Lee, D. B. Yoon, H. G. Park, S. M. Choi, Jun Woo Chen, G. Kwon, Hee Young Won, C. 2024-01-19T09:03:58Z 2024-01-19T09:03:58Z 2023-08-24 2023-07 2045-2322 https://pubs.kist.re.kr/handle/201004/113496 We construct a deep neural network to enhance the resolution of spin structure images formed by spontaneous symmetry breaking in the magnetic systems. Through the deep neural network, an image is expanded to a super-resolution image and reduced to the original image size to be fitted with the input feed image. The network does not require ground truth images in the training process. Therefore, it can be applied when low-resolution images are provided as training datasets, while high-resolution images are not obtainable due to the intrinsic limitation of microscope techniques. To show the usefulness of the network, we train the network with two types of simulated magnetic structure images; one is from self-organized maze patterns made of chiral magnetic structures, and the other is from magnetic domains separated by walls that are topological defects of the system. The network successfully generates high-resolution images highly correlated with the exact solutions in both cases. To investigate the effectiveness and the differences between datasets, we study the network's noise tolerance and compare the networks' reliabilities. The network is applied with experimental data obtained by magneto-optical Kerr effect microscopy and spin-polarized low-energy electron microscopy. English Nature Publishing Group Super-resolution of magnetic systems using deep learning Article 10.1038/s41598-023-38335-y 1 Scientific Reports, v.13, no.1 Scientific Reports 13 1 Y scie scopus 001032784200021 2-s2.0-85165218970 Multidisciplinary Sciences Science & Technology - Other Topics Article PHASE CNN