KEYWORDS: Data storage, Image segmentation, Holography, Deep learning, Holographic data storage systems, Neural networks, Education and training, Data modeling, Mathematical optimization, Holograms
Experiments have shown that deep learning can improve the data reading of holographic data storage. However, it requires a large amount of storage materials and time to obtain data to optimize the network model. In data encoding, each encoded data page consists of 51sub-pages with the same structure. This paper proposes a deep learning method for image segmentation based on encoding features in collinear holographic data storage. Using a deep learning method of image segmentation, the encoded data page is segmented into data sub-pages. It can reduce material loss and data collection time.
KEYWORDS: Data storage, Holography, Deep learning, Tunable filters, Phase retrieval, Education and training, Optical filters, Linear filtering, Data modeling, Signal to noise ratio
Holographic data storage is a powerful potential technology to solve the problem of mass data long-term storage. To increase the storage capacity, the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is necessary during data readout. In this talk, we propose a complex amplitude demodulation method based on deep learning from a single-shot diffraction intensity image and verified it by a non-interferometric lensless experiment demodulating four-level amplitude and four-level phase. By analyzing the correlation between the diffraction intensity features and the amplitude and phase encoding data pages, the inverse problem is decomposed into two backward operators denoted by two convolutional neural networks to demodulate amplitude and phase respectively. The stable and simple complex amplitude demodulation and strong anti-noise performance from the deep learning provide an important guarantee for the practicality of holographic data storage.
There are many ways to realize null reconstruction in polarization holography, which can be divided into two types. One is the null reconstruction without exposure response coefficient constraint, and the other is the null reconstruction limited by the exposure response coefficient. On the basis of previous studies, we have further studied these two types of null reconstruction, and obtained the necessary conditions for realizing the two types of null reconstruction under arbitrary interference angle and polarization state.
KEYWORDS: Deep learning, Crosstalk, Spatial light modulators, Phase retrieval, Phase reconstruction, Diffraction, Data storage, Near field diffraction, Image restoration, Photonics
In the holographic data storage system, we can use deep learning method to learn the relationship between phase patterns and their near-field diffraction intensity images. In the practice, pixel crosstalk always exists. We found the pixel crosstalk between adjacent variable phase pixels was benefit for quick and accurate phase retrieval based on deep learning. We validated our idea by the simulation of adding phase disturbance between pixels on the spatial light modulator.
Compared with traditional iterative methods, deep learning phase reconstruction has lower bit error rate and higher data transfer rate. We found the efficiency of training mainly was from the edges of the phase patterns due to their stronger intensity changes between adjacent phase distribution. According to this characteristic, we proposed a method to only record and use the high frequency component of the phase patterns and to do the deep learning training. This method can improve the storage density due to reducing the material consumption.
The phase retrieval method based on deep learning can be used to solve the iterative problem in holographic data storage. The key of the deep learning method is to build the relationship between the phase data pages and the corresponding near-field diffraction intensity patterns. However, to build the correct relationship, thousands of samples of the training dataset are usually required. In this paper, according to the coding characteristics of phase data pages, we proposed an image segmentation method to greatly reduce the number of original training dataset. The innovation proposed by this new method lies in the special segmentation of the original samples to expand the number of samples.
Polarization holography has gained traction with the development of tensor theory. It primarily focuses on the interaction between polarization waves and photosensitive materials. By introducing the polarization characteristics of light into conventional holography, more degrees of freedom can be provided to control optical information. Based on the polarization modulation of polarization hologram, we propose a method to realize bifocal-polarization holographic lens in volume hologram. Two foci can be generated simultaneously or separately by changing the polarization state of the reading wave. The material used is a PQ/PMMA polarization sensitive medium, the thickness is 1.5mm. The bifocal-polarization holographic lens has 112 mm clear aperture and 446mm focal length.
This paper proposes a complex amplitude demodulation method based on deep learning used in holographic data storage (HDS). To increase the storage capacity of a single data page in HDS, the complex amplitude of the object light can be used to encode the information data. However, the phase information of the complex amplitude cannot be detected directly. In this paper, we propose a non-interferometric complex amplitude retrieval method based on deep learning that can demodulate amplitude and phase simultaneously. A one-to-two convolutional neural network (CNN) is designed to establish the relationship between the intensity images captured by the detector and complex amplitude data pages. A simulation experiment is established to verify the feasibility of the proposed method.
Holographic data storage is one powerful potential technology to solve the problem of mass data long-term storage. Deep learning is showing its advantages in many fields such as artificial intelligence, detection and imaging. When deep learning meets holographic data storage, new modulation ways and decoding methods were born. We did three kinds of modulation amplitude only, phase only and complex amplitude respectively in holographic data storage and used deep learning method to do data reconstruction. The results were better than previous reconstruction methods. Data reconstruction based on deep learning owns more anti-noise performance.
A method for collinear non-interferometric phase retrieval holographic data storage using a single reference pixel is
proposed. The known embedded data of the signal beam in the traditional off-axis holographic data storage system is
placed in the reference beam through the collinear holographic data storage system, which greatly improves the material
utilization rate. And increasing the intensity of the reference beam can achieve phase retrieval using only one reference
pixel. As the intensity of the reference beam becomes stronger within a certain range, the number of iterations gradually
decreases. With this method, the phase retrieval can be achieved even when the total energy of the reference beam is less
than the signal beam. In the simulation, the four-level phase pattern was recorded and the phase was restored correctly.
The phase holographic storage system is different from the traditional object -image corresponding imaging. Because
of the particularity of phase, it is not easy to be captured by the traditional detector. Therefore, the Fourier lens is used
for Fourier transform to image it on the Fourier plane. The Fourier intensity is detected and the phase is recovered
iteratively by using the iterative Fourier transform algorithm. Due to the existence of aberrations, the wavefront phase
will be affected and the phase will be distorted.In this paper, we mainly study the influence of spherical aberration on
phase transformation. By establishing the light field with wavefront aberration, we study the influence of wavefront
aberration on phase recovery and propose the image restoration algorithm for aberration compensation .The feasibility
of the theory is proved.
KEYWORDS: Phase retrieval, Data storage, Holography, Fourier transforms, Computer programming, Solids, Data conversion, Reconstruction algorithms, Interferometry, Holographic data storage systems
Phase-modulation holographic data storage is imaging on the Fourier plane, and the imaging quality has a great influence on
phase retrieval. The iterative Fourier transform algorithm in the non-interference phase retrieval algorithm is widely used
because of its simple and stable system. By adding embedded data to the phase encoding method, the number of iterations can be
effectively reduced. However, the intensity of high-frequency information in Fourier intensity is weaker and more susceptible to
noise. To solve this problem, this paper proposes to use embedded data to improve the intensity of high-frequency information in
the Fourier intensity distribution, thereby improving noise immunity. In simulation, the convergence speed of BER (the bit error
rate) is faster under the same number of iterations.
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