Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results

Summary: A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missed in a deeply reconstructed image, and (3) decreased imaging perf...

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Bibliographic Details
Main Authors: Weiwen Wu, Dianlin Hu, Wenxiang Cong, Hongming Shan, Shaoyu Wang, Chuang Niu, Pingkun Yan, Hengyong Yu, Varut Vardhanabhuti, Ge Wang
Format: Article
Language:English
Published: Elsevier 2022-05-01
Series:Patterns
Subjects:
DSML 2: Proof-of-concept: Data science output has been formulated, implemented, and tested for one domain/problem
Online Access:http://www.sciencedirect.com/science/article/pii/S266638992200054X

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http://www.sciencedirect.com/science/article/pii/S266638992200054X

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