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eFIN: Enhanced Fourier Imager Network for Generalizable Autofocusing and Pixel Super-Resolution in Holographic Imaging
被引:16
作者:
Chen, Hanlong
[1
]
Huang, Luzhe
[1
]
Liu, Tairan
[1
]
Ozcan, Aydogan
[1
]
机构:
[1] Univ Calif Los Angeles, Calif Nano Syst Inst CNSI, Elect & Comp Engn Dept, Bioengn Dept, Los Angeles, CA 90095 USA
关键词:
Digital holography;
deep learning;
phase retrieval;
autofocusing;
pixel super-resolution;
physics-informed learning;
computational imaging;
hologram reconstruction;
QUANTITATIVE PHASE;
RETRIEVAL;
FIELD;
MICROSCOPY;
RECONSTRUCTION;
ALGORITHMS;
D O I:
10.1109/JSTQE.2023.3248684
中图分类号:
TM [电工技术];
TN [电子技术、通信技术];
学科分类号:
0808 ;
0809 ;
摘要:
The application of deep learning techniques has greatly enhanced holographic imaging capabilities, leading to improved phase recovery and image reconstruction. Here, we introduce a deep neural network termed enhanced Fourier Imager Network (eFIN) as a highly generalizable and robust framework for hologram reconstruction with pixel super-resolution and image autofocusing. Through holographic microscopy experiments involving lung, prostate and salivary gland tissue sections and Papanicolau (Pap) smears, we demonstrate that eFIN has a superior image reconstruction quality and exhibits external generalization to new types of samples never seen during the training phase. This network achieves a wide autofocusing axial range of Delta z similar to 350 mu m, with the capability to accurately predict the hologram axial distances by physics-informed learning. eFIN enables 3x pixel super-resolution imaging and increases the space-bandwidth product of the reconstructed images by 9-fold with almost no performance loss, which allows for significant time savings in holographic imaging and data processing steps. Our results showcase the advancements of eFIN in pushing the boundaries of holographic imaging for various applications in e.g., quantitative phase imaging and label-free microscopy.
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页数:10
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