Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy
We discuss the development of a technique for automatic generation of databases of images obtained with a scanning tunneling microscope. An analysis of state-of-the-art methods and means of automatic processing of images obtained from probe and electron microscopes is carried out. We proposed using...
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| Format: | Article |
| Language: | English |
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Samara National Research University
2023-04-01
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| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | https://computeroptics.ru/eng/KO/Annot/KO47-2/470214e.html |
| _version_ | 1797363967908118528 |
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| author | T.E. Shelkovnikova S.F. Egorov P.V. Gulyaev |
| author_facet | T.E. Shelkovnikova S.F. Egorov P.V. Gulyaev |
| author_sort | T.E. Shelkovnikova |
| collection | DOAJ |
| description | We discuss the development of a technique for automatic generation of databases of images obtained with a scanning tunneling microscope. An analysis of state-of-the-art methods and means of automatic processing of images obtained from probe and electron microscopes is carried out. We proposed using generative-adversarial networks for generating images taken with a scanning tunneling microscope to form training databases of images. A process of training and comparison of deep convolutional generative adversarial network (DCGAN) architectures using the OpenCV and Keras libraries together with TensorFlow is described, with the best of them identified by computing the metrics IS, FID, and KID. The scaling of images obtained from DCGAN is performed using a method of fine tuning of a super-resolution generative adversarial neural network (SRGAN) and bilinear interpolation based on the Python programming language. An analysis of calculated quantitative metrics values shows that the best results of image generation are obtained using DCGAN96 and SRGAN. It is found that FID and KID metric values for SRGAN method are better than values for bilinear interpolation in all cases except for DCGAN32. All calculations are performed on a GTX GeForce 1070 video card. A method for automatic generation of a scanning tunneling microscope image database based on the stepwise application of DCGAN and SRGAN is developed. Results of generation and comparison of the original image, the one obtained with DCGAN96 and the enlarged image with SRGAN are presented. |
| first_indexed | 2024-03-08T16:28:36Z |
| format | Article |
| id | doaj.art-bda1624a9b664f3bb200328be053ff47 |
| institution | Directory Open Access Journal |
| issn | 0134-2452 2412-6179 |
| language | English |
| last_indexed | 2024-03-08T16:28:36Z |
| publishDate | 2023-04-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj.art-bda1624a9b664f3bb200328be053ff472024-01-06T14:40:57ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792023-04-0147231432210.18287/2412-6179-CO-1144Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopyT.E. Shelkovnikova0S.F. Egorov1P.V. Gulyaev2Institute of Mechanics, Udmurt Federal Research Center, Ural Branch, Russian Academy of SciencesInstitute of Mechanics, Udmurt Federal Research Center, Ural Branch, Russian Academy of SciencesInstitute of Mechanics, Udmurt Federal Research Center, Ural Branch, Russian Academy of SciencesWe discuss the development of a technique for automatic generation of databases of images obtained with a scanning tunneling microscope. An analysis of state-of-the-art methods and means of automatic processing of images obtained from probe and electron microscopes is carried out. We proposed using generative-adversarial networks for generating images taken with a scanning tunneling microscope to form training databases of images. A process of training and comparison of deep convolutional generative adversarial network (DCGAN) architectures using the OpenCV and Keras libraries together with TensorFlow is described, with the best of them identified by computing the metrics IS, FID, and KID. The scaling of images obtained from DCGAN is performed using a method of fine tuning of a super-resolution generative adversarial neural network (SRGAN) and bilinear interpolation based on the Python programming language. An analysis of calculated quantitative metrics values shows that the best results of image generation are obtained using DCGAN96 and SRGAN. It is found that FID and KID metric values for SRGAN method are better than values for bilinear interpolation in all cases except for DCGAN32. All calculations are performed on a GTX GeForce 1070 video card. A method for automatic generation of a scanning tunneling microscope image database based on the stepwise application of DCGAN and SRGAN is developed. Results of generation and comparison of the original image, the one obtained with DCGAN96 and the enlarged image with SRGAN are presented.https://computeroptics.ru/eng/KO/Annot/KO47-2/470214e.htmlstm-imagegenerative adversarial neural networksautomatic generation methoddatabaseconvolution |
| spellingShingle | T.E. Shelkovnikova S.F. Egorov P.V. Gulyaev Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy Компьютерная оптика stm-image generative adversarial neural networks automatic generation method database convolution |
| title | Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy |
| title_full | Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy |
| title_fullStr | Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy |
| title_full_unstemmed | Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy |
| title_short | Application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy |
| title_sort | application of generative adversarial neural networks for the formation of databases in scanning tunneling microscopy |
| topic | stm-image generative adversarial neural networks automatic generation method database convolution |
| url | https://computeroptics.ru/eng/KO/Annot/KO47-2/470214e.html |
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