Polarization holography is a newly researched field, that has gained traction with the development of tensor theory. It primarily focuses on the interaction between polarization waves and photosensitive materials. The extraordinary capabilities in modulating the amplitude, phase, and polarization of light have resulted in several new applications, such as holographic data storage technology, polarization multiplexing, vector or vortex beams, and optical functional devices. In this paper, fundamental research on polarization holography with linear polarized light, a component of the theory of polarization holography, has been reviewed. Primarily, the effect of various polarization changes on the linear and nonlinear polarization characteristics of reconstructed light wave under continuous exposure and during holographic recording and reconstruction have been focused upon. The polarization modulation realized using these polarization characteristics exhibits unusual functionalities, rendering polarization holography as an attractive research topic in many fields of applications.
The old theory of polarization holography is based on Jones matrix formalism, where the angle between two lights to be interfered each other should be small, and the results are limited under the paraxial approximation. However, since the tensor theory of polarization holography was proposed, the research of polarized holography has become hot, and has made a lot of new progress. There are also many researching works of reconstruction characteristics have been reported. One of the examples is that multi-channel recording was applied to data storage high density recording. In this paper, the representative works are introduced.
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.
Polarization holography can record and reconstruct information by means of polarization state modulation. It can record the amplitude, phase and polarization information of light field by recording the polarization grating of two coherent waves with different polarization states. This technology has been used to record two or four pairs of 2-level grayscale images in the case of two or four channels in the past. Now we designed experiments to record and reconstruct two different 4-level grayscale images at the same point of polarization-sensitive media through the faithful reconstruction of linearly polarization holography by matching the exposure time and the polarization angles of the two interference waves. We verify that it is possible to reconstruct two 4-level intensity images at the same point to achieve polarization and intensity multiplexing.
In Big Data era, holographic data storage has become a good candidate recording technology, because of there are not only large storage capacities, but also high transfer rates. However, the realized capacity of it has a big gap to the theory. Polarization holography, a newly researched field, with the extraordinary capabilities in modulating the amplitude, phase, and polarization of light have resulted in several new applications, such as holographic storage technology, multichannel polarization multiplexing, vector beams, and optical functional devices. In this paper, the fundamental research on polarization holography with linear polarized light, a component of the theory of polarization holography, has been introduced. The polarization modulation realized using these polarization characteristics exhibits unusual functionalities, rendering polarization holography as an attractive research topic in a novel method for increasing the capacity of holographic data storage has been provided.
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.
In this letter, we employ vector wave polarization holography theory based on the dielectric tensor description. Newly developed vector wave polarized holography theory breaks up the limitation of paraxial approximation in polarization holograms. Various interesting phenomena have been investigated, the faithful reconstruction is of particular significance. The faithful reconstruction (FR) effect indicates that the polarization state of the reconstruction wave is identical to that of the signal wave, it can be achieved process when the intensity and polarization holographic grating attained a balance during after exposure. The FR property related to the linearly, circularly and elliptically polarization is investigated in our previous work. In our experiment, the recording medium we use is the bulk polarization holographic recording material of phenanthrenequinone-doped polymethyl methacrylate photopolymer (PQ-PMMA). The mixed mass ratio of methyl methacrylate (MMA), azobisisobutyronitrile (AIBN) and phenanthrenequinone (PQ) are 100:1:1. Under the cross-angle of π/2 inside the recording media, the polarized holographic reconstruction of the circular polarization recorded by a horizontal linear polarization wave is calculated. It is found that the circularly polarized signal can be faithful reconstruction by arbitrarily polarized reading waves. However, when the polarization of the reading wave is orthogonal to the polarization of the reference wave, it will occur the null reconstruction (NR). The FR technology will provide a simpler and more effective method for a circular polarization generator. At the same time, the NR technology can quickly detect that the polarized wave is vertical polarization.
Phase retrieval is the key technique in phase-modulated holographic storage. In this paper, a deep convolutional neural network is proposed to directly retrieve phase data. Compared with the traditional non-interferometric phase retrieval method, this method has the advantages of fast retrieval speed and high reconstruction accuracy. In this paper, the influence of intensity image noise on retrieval results under different retrieved conditions is researched and analyzed. By establishing a simulation system that is in strict agreement with real experiments, the lensless spatial diffraction images are generated. By adding different proportions of random noise into the intensity images we get the training dataset. The convolutional neural network is trained by a training dataset and tested by a new noisy test dataset. Experimental results show that the phase retrieval method based on deep learning has a high tolerance for systematic errors and strong anti-noise performance.
