AffiliationUniv Arizona, Coll Opt Sci
Univ Arizona, Dept Elect & Comp Engn
Univ Arizona, Dept Comp Sci
MetadataShow full item record
PublisherOPTICAL SOC AMER
CitationShao, J., Zhang, J., Liang, R., & Barnard, K. (2019). Fiber bundle imaging resolution enhancement using deep learning. Optics express, 27(11), 15880-15890.
Rights© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractWe propose a deep learning based method to estimate high-resolution images from multiple fiber bundle images. Our approach first aligns raw fiber bundle image sequences with a motion estimation neural network and then applies a 3D convolution neural network to learn a mapping from aligned fiber bundle image sequences to their ground truth images. Evaluations on lens tissue samples and a 1951 USAF resolution target suggest that our proposed method can significantly improve spatial resolution for fiber bundle imaging systems.
NoteOpen access journal
VersionFinal published version
SponsorsNational Institute of Biomedical Imaging and Bioengineering (NIBIB) [R21EB022378]
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