Phase recovery and holographic image reconstruction using deep learning in neural networks

Light: Science and Applications

Volumn 7, Issue 2, 2018, Pages 17141-

  Rivenson, Yair ^a,b   Zhang, Yibo ^a,b   Günaydın, Harun ^a   Teng, Da ^a,b   Ozcan, Aydogan ^a,b,c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b Engineering IV   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

deep learning; holography; machine learning; neural networks; phase recovery

Indexed keywords

COMPUTER SYSTEM RECOVERY; DEEP LEARNING; DEEP NEURAL NETWORKS; HOLOGRAMS; HOLOGRAPHY; LEARNING ALGORITHMS; LEARNING SYSTEMS; MACHINE LEARNING; NEURAL NETWORKS; RECOVERY;

COMPUTATIONAL IMAGING SYSTEM; HOLOGRAPHIC IMAGING; LEARNING-BASED APPROACH; PAP SMEAR; PHASE RECOVERY; SELF-INTERFERENCES; TISSUE SECTIONS; TWIN-IMAGE;

IMAGE RECONSTRUCTION;

EID: 85077152363     PISSN: 20955545     EISSN: 20477538     Source Type: Journal    
DOI: 10.1038/lsa.2017.141     Document Type: Article

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