PID3Net: a deep learning approach for single-shot coherent X-ray diffraction imaging of dynamic phenomena

Vu, TS; Ha, MQ; Bachtiar, AM; Dao, DA; Tran, Truyen; Kino, H; Takazawa, S; Ishiguro, N; Sasaki, Y; Abe, M; Uematsu, H; Okawa, N; Ozaki, K; Kobayashi, K; Honjo, Y; Nishino, H; Joti, Y; Hatsui, T; Takahashi, Y; Dam, HC

PID3Net: a deep learning approach for single-shot coherent X-ray diffraction imaging of dynamic phenomena

journal contribution

posted on 2025-04-02, 00:43 authored by TS Vu, MQ Ha, AM Bachtiar, DA Dao, Truyen TranTruyen Tran, H Kino, S Takazawa, N Ishiguro, Y Sasaki, M Abe, H Uematsu, N Okawa, K Ozaki, K Kobayashi, Y Honjo, H Nishino, Y Joti, T Hatsui, Y Takahashi, HC Dam

PID3Net: a deep learning approach for single-shot coherent X-ray diffraction imaging of dynamic phenomena

History

Journal

npj Computational Materials

Volume

11

Article number

66

Pagination

1-14

Location

London, Eng.

Open access

Yes

ISSN

2057-3960

eISSN

2057-3960

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

1

Publisher

Nature Research

Publication URL

https://doi.org/10.1038/s41524-025-01549-x

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Keywords

Chemistry Chemistry, Physical DEFECT DYNAMICS HIGH-RESOLUTION Materials Science Materials Science, Multidisciplinary NEURAL-NETWORKS PHASE RETRIEVAL Physical Sciences Science & Technology Technology

Licence

CC BY-NC-ND 4.0

PID3Net: a deep learning approach for single-shot coherent X-ray diffraction imaging of dynamic phenomena

History

Journal

Volume

Article number

Pagination

Location

Open access

ISSN

eISSN

Language

Publication classification

Issue

Publisher

Publication URL

Usage metrics

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Keywords

Licence

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