Holographic optical correlator (HOC) is applicable in occasion where the instant search throughout a huge database is
demanded. The primary advantage of the HOC is its inherent parallel processing ability and large storage capacity. The
HOC’s searching speed is proportional to the storage density. This paper proposes a phase-encoding method in the object
beam to increase the storage density. A random phase plate (RPP) is used to encode the phase of the object beam before
uploading the data pages to the object beam. By shifting the RPP at a designed interval, the object beam is modulated
into an orthogonal object beam to the previous one and a new group of database can be stored. Experimental results
verify the proposed method. The maximum storage number of the data pages with a RPP at a fixed position can be as
large as 7,500. The crosstalk among different groups of the databases can be unnoticeable. The increase in the storage
density of the HOC depends on the number of the orthogonal positions from the different portions of a same RPP.
A hybrid digital-optical correlator (HDOC) based on volume holographic memory is able to compute the correlation of images at a high speed. HDOC is suitable for real-time image processing and has potential usage in big data processing areas. A 7500-channel HDOC system is experimentally set up, and the target image is correlated with all the channels. The large number of parallel correlation channels could contribute to the precise rotation measurement as well as the translation measurement. In the image recognition applications, the target image involves rotation distortion with respect to the template images. A method with two coarse-fine steps is proposed to measure the rotation at a full range of 360 deg. In the coarse step, the target image is rotated 36 times at an increment of 10 deg. The 36 new images are sent into the HDOC to compute with the template images. Each new image corresponds to a correlation matrix. By searching the smallest value throughout the 36 minimums of the 36 correlation matrixes, the rotation of the target image is narrowed into ±5 deg . In the fine step, the new image is rotated another 10 times at an increment of 1 deg. The rotation measurement error is <0.3 deg .
A search engine containing various target images or different part of a large scene area is of great use for many
applications, including object detection, biometric recognition, and image registration. The input image captured in realtime
is compared with all the template images in the search engine. A volume holographic correlator is one type of these
search engines. It performs thousands of comparisons among the images at a super high speed, with the correlation task
accomplishing mainly in optics. However, the inputted target image always contains scale variation to the filtering
template images. At the time, the correlation values cannot properly reflect the similarity of the images. It is essential to
estimate and eliminate the scale variation of the inputted target image. There are three domains for performing the scale
measurement, as spatial, spectral and time domains. Most methods dealing with the scale factor are based on the spatial
or the spectral domains. In this paper, a method with the time domain is proposed to measure the scale factor of the input
image. It is called a time-sequential scaled method. The method utilizes the relationship between the scale variation and
the correlation value of two images. It sends a few artificially scaled input images to compare with the template images.
The correlation value increases and decreases with the increasing of the scale factor at the intervals of 0.8~1 and 1~1.2,
respectively. The original scale of the input image can be measured by estimating the largest correlation value through
correlating the artificially scaled input image with the template images. The measurement range for the scale can be
0.8~4.8. Scale factor beyond 1.2 is measured by scaling the input image at the factor of 1/2, 1/3 and 1/4, correlating the
artificially scaled input image with the template images, and estimating the new corresponding scale factor inside
0.8~1.2.
Volume holographic correlator (VHC) calculates the inner product between two data pages through parallel optical
correlation. It has great potential in the field of information processing and real-time identification because of its high
storage density, integration of storing and computing, and multi-channel parallel processing ability. Current studies on
the improvements of VHC mainly focus on the processing speed and channel uniformity. However, the accuracy of the
VHC is mainly related to the minimum output intensity varying with the spatial light modulator (SLM) pixel intensity,
which is the sensitivity of the VHC. In this work, the Minimum Pixel Block Size (MPBS) is proposed to characterize the
sensitivity of the VHC. The Effective Number of Pixels (ENP) is employed to evaluate the optical computing ability,
which is more accurate compared with traditional calculating method based on the pixel number of the SLM. The
theoretical and experimental results are instructive in the system design. Desired system performance can be achieved by
optimizing the system parameters.
Volume holographic optical correlator can compute the correlation results between images at a super-high speed. In the application of remote imaging processing such as scene matching, 6,000 template images have been angularly multiplexed in the photorefractive crystal and the 6,000 parallel processing channels are achieved. In order to detect the correlation pattern of images precisely and distinguishingly, an on-off pixel inverted technology of images is proposed. It can fully use the CCD’s linear range for detection and expand the normalized correlation value differences as the target image rotates. Due to the natural characteristics of the remote sensing images, the statistical formulas between the rotation distortions and the correlation results can be estimated. The rotation distortion components can be estimated by curve fitting method with the data of correlation results. The intensities of the correlation spots are related to the distortion between the two images. The rotation distortion could be derived from the intensities in the post processing procedure. With 18 rotations of the input image and sending them into the volume holographic system, the detection of the rotation variation in the range of 180° can be fulfilled. So the large range rotation distortion detection is firstly realized. It offers a fast, large range rotation measurement method for image distortions.
A volume holographic correlator (VHC) can function as an optical processing unit (OPU). With its multi-channel
processing ability, the VHC is capable to extract inner products between the target image and all the stored remote
sensing images with high speed and high parallelism. An opto-electronic hybrid system based on the VHC for scene
matching is proposed. The innovative hybrid processing mode of the system combines the advantages of the high
parallelism in VHC with the high flexibility and accuracy of digital processing, improving the overall system
performances. The influences of the VHC’s unique multi-channel parallel processing ability on the system speed and
accuracy are theoretically studied in the context of different VHC working modes. The improvements of the system
adaptability for different situations, such as illumination conditions and noise, are also analyzed by numerical simulation.
Finally, experimental results are discussed to evaluate the system feasibility.
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