturbative effects in the gravitational path integral, captured by spacetime wormholes, resolve the resulting overcounting and reproduce the black hole entropy from state counting. Full article

Millimeter-wave and sub-millimeter-wave techniques are widely used in non-destructive testing of multilayered materials due to their ability to penetrate non-conductive media and resolve dielectric stratifications. However, conventional thickness estimation methods suffer from an inherent resolution limit dictated by the available frequency bandwidth. In this paper, a MUSIC-based approach is proposed to achieve super-resolution localization of echoes in the reflective response of the structure under test. The method exploits the sparsity of the reflective response, similarly to compressive sensing approaches, while providing improved reconstruction accuracy. Moreover, the proposed strategy enables the retrieval of dielectric permittivities and layer thicknesses without resorting to complex nonlinear fitting procedures. Finally, the method operates on magnitude-only data, with phase information recovered through an interferometric holographic technique, making the proposed framework well-suited for cost-effective industrial applications. Full article

Multidisciplinary technologies are truly driving major transformations in industries, innovating to become more efficient. The need for more efficient renewable energy systems, such as solar energy, has recently been addressed with the innovation of using holographic photonic devices to avoid solar tracking devices as much as possible. In this work, a multiplexed holographic device is created and characterized for use in front of a photocell, thereby eliminating the need for tracking systems due to its wide acceptance angle and high diffraction efficiency. Commercial Bayfol^® HX121 photopolymer was used as the holographic recording material to manufacture holograms, achieving high performance and facilitating the industrial scaling of this technique. Results obtained using the multiplexing technique enable low-frequency holograms (478 lines/mm) with a 43° acceptance angle. Using three of these devices, a 129° angular sweep is possible without the need for tracking. Full article

Coherent noise in digital holographic microscopy (DHM) seriously degrades the accuracy of quantitative phase imaging, limiting its applications in fields such as nondestructive testing. However, traditional numerical denoising methods struggle to achieve an ideal balance between noise suppression, detail preservation, and computational efficiency. To address this challenge, we propose a multi-scale attention efficient network (MAENet). This network employs a dual-encoder architecture to achieve complementary extraction of multi-scale features. To efficiently integrate the features from these two branches, a dual-branch dense attention fusion (DDAF) module is designed. It performs a weighted fusion of features from the dual branches via an adaptive attention mechanism and enhances feature representation via dense residual connections, significantly boosting the model’s denoising performance. Furthermore, a hierarchical fusion strategy is adopted to preserve high-frequency details in the shallow layers of the network while performing feature fusion in the deeper layers, thereby maximizing protection of image textures while effectively suppressing noise. To address the lack of paired training data in real-world scenarios, a DHM simulation system capable of simulating the key physical characteristics of coherent noise was constructed. Extensive experiments on the simulated dataset show that MAENet achieves a PSNR of 33.25 dB and an SSIM of 0.93042, outperforming various mainstream denoising algorithms and demonstrating its excellent performance in suppressing coherent noise, providing an effective solution for denoising in coherent imaging systems. Full article

How does the complex, adaptive, and autonomous organization of life emerge from the laws of physics and information? This review argues that the answer lies in a convergent set of universal organizational principles that constitute a physical and informational grammar of the living. Living systems are dissipative structures that achieve organizational closure—materially and energetically open, yet causally closed—thereby attaining genuine autonomy and agency. Their architecture exhibits fractal and modular scaling laws that maximize energy flow, robustness, and evolvability under universal physical constraints. Critically, organisms operate at critical transitions—zones of controlled instability where fluctuations amplify information processing, transforming noise into adaptive signal. This self-organized criticality enables functional degeneracy, relational redundancy, and evolutionary antifragility. Cognition emerges as a distributed process of active inference, operating through a predictive–corrective cycle that integrates perception, action, and learning under the Free Energy Principle. From molecular networks to ecosystems, the same physico-informational grammars unfold recursively, revealing a deep organizational holography: the principles of organization are replicated across scales. Evolution under the Law of Increasing Functional Information is not random drift, but a directional expansion of functional complexity—a thermodynamic gradient towards greater agency. This synthesis challenges biological exceptionalism: the trajectory from thermodynamics to cognition is continuous, physically constrained, and potentially inevitable. Life does not violate physical laws—it fulfills them in regimes of high informational complexity, instantiating fundamental principles in self-organized architectures capable of prediction, memory, and purpose. The objective of this work is to articulate how the synthesis of these principles not only unifies physics and biology, but also illuminates the profound continuity between thermodynamics, chemistry, informational constraints, organization, and the mind. Full article

