Search Results (43)

Neural networks are a state-of-the-art technology for fast and accurate holographic image reconstruction. However, at present, neural network-based reconstruction methods are predominantly applied to objects with simple, homogeneous spatial structures: blood cells, bacteria, microparticles in solutions, etc. However, in the case of objects with high contrast details, the reconstruction needs to be as precise as possible to successfully extract details and parameters. In this paper we investigate the use of neural networks in holographic reconstruction of spatially inhomogeneous binary data containers (QR codes). Two modified lightweight convolutional neural networks (which we named HoloLightNet and HoloLightNet-Mini) with an encoder–decoder architecture have been used for image reconstruction. These neural networks enable high-quality reconstruction, guaranteeing the successful decoding of QR codes (both in demonstrated numerical and optical experiments). In addition, they perform reconstruction two orders of magnitude faster than more traditional architectures. In optical experiments with a liquid crystal spatial light modulator, the obtained bit error rate was equal to only 1.2%. These methods can be used for practical applications such as high-density data transmission in coherent systems, development of reliable digital information storage and memory techniques, secure optical information encryption and retrieval, and real-time precise reconstruction of complex objects. Full article

Lithium niobate (LiNbO₃), a multifunctional crystalline material, has critical importance in advancing holographic storage systems. However, persistent challenges such as optical damage, limited diffraction efficiency, and slow response kinetics hinder its practical implementation. This work systematically examines the correlation between the Zn²⁺ dopant concentration and the defect architecture, photodamage resistance, and holographic storage properties of Zn:Cu:Fe:LiNbO₃ crystals, employing advanced characterization techniques to elucidate structure–property relationships and optimize performance metrics. The experimental data reveal a pronounced Zn²⁺ doping concentration dependence in both photodamage resistance and holographic storage capabilities. Notably, Zn:Cu:Fe:LiNbO₃ crystals doped with 7 mol% Zn²⁺ achieve a substantial 416-fold improvement in photodamage resistance (786.55 J/cm²) relative to the 1 mol% doped variant. Concurrently, these optimally doped crystals demonstrate superior holographic storage performance, characterized by a response time of 196.4 s, a dynamic range of 9.81, a diffraction efficiency of 66.7%, and a sensitivity of 1.04. The observed performance enhancement is fundamentally attributed to Zn²⁺ doping, which concomitantly suppresses intrinsic defect formation and tailors the spatial distribution of Fe³⁺/Cu²⁺ photorefractive centers within the crystal lattice. These mechanistic insights establish critical guidelines for the rational design of next-generation holographic storage materials with optimized photorefractive response and defect engineering capabilities. Full article

In the big data era, data are created in huge volume. This leads to the development of storage devices. Many technologies are proposed for the next generation of storage fields. However, among them, holographic data storage (HDS) has attracted much attention and has been introduced as the promising candidate to meet the increasing demand for capacity and speed. For signal processing, HDS faces two major challenges: inter-page interference (IPI) and two-dimensional (2D) interference. To access the IPI problem, we can use balanced coding, which converts user data into an intensity level with uniformly distributed values for each page. For 2D interference, we can use the equalizer and detection to mitigate the 2D interference. However, the often-used equalizer and detection are methods in wireless communication and only handle the one-dimensional (1D) signal. Thus, we can combine the equalizer, detection, and estimator to reduce 2D interference into 1D interference. In this paper, we proposed a combined model using serial maximum a posteriori (MAP) detection and estimator to improve the detection of HDS systems. In our proposed model, instead of using an estimator with the Viterbi algorithm to predict the upper–lower interference (UPI) or left–right interference (LRI) and converting the received signal into 1D ISI, we used the estimator to predict the extrinsic information for serial MAP detection. This preserves the 2D information in the received signal in serial MAP detection and improves the detection of serial MAP detection by extrinsic information. The simulation results demonstrate that our proposed model significantly improves the bit-error rate (BER) performance compared to previous studies. Full article

In the past, comprehensive information was imperative for image processing, prompting a preference for high-depth cameras. However, in our research, we discovered that the abundance of image details may impede phase retrieval. Consequently, this paper presents an iterative phase retrieval method based on a low bit depth. Through simulations and experiments, this approach has proven effective in evidently enhancing phase retrieval outcomes. Furthermore, the concept of low bit depth holds promise for broader application across diverse domains within the field of image retrieval. Full article

Intensity-modulated signals have the advantage of being directly detectable by the image sensor but have the drawback that the signal quality is easily deteriorated by crosstalk noise, in contrast to phase-modulated signals. In order to suppress the crosstalk noise, we propose a new signal arrangement for multilevel intensity-modulated signals. The concept of our method is to reduce the number of adjacent pixels that are a source of inter-pixel crosstalk noise and to minimize intensity modulation owing to interference with crosstalk noise. We have numerically and experimentally demonstrated that our method can reduce the error rate and improve the recording density compared to the conventional signal arrangement. Our proposed method offers a promising solution for achieving higher recording densities in intensity-modulated holographic data storage systems. Full article