In this paper, a phase retrieval method based on deep learning is proposed and applied to the phase-modulated holographic storage system. The phase-modulated holographic storage system has become a research hotspot because of its higher encoding rate and higher signal-to-noise ratio (SNR). Since the phase data cannot be detected directly by the detector, the intensity image is used to retrieve the phase. The traditional interferometric phase retrieval method is not suitable for the storage system because its optical system is complex and is easily affected by environmental disturbances. The non-interferometric phase modulation storage system uses iterative methods to solve the phase data, and the number of iterations will affect the data transmission rate in the holographic data system. In this paper, a simulated non-interferometric phase retrieval system based on deep learning is established, which uses a convolutional neural network to directly establish the relationship between phase and intensity images captured by CCD. The neural network is trained by learning the dataset of intensity images and phase data images. After training, the phase can be obtained by a single calculation, which greatly improves the data transmission speed. In the process of deep learning training, we introduced embedded data to improve the precision of phase reconstruction and reduce the bit error rate. According to our investigation, this is the first application of deep learning in phase retrieval of optical holographic storage.
The single-shot iterative Fourier transform algorithm as a common non-interferometric phase retrieval algorithm is very suitable for phase-modulated holographic data storage due to its fast, simple and stable properties. It retrieves the phase in the object domain iteratively from the intensity image in the Fourier domain captured by the detector. Because of the effects by complex noises of the experimental system, there is always an intensity image degradation which increases the phase decoding bit error rate. This paper proposed a denoising method based on end-to-end convolutional neural networks by learning the relationship between the captured intensity images and the simulation results to improve image quality significantly. Then the denoised intensity image was used in the phase retrieval. The experiment results showed that the bit error rate can be reduced by 6.7 times using the denoised image, which proved the feasibility of the neural network denoising method in the phase-modulated holographic data storage system.
A single-shot non-interferometric phase retrieval method in holographic data storage is proposed to solve the problems that undetectability for phase by detector directly and unstability caused by interferometric detection. Embedded data are inserted in iterative Fourier transform algorithm to shorten iterations sharply. For avoiding embedded data occupying the code rate, we propose a collinear system to refer to the reference beam, which is always known, as the embedded data. Finally, fast stable phase information reading is realized because of single-shot non-interferometric detection and fast phase retrieval within only several iterations.
We present single-shot fast phase information retrieval without interferometry in the holographic data storage. Noninterferometry systems are more compact and stable than interferometric ones. Only single-shot of the intensity distribution on the Fourier plane is required to retrieve the phase information. Enhanced iterative Fourier transform algorithm (IFTA) was developed by applying embedded known phase data and phase only modulation as the prior constraints, which can be provided easily as the code rule in holographic data storage system. Strong intensity distribution on the Fourier plane reduces the requirement of high-power laser and high material diffractive efficiency. The bit-errorrate (BER) can be decreased to 0 in the simulation study. We realized BER without check code in the order of 10-2 for 4 level phase retrieval experimentally. The code rate is increased by 2.8 times using 4 level phase code compared to with amplitude code.
A non-interferometric phase retrieval method in collinear holographic data storage (HDS) is proposed. Noninterferometric system is stable which is suitable for phase-modulated HDS but non-interferometric phase retrieval algorithm replies on strong constraint to shorten iteration number. Embedded data can provide strong constraint. However, in off-axis system, embedded data have to be in the signal part which sacrifice code rate. Our proposed collinear system considers the reference beam as embedded data to increase the code rate by about 2 times.
In this paper, we propose a frequency expanded method based on non-interferometric phase retrieval which can retrieve complex multi-level phase image by using only 1 times Nyquist frequency. Our proposed method utilizes the property of frequency spectrum periodicity and is the unique method with non-interferometry due to the intensity detection directly on the Fourier domain. For a regular phase image, same spacial frequency means same spectrum width. We choose a rectangular window with the same spacial frequency to the phase image and consider normalized Fourier intensity distribution of the rectangular window as the envelope of that of the phase image. After normalizing the spectrum of the phase image, we can expand its Fourier frequency with 1 times Nyquist size to other higher order frequency positions. Therefore, we can generate high-order frequencies artificially from low-order frequency which help us to retrieve phase image accurately and quickly.
Non-interferometric phase retrieval is a fundamental technique for phase-modulated holographic data storage due to its advantages of easy implementation, simple system setup, and robust noise tolerance. Usually, the iterative algorithm of non-interferometry needs hundreds of iteration numbers to retrieve phase accurately, which decreased the data transfer rate severely. Strong constraint conditions, such as embedded data, can be used on the phase data page to reduce the iteration numbers. However, introducing embedded data will reduce the code rate of the system. We proposed a method that combined the single-shot interferometric method with the non-interferometric iterative Fourier transform algorithm method. We used the phase decoding result by single-shot interferometry as the embedded data in the process of non-interferometry. Therefore, no extra embedded data are needed in the signal code. We realized the code rate improvement as well as keeping fast data transfer rate. In the demonstration, we recorded a four-level phase pattern and retrieved the phase correctly. The bit error rate of phase retrieval is less than 1% within 20 iterations, which proves our approach is practical. In our case, the code rate is increased by two times.
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