Application of the associative recovery method in the challenges of increase informativity of distorted images and detection of minor changes in stored samples
DOI:
https://doi.org/10.15587/2706-5448.2022.266429Keywords:
holographic associative memory, hologram models, associative information recovery, physical modelingAbstract
The object of research is optical-electronic methods and related digital information processing. One of the most problematic areas is the reconstruction of missing parts of the stored data and the inability to detect minor changes in the stored samples, as well as the reconstruction of the entire corrected template from its incomplete version.
As part of the study, a correlation-optical approach to the problem of holographic associative memory was used, which made it possible to achieve highly efficient heteroassociative reconstruction of the entire corrected template from its incomplete version. The analysis of hologram models with phantom images and nonlinearly recorded holograms read in the associative mode shows a wide range of useful possibilities. It is primed not only in the tasks of reconstruction of data, but also in the case of insignificant changes in savings, highly effective heteroassociative reconstruction based on a non-interference mechanism. The analysis of the results of the correlation-optical approach to the problem of holographic associative memory shows that the described method opens up additional opportunities for solving the problems of detecting small changes in the object scene, which is important, in particular, for early registration of events and phenomena. It is related to the fact that the detection and localization of changes is carried out according to the difference in intensity across the image field (the effect of brightness inversion in the phantom image of referenceless hologram): the brightness of the image of the changed area is higher, and to a greater extent, the smaller the changes compared to the reference image. It should be especially noted that the specified properties of the nonlinear-holographic associative memory are realized not algorithmically, but physically, taking into account the fundamental nonlinearity of all natural processes, which is neglected when conducting a superficial (in the first approximation) analysis. Physical modeling of associative memory based on second-order holograms does not involve any circuit complications compared to the standard holographic procedure.
References
- Dai, B., Cao, Y., Wu, Z., Dai, Z., Yao, R., Xu, Y. (2021). Routing optimization meets Machine Intelligence: A perspective for the future network. Neurocomputing, 459, 44–58. doi: https://doi.org/10.1016/j.neucom.2021.06.093
- Wang, Q., Hao, Y. (2020). ALSTM: An attention-based long short-term memory framework for knowledge base reasoning. Neurocomputing, 399, 342–351. doi: https://doi.org/10.1016/j.neucom.2020.02.065
- Yang, R., Singh, S. K., Tavakkoli, M., Amiri, N., Yang, Y., Karami, M. A., Rai, R. (2020). CNN-LSTM deep learning architecture for computer vision-based modal frequency detection. Mechanical Systems and Signal Processing, 144, 106885. doi: https://doi.org/10.1016/j.ymssp.2020.106885
- Kakkava, E., Rahmani, B., Borhani, N., Teğin, U., Loterie, D., Konstantinou, G., Moser, C., Psaltis, D. (2019). Imaging through multimode fibers using deep learning: The effects of intensity versus holographic recording of the speckle pattern. Optical Fiber Technology, 52, 101985. doi: https://doi.org/10.1016/j.yofte.2019.101985
- Moura, A. L., Pereira, A. F., Canabarro, A., Carvalho, J. F., de Oliveira, I., dos Santos, P. V. (2020). Near-infrared holographic photorefractive recording under applied electric field in undoped Bi12TiO20 sillenite crystal. Optical Materials, 108, 110398. doi: https://doi.org/10.1016/j.optmat.2020.110398
- Haleem, A., Javaid, M., Singh, R. P., Suman, R., Rab, S. (2022). Holography and its applications for industry 4.0: An overview. Internet of Things and Cyber-Physical Systems, 2, 42–48. doi: https://doi.org/10.1016/j.iotcps.2022.05.004
- Ma, Y., Tresp, V., Daxberger, E. A. (2019). Embedding models for episodic knowledge graphs. Journal of Web Semantics, 59, 100490. doi: https://doi.org/10.1016/j.websem.2018.12.008
- Angelsky, O. V., Polyanskii, P. V., Felde, C. V. (2012). The Emerging Field of Correlation Optics. Optics and Photonics News, 23 (4), 25. doi: https://doi.org/10.1364/opn.23.4.000025
- Polyanskii, P. V. (1990). On matched filtering using reflection hologram. Ukrainian Journal of Physics, 35 (3), 353–356.
- Polyanskii, V. K. (1995). Holographic associative memories with a true brightness tone rendering. Optical Engineering, 34 (4), 1079–1087. doi: https://doi.org/10.1117/12.197162
- Owechko, Y. (1989). Nonlinear holographic associative memories. IEEE Journal of Quantum Electronics, 25 (3), 619–634. doi: https://doi.org/10.1109/3.18575
- Polyanskii, P. V. (1988). On conjugate imaging in generalized holographic filtering arrangement. Optics and Spectroscopy, 65 (2), 345–348.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Olena Husak
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