Ocean turbulence, a fundamental process influencing marine hydrodynamics, holds significant guiding implications for the development of multiple disciplines and has emerged as a research hotspot in ocean science in recent years. However, constrained by traditional oceanographic instruments limited to single-point measurements, current observations and analyses of oceanic turbulence still experience considerable shortcomings. To advance oceanic turbulence observations beyond single-point measurements toward comprehensive three-dimensional (3D) field characterization, this study introduces an innovative Holographic Astigmatic Particle Tracking Velocimetry (HAPTV) technology combined with an integrated in situ underwater measurement and processing system. For the first time, this system has successfully acquired 3D two-component (u, v components) ocean flow fields in natural environments. The measured flow velocities reach up to 15 cm/s, with turbulence dissipation rates on the order of 10⁻⁴ m²/s³, which is consistent with the hydrodynamic conditions in coastal marine environments. These results demonstrate the feasibility of using HAPTV for field-scale turbulence observations, offering a novel volumetric alternative to conventional single-point techniques. Nevertheless, due to factors such as excessively high concentrations of suspended matter in nearshore waters and z-axis positioning limitations, the accuracy of the flow field results obtained from the initial sea trials still needs to be improved. Full article

In this paper, a metasurface-enabled dual-channel optical image authentication based on polarization multiplexing is proposed. During encryption, authentication phases corresponding to dual-channel plaintext images are firstly calculated by using a sparse-constraint-driven authentication-holography (SCDAH) algorithm. Then, target transmission phase and geometric phase of metasurface to be designed are obtained accordingly by the composite phase modulation (CPM) principle. Next, the nanopillar-type metasurface unit is performed with parameter scanning to establish the transmission and geometric phase databases. Finally, the structural parameters of each nanopillar are determined on a pixel-by-pixel basis to complete the construction of polarization-multiplexing authentication metasurface (PMAM). During authentication, the PMAM are respectively illuminated by the left-handed circularly polarized (LCP) and right-handed circularly polarized (RCP) light to obtain pseudo-random images produced by far-field diffraction, and then the nonlinear correlation distribution between diffraction image and corresponding channel plaintext image is calculated, and the final authentication result of each channel is determined based on whether the signal-to-noise ratio of the nonlinear correlation distribution meets the standard. In fact, a new physical-characteristic-driven dual-channel optical image authentication technology is formed, where double identities of the user holding this PMAM can be simultaneously verified, breaking through the rigid constraint of conventional single metasurface-to-single image, meanwhile improving the capacity and efficiency for authentication metasurface from the perspective of physical mechanism. Numerical simulations are performed to demonstrate the feasibility of the proposed method, and the simulation results prove that the proposed method exhibits high feasibility and security as well as strong robustness against cropping attack, showing a promising application potential in the field of identity recognition and authentication. Full article

Multi-plane computer-generated holography is a key technology for enabling volumetric and near-eye displays. However, its widespread adoption remains constrained by the high computational cost of phase optimization and the persistent issue of axial crosstalk between depth planes. In this work, we propose a physics-informed deep learning framework that directly generates holograms for 3D multi-plane displays. Our approach implements a learnable mapping from spatial distributions to depth-dependent reconstructions and incorporates a trainable Fourier transform layer, enabling end-to-end optimization entirely in the physical domain (i.e., from the hologram plane to the multi-plane reconstruction). As a result, hologram generation time is decreased significantly, while effectively suppressing crosstalk across axial planes. Experimental validation demonstrates that the obtained phase hologram successfully reconstructs sparse multi-plane structured patterns with low visible crosstalk. These results highlight the potential of deep learning to advance practical applications in dynamic 3D display and holographic optical tweezer technologies. Full article

Phase unwrapping is a critical step in interferometric imaging modalities such as holography and synthetic aperture radar, yet conventional analytical algorithms struggle in low signal-to-noise and high-speckle environments. This study presents an artificial intelligence (AI)-based phase-unwrapping framework using a Pix2Pix conditional generative adversarial network (cGAN). A model was designed for robustness under Rayleigh-distributed speckle noise and phase decorrelation, conditions representative of realistic interferometric measurements. Trained on synthetically generated wrapped–unwrapped phase pairs, the AI approach was compared against established analytical phase-unwrapping methods, a quality-guided unwrapping algorithm (Herraez)and a minimum-norm network-flow optimization method (Costantini). Quantitative evaluation using the root mean square error (RMSE), structural similarity index measure (SSIM), and a composite performance index demonstrated that the cGAN was superior under noisy conditions, successfully recovering phase information beyond its training noise range at $\sigma =10$, and accurately unwrapping phases up to $\sigma =20$. This was under a pure unwrapping performance analysis, utility performance was also tested comparing all images to clean noiseless phase. The Pix2Pix model also proved resilient to detector artifacts, despite not being explicitly trained on them, and its worst performance yielded RMSE and SSIM values of 0.089 and 0.927, respectively, with perfect values being 0 and 1. The proposed framework simultaneously unwraps and denoises the phase, offering a simple, open-source, and highly adaptable alternative for phase unwrapping in noisy interferometric systems. Future work will focus on extending the framework to experimental datasets. Full article

While conventional Bayesian compressive sensing exploits signal sparsity for accurate sound field reconstruction from under-sampled measurements, its practicality is limited by high computational complexity and slow convergence. To address these limitations, this paper proposes a variational Bayesian compressive sensing framework integrated with equivalent source modeling for sound field reconstruction. The approach first establishes a sparse representation of the sound field using the equivalent source method, and then assigns hierarchical prior distributions to the equivalent source strengths and the noise precision within this Bayesian model. Mean-field variational inference is adopted to derive an analytically tractable approximation to the true posterior distribution by minimizing the Kullback–Leibler divergence, thus enabling efficient estimation of the equivalent source strengths and subsequent high-accuracy sound field reconstruction. This proposed method retains the desirable statistical advantages of Bayesian modeling while enhancing computational efficiency. Numerical simulations and experiments validate that the proposed method achieves superior reconstruction accuracy compared with conventional Bayesian compressive sensing and orthogonal matching pursuit algorithm, with significantly reduced computational burden and enhanced robustness in low signal-to-noise ratio scenarios. Full article

In adaptive optics systems, high spatial resolution detection is a core prerequisite for achieving accurate wavefront correction. High spatial resolution wavefront measurement based on the traditional Shack-Hartmann technique is limited by the density of the microlens array. In contrast, off-axis digital holography technology is applied in wavefront measurement systems of adaptive optics systems due to its advantages of high spatial resolution, non-contact measurement, and full-field measurement. However, during the demodulation of its interference fringes, the accurate extraction of the complex amplitude of the +1st-order diffraction order directly determines the precision of wavefront reconstruction. Traditional frequency-domain filtering methods suffer from drawbacks such as reliance on manual threshold setting, poor adaptability to irregular spectra, and localization deviations caused by multi-region interference, making it difficult to meet the dynamic application requirements of adaptive optics. To address these issues, this study proposes a spectrum extraction method based on the Canny operator for adaptive edge extraction and centroid localization. The method first locks the rough range of the +1st-order spectrum through multi-stage peak screening, then achieves complete segmentation of spectrum spots by combining adaptive histogram equalization with edge closing and filling, resolves centroid indexing errors via maximum connected component screening, and ultimately accomplishes accurate extraction through Gaussian window filtering. Simulation experimental results show that, in comparison with two classical spectrum filtering methods, the centroid estimation error of the proposed method remains below 0.245 pixels under different noise intensity conditions. Moreover, the root mean square error of the residual wavefront corresponding to the reconstructed wavefront of the proposed method is reduced by 89.0% and 87.2% compared with those of the two classical methods, respectively. We further carried out measurement experiments based on a self-developed atmospheric turbulence test bench. The experimental results demonstrate that the proposed method exhibits higher-precision spectral centroid localization capability, which provides a reliable technical support for the high-precision measurement of dynamic distortion induced by atmospheric turbulence. Full article