Thesis Details

Rekonstrukce řídce vzorkovaného obrazu pomocí hlubokého učení

Master's Thesis Student: Le Hoang Anh Academic Year: 2020/2021 Supervisor: Juránek Roman, Ing., Ph.D.
English title
Reconstruction of Sparse Sampled Images with Deep Learning
Language
Czech
Abstract

The main goal of this thesis was to increase reconstruction quality of sparse sampled microscopic images by using neural networks. The thesis will cover various approaches for image reconstruction and will also include descriptions of implementations, which were used. Implementations will be evaluated based on quality of reconstruction, but also based on segmentation, which could be their main possible application. 

Keywords

Neural network, GAN, U-Net, image reconstruction, segmentation, machine learning

Department
Department of Computer Graphics and Multimedia FIT BUT
Degree Programme
Information Technology and Artificial Intelligence, Specialization Application Development
Files
Status
defended, grade C
Date
23 June 2021
Reviewer
Hradiš Michal, Ing., Ph.D.
Committee
Zemčík Pavel, prof. Dr. Ing. (DCGM FIT BUT), předseda
Bartík Vladimír, Ing., Ph.D. (DIFS FIT BUT), člen
Holík Lukáš, doc. Mgr., Ph.D. (DITS FIT BUT), člen
Kreslíková Jitka, doc. RNDr., CSc. (DIFS FIT BUT), člen
Orság Filip, Ing., Ph.D. (DITS FIT BUT), člen
Polčák Libor, Ing., Ph.D. (DIFS FIT BUT), člen
Citation
LE, Hoang. Rekonstrukce řídce vzorkovaného obrazu pomocí hlubokého učení. Brno, 2021. Master's Thesis. Brno University of Technology, Faculty of Information Technology. 2021-06-23. Supervised by Juránek Roman. Available from: https://www.fit.vut.cz/study/thesis/23287/
BibTeX 
@mastersthesis{FITMT23287,
    author = "Anh Hoang Le",
    type = "Master's thesis",
    title = "Rekonstrukce \v{r}\'{i}dce vzorkovan\'{e}ho obrazu pomoc\'{i} hlubok\'{e}ho u\v{c}en\'{i}",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2021,
    location = "Brno, CZ",
    language = "czech",
    url = "https://www.fit.vut.cz/study/thesis/23287/"
}

Theses

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