{"title":"Deep Learning Ghost Polarimetry of Two-Dimensional Objects with Amplitude Anisotropy","authors":"D. A. Chernousov, D. P. Agapov","doi":"10.3103/S002713492570016X","DOIUrl":null,"url":null,"abstract":"<p>This paper discusses the potential of deep learning in solving the inverse problem of computational ghost polarimetry. For the first time, it is demonstrated that the spatial distribution of the polarization properties of objects with linear amplitude anisotropy can be restored using a neural network trained on model data. The spatial distribution of the parameters of linear amplitude anisotropy is determined with an accuracy of 7.8<span>\\(\\%\\)</span> and 15.6<span>\\(\\%\\)</span> for the azimuth and the value of anisotropy, respectively.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"80 1","pages":"112 - 118"},"PeriodicalIF":0.4000,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Moscow University Physics Bulletin","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.3103/S002713492570016X","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
This paper discusses the potential of deep learning in solving the inverse problem of computational ghost polarimetry. For the first time, it is demonstrated that the spatial distribution of the polarization properties of objects with linear amplitude anisotropy can be restored using a neural network trained on model data. The spatial distribution of the parameters of linear amplitude anisotropy is determined with an accuracy of 7.8\(\%\) and 15.6\(\%\) for the azimuth and the value of anisotropy, respectively.
期刊介绍:
Moscow University Physics Bulletin publishes original papers (reviews, articles, and brief communications) in the following fields of experimental and theoretical physics: theoretical and mathematical physics; physics of nuclei and elementary particles; radiophysics, electronics, acoustics; optics and spectroscopy; laser physics; condensed matter physics; chemical physics, physical kinetics, and plasma physics; biophysics and medical physics; astronomy, astrophysics, and cosmology; physics of the Earth’s, atmosphere, and hydrosphere.