Phase unwrapping is a technique used to recover the original phase from the wrapped phase in the range $(-\pi ,\pi ]$. Various methods have been proposed for phase unwrapping. In particular, methods using convolutional neural networks (CNNs) have been extensively researched because of their high robustness against noise and fast inference speed. However, conventional CNN-based methods discard the local position information and relationships between pixels in the convolution process, resulting in poor phase-unwrapping performance. To obtain better phase unwrapping results, we propose a module that combines a global convolution network, which applies convolutional layers with a kernel size equivalent to that of the feature maps, and CoordConv, which acquires the positional relationships between pixels. We validated the performance of the proposed method by comparing it with a quality-guided path algorithm and deep learning-based phase unwrapping methods and found that the proposed method is highly robust against noise. Full article

Holographic memory offers high-capacity optical storage with rapid data readout and long-term durability. Recently, read data pages have been classified using digital deep neural networks (DNNs). This approach is highly accurate, but the prediction time hinders the data readout throughput. This study presents a diffractive DNN (D2NN)-based classifier for holographic memory. D2NNs have so far attracted a great deal of attention for object identification and image transformation at the speed of light. A D2NN, consisting of trainable diffractive layers and devoid of electronic devices, facilitates high-speed data readout. Furthermore, we numerically investigated the classification performance of a D2NN-based classifier. The classification accuracy of the D2NN was 99.7% on 4-bit symbols, exceeding that of the hard decision method. Full article

Polarized holographic properties play an important role in the holographic data storage of traditional organic recording materials. In this study, reduced graphene oxide (RGO) was introduced into a phenanthraquinone-doped polymethylmethacrylate (PQ/PMMA) photopolymer to effectively improve the orthogonal polarization holographic properties of the material. Importantly, the lateral size of RGO nanosheets has an important influence on the polymerization of MMA monomers. To some extent, a larger RGO diameter is more conducive to promoting the polymerization of MMA monomers and can induce more PMMA polymers to be grafted on its surface, thus obtaining a higher PMMA molecular weight. However, too large of a RGO will lead to too much grafting of the PMMA chain to shorten the length of a single PMMA chain, which will lead to the degradation of PQ/PMMA holographic performance. Compared with the original PQ/PMMA, the diffraction efficiency of the RGO-doped PQ/PMMA photopolymer can reach more than 11.4% (more than 3.5 times higher than the original PQ/PMMA), and its photosensitivity is significantly improved by 4.6 times. This study successfully synthesized RGO-doped PQ/PMMA high-performance photopolymer functional materials for multi-dimensional holographic storage by introducing RGO nanoparticles. Furthermore, the polarization holographic properties of PQ/PMMA photopolymer materials can be further accurately improved to a new level. Full article

Central data systems require mass storage systems for big data from many fields and devices. Several technologies have been proposed to meet this demand. Holographic data storage (HDS) is at the forefront of data storage innovation and exploits the extraordinary characteristics of light to encode and retrieve two-dimensional (2D) data from holographic volume media. Nevertheless, a formidable challenge exists in the form of 2D interference that is a by-product of hologram dispersion during data retrieval and is a substantial barrier to the reliability and efficiency of HDS systems. To solve these problems, an equalizer and target are applied to HDS systems. However, in previous studies, the equalizer acted only as a linear convolution filter for the received signal. In this study, we propose a nonlinear equalizer using a convolutional neural network (CNN) for HDS systems. Using a CNN-based equalizer, the received signal can be nonlinearly converted into the desired signal with higher accuracy. In the experiments, our proposed model achieved a gain of approximately 2.5 dB in contrast to conventional models. Full article

To enhance the holographic properties, one of the main methods is increasing the solubility of the photosensitizer and modifying the components to improve the modulation of the refractive index in the photopolymer. This study provides evidence, through the introduction of a mutual diffusion model, that the incorporation of SiO₂ nanoparticles in photopolymers can effectively enhance the degree of refractive index modulation, consequently achieving the objective of improving the holographic performance of the materials. Different concentrations of SiO₂ nanoparticles have been introduced into highly soluble photosensitizer Irgacure 784 (solubility up to 10wt%)-doped poly-methyl methacrylate (Irgacure 784/PMMA) photopolymers. Holographic measurement experiments have been performed on the prepared samples, and the experiments have demonstrated that the Irgacure 784/PMMA photopolymer doped with 1.0 $\times {10}^{-3}$wt% SiO₂ nanoparticles exhibits the highest diffraction efficiency (74.5%), representing an approximate 30% increase in diffraction efficiency as compared to an undoped photopolymer. Finally, we have successfully achieved the recording of real objects on SiO₂/Irgacure 784/PMMA photopolymers, demonstrated by the SiO₂/Irgacure 784/PMMA photopolymer material prepared in this study, which exhibits promising characteristics for holographic storage applications. The strategy of doping nanoparticles (Nps) in Irgacure 784/PMMA photopolymers has also provided a new approach for achieving high-capacity holographic storage in the future. Full article

Previous studies have shown that orthogonal phase-coding multiplexing performs well with low crosstalk in conventional off-axis systems. However, noticeable crosstalk occurs when applying the orthogonal phase-coding multiplexing to collinear holographic data storage systems. This paper demonstrates the crosstalk generation mechanism, features, and elimination methods. The crosstalk is caused by an inconsistency in the intensity reconstruction from the orthogonal phase-coded reference wave. The intensity fluctuation range was approximately 40%. Moreover, the more concentrated the distribution of pixels with the same phase key, the more pronounced the crosstalk. We propose an effective random orthogonal phase-coding reference wave method to reduce the crosstalk. The orthogonal phase-coded reference wave is randomly distributed over the entire reference wave. These disordered orthogonal phase-coded reference waves achieve consistent reconstruction intensities exhibiting the desired low-crosstalk storage effect. The average correlation coefficient between pages decreased by 73%, and the similarity decreased by 85%. This orthogonal phase-coding multiplexing method can be applied to encrypted holographic data storage. The low-crosstalk nature of this technique will make the encryption system more secure. Full article

Holographic data storage (HDS) utilizes the unique properties of light for writing and reading two-dimensional (2D) data from holographic media, providing significantly higher densities and faster data transfer rates than traditional storage media for short-term dependencies. With its ability to store terabytes of data in a single crystal, HDS has garnered attention as a promising candidate for next-generation storage technologies. However, the 2D interference caused by hologram dispersion during the reading process poses a significant obstacle to achieving reliable and efficient HDS systems. This study proposes a method for enhancing the accuracy of estimating the 2D intersymbol interference (ISI) using a recurrent neural network (RNN) equalizer for HDS systems. The proposed method leverages the ability of RNNs to model complex and temporal dependencies in data and more accurately estimate the interference caused by ISI and interchannel interference (ICI) in HDS systems. In addition, to recreate the relationship between the samples in the training process, RNN is applied to fields such as computer vision, natural language process, speech recognition, and so on. We evaluated the performance of our proposed method on a simulation model of HDS system and compared it with the previous studies. In the simulations, the proposed method outperformed the previous schemes in terms of bit error rate, indicating its potential for improving the reliability and efficiency of HDS systems. Full article

We report a single-shot phase-detection method using deep learning in a holographic data-storage system. The error rate was experimentally confirmed to be reduced by up to three orders of magnitude compared with that in the conventional phase-determination algorithm by learning the light-intensity distribution around a target signal pixel. In addition, the output speed of a signal phase could be shortened by devising a network and arranging the fully connected layers in parallel. In our environment, the phase-output time of a single-pixel classification was approximately 18 times longer than that in our previous method, with the minimum-finding algorithm. However, it could be reduced to 1.7 times or less when 32 pixels were simultaneously classified. Therefore, the proposed method can significantly reduce the error rates and suppress the phase-output time to almost the same level as that in the previous method. Thus, our proposed method can be a promising phase-detection method for realizing a large-density data-storage system. Full article

Holographic data storage (HDS) has emerged as a promising technology for high-capacity data storage. In this study, we propose a novel approach to enhance the storage density in HDS through a multiplexing perfect optical vortex (POV) hologram. By utilizing the orthogonality property of POV, different POV-recording holograms can be multiplexed to store multiple data pages within the single hologram. Compared with the conventional optical vortex, the better storage density of POV through proof-of-principle experiments is demonstrated. For the POV-multiplexing hologram of six data pages, each one can be reconstructed successfully. In addition, we investigate the impact of axicon periods and multiplexing numbers on the storage performance. Our results reveal that an appropriate selection of axicon periods and multiplexing numbers is crucial to balance storage density and bit error rate (BER). The proposed multiplexing approach offers a valuable solution for achieving high-density and secure holographic data storage systems. Full article

Holographic data storage (HDS) is an emerging technology that promises to revolutionize digital data storage and access. Unlike traditional storage media such as hard drives and flash memory, HDS uses light to write and read page-oriented two-dimensional (2D) data from volume media. This allows for significantly higher densities and faster data transfer rates in HDS systems. However, 2D interference is a serious issue in HDS due to hologram dispersion during the reading process. Therefore, we present a novel detection algorithm based on maximum a posteriori (MAP) detection to mitigate 2D interference. In our proposed model, we inherited the structure of the serial generalized partial response target to design the serial structure for MAP detection. The simulation results show that the proposed model can achieve a high bit error rate performance. Full